- Radiation Oncology
- Head and neck cancer
- Lung cancer
- Skin cancer
Clinical Associate Professor, Radiation Oncology - Radiation Therapy
Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Member, Stanford Cancer Institute
Chair, Data and Safety Monitoring Committee, Stanford Cancer Institute (2020 - Present)
Member, Head and Neck Cancer General Committee, NRG Oncology (2022 - Present)
Boards, Advisory Committees, Professional Organizations
Member, Annual Meeting Education Committee, American Society for Radiation Oncology (2019 - Present)
Medical Education: Vanderbilt University School of Medicine (2010) TN
Residency: University of Washington Dept of Radiation Oncology (2015) WA
Board Certification: American Board of Radiology, Radiation Oncology (2016)
Internship: Johns Hopkins Hospital Internal Medicine Residency (2011) MD
AB with honors, Harvard College, Physics (2005)
Current Research and Scholarly Interests
In addition to my clinical research in head and neck and lung cancer, I work on the application of computer science and machine learning to cancer research. I develop tools for analyzing large datasets to improve outcomes and safety of cancer treatment. I developed a machine learning prognostic model using data from around 13,000 patients with metastatic cancer which performs better than traditional models and physicians [PubMed ID 33313792]. We recently completed a prospective randomized study in thousands of patients in which the model was used to help improve advance care planning conversations.
I also work on the methods underpinning observational and predictive modeling research. My open source nnet-survival software that allows use of neural networks for survival modeling has been used by researchers internationally. In collaboration with the Stanford Research Informatics Center, I examined how electronic medical record (EMR) survival outcome data compares to gold-standard data from a cancer registry [PubMed ID 35802836]. The EMR data captured less than 50% of deaths, a finding that affects many studies being published that use EMR outcomes data.
Testing Docetaxel-Cetuximab or the Addition of an Immunotherapy Drug, Atezolizumab, to the Usual Chemotherapy and Radiation Therapy in High-Risk Head and Neck Cancer
This phase II/III trial studies how well radiation therapy works when given together with cisplatin, docetaxel, cetuximab, and/or atezolizumab after surgery in treating patients with high-risk stage III-IV head and neck cancer the begins in the thin, flat cells (squamous cell). Specialized radiation therapy that delivers a high dose of radiation directly to the tumor may kill more tumor cells and cause less damage to normal tissue. Drugs used in chemotherapy, such as cisplatin and docetaxel, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. Cetuximab is a monoclonal antibody that may interfere with the ability of tumor cells to grow and spread. Immunotherapy with monoclonal antibodies, such as atezolizumab, may help the body's immune system attack the cancer, and may interfere with the ability of tumor cells to grow and spread. The purpose of this study is to compare the usual treatment (radiation therapy with cisplatin chemotherapy) to using radiation therapy with docetaxel and cetuximab chemotherapy, and using the usual treatment plus an immunotherapy drug, atezolizumab.
Radical-Dose Image Guided Radiation Therapy in Treating Patients With Metastatic Non-small Cell Lung Cancer Undergoing Immunotherapy
This phase II trial studies how well radical-dose image guided radiation therapy works in treating patients with non-small cell lung cancer that has spread to other places in the body who are undergoing immunotherapy. Radiation therapy uses high energy x-rays to kill tumor cells and shrink tumors. Giving radical-dose image guided radiation therapy to patients with non-small cell lung cancer may help to improve response to immunotherapy anti-cancer treatment.
Stanford is currently not accepting patients for this trial. For more information, please contact Kim Nguyen, 650-497-8966.
Trial of XRD-0394, a Kinase Inhibitor, in Combination With Palliative Radiotherapy in Advanced Cancer Patients
XRD-0394 is a novel, potent, oral, small molecule dual inhibitor of ataxia telangiectasia mutated kinase (ATM) and deoxyribonucleic acid (DNA)-dependent protein kinase (DNA-PK) that has selectivity compared with other phosphatidylinositol 3-kinase-related kinase (PIKK) family enzymes. This is a first-time-in-human study, which means that it is the first time the study drug is being used in humans. The purpose of the study is to evaluate the safety and tolerability of single doses of XRD-0394 administered with palliative radiotherapy (RT) to subjects with metastatic, locally advanced, or recurrent cancer. The pharmacokinetic (PK) profile and pharmacodynamic (PD) effects of single-dose XRD-0394 administered in combination with palliative RT will also be characterized.
Stanford is currently not accepting patients for this trial. For more information, please contact Cancer Clinical Trials Office (CCTO), 650-498-7061.
Individualized Stereotactic Ablative Radiotherapy for Lung Tumors: The iSABR Phase 2 Nonrandomized Controlled Trial.
Stereotactic ablative radiotherapy (SABR) is used for treating lung tumors but can cause toxic effects, including life-threatening damage to central structures. Retrospective data suggested that small tumors up to 10 cm3 in volume can be well controlled with a biologically effective dose less than 100 Gy.To assess whether individualizing lung SABR dose and fractionation by tumor size, location, and histological characteristics may be associated with local tumor control.This nonrandomized controlled trial (the iSABR trial, so named for individualized SABR) was a phase 2 multicenter trial enrolling participants from November 15, 2011, to December 5, 2018, at academic medical centers in the US and Japan. Data were analyzed from December 9, 2020, to May 10, 2023. Patients were enrolled in 3 groups according to cancer type: initial diagnosis of non-small cell lung cancer (NSCLC) with an American Joint Committee on Cancer 7th edition T1-3N0M0 tumor (group 1), a T1-3N0M0 new primary NSCLC with a history of prior NSCLC or multiple NSCLCs (group 2), or lung metastases from NSCLC or another solid tumor (group 3).Up to 4 tumors were treated with once-daily SABR. The dose ranged from 25 Gy in 1 fraction for peripheral tumors with a volume of 0 to 10 cm3 to 60 Gy in 8 fractions for central tumors with a volume greater than 30 cm3.Per-group freedom from local recurrence (same-lobe recurrence) at 1 year, with censoring at time of distant recurrence, death, or loss to follow-up.In total, 217 unique patients (median [IQR] age, 72 [64-80] years; 129 [59%] male; 150 [69%] current or former smokers) were enrolled (some multiple times). There were 240 treatment courses: 79 in group 1, 82 in group 2, and 79 in group 3. A total of 285 tumors (211 [74%] peripheral and 74 [26%] central) were treated. The most common dose was 25 Gy in 1 fraction (158 tumors). The median (range) follow-up period was 33 (2-109) months, and the median overall survival was 59 (95% CI, 49-82) months. Freedom from local recurrence at 1 year was 97% (90% CI, 91%-99%) for group 1, 94% (90% CI, 87%-97%) for group 2, and 96% (90% CI, 89%-98%) for group 3. Freedom from local recurrence at 5 years ranged from 83% to 93% in the 3 groups. The proportion of patients with grade 3 to 5 toxic effects was low, at 5% (including a single patient [1%] with grade 5 toxic effects).The results of this nonrandomized controlled trial suggest that individualized SABR (iSABR) used to treat lung tumors may allow minimization of treatment dose and is associated with excellent local control. Individualized dosing should be considered for use in future trials.ClinicalTrials.gov Identifier: NCT01463423.
View details for DOI 10.1001/jamaoncol.2023.3495
View details for PubMedID 37707820
- Potential Biases in a Population-based Study of Surveillance Imaging for Head and Neck Cancer. Radiology 2023; 308 (2): e230286
Lessons and Opportunities for Biomarker-Driven Radiation Personalization in Head and Neck Cancer.
Seminars in radiation oncology
2023; 33 (3): 336-347
Head and neck cancer is notoriously challenging to treat in part because it constitutes an anatomically and biologically diverse group of cancers with heterogeneous prognoses. While treatment can be associated with significant late toxicities, recurrence is often difficult to salvage with poor survival rates and functional morbidity.1,2 Thus, achieving tumor control and cure at the initial diagnosis is the highest priority. Given the differing outcome expectations (even within a specific sub-site like oropharyngeal carcinoma), there has been growing interest in personalizing treatment: de-escalation in selected cancers to decrease the risk of late toxicity without compromising oncologic outcomes, and intensification for more aggressive cancers to improve oncologic outcomes without causing undue toxicity. This risk stratification is increasingly accomplished using biomarkers, which can represent molecular, clinicopathologic, and/or radiologic data. In this review, we will focus on biomarker-driven radiotherapy dose personalization with emphasis on oropharyngeal and nasopharyngeal carcinoma. This radiation personalization is largely performed on the population level by identifying patients with good prognosis via traditional clinicopathologic factors, although there are emerging studies supporting inter-tumor and intra-tumor level personalization via imaging and molecular biomarkers.
View details for DOI 10.1016/j.semradonc.2023.03.013
View details for PubMedID 37331788
Study of Patient and Physician Attitudes Toward Automated Prognostic Models for Patients With Metastatic Cancer.
JCO clinical cancer informatics
2023; 7: e2300023
For patients with cancer and their doctors, prognosis is important for choosing treatments and supportive care. Oncologists' life expectancy estimates are often inaccurate, and many patients are not aware of their general prognosis. Machine learning (ML) survival models could be useful in the clinic, but there are potential concerns involving accuracy, provider training, and patient involvement. We conducted a qualitative study to learn about patient and oncologist views on potentially using a ML model for patient care.Patients with metastatic cancer (n = 15) and their family members (n = 5), radiation oncologists (n = 5), and medical oncologists (n = 5) were recruited from a single academic health system. Participants were shown an anonymized report from a validated ML survival model for another patient, which included a predicted survival curve and a list of variables influencing predicted survival. Semistructured interviews were conducted using a script.Every physician and patient who completed their interview said that they would want the option for the model to be used in their practice or care. Physicians stated that they would use an AI prognosis model for patient triage and increasing patient understanding, but had concerns about accuracy and explainability. Patients generally said that they would trust model results completely if presented by their physician but wanted to know if the model was being used in their care. Some reacted negatively to being shown a median survival prediction.Patients and physicians were supportive of use of the model in the clinic, but had various concerns, which should be addressed as predictive models are increasingly deployed in practice.
View details for DOI 10.1200/CCI.23.00023
View details for PubMedID 37478393
Adaptive Region-Specific Loss for Improved Medical Image Segmentation.
IEEE transactions on pattern analysis and machine intelligence
Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and vice versa. Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.
View details for DOI 10.1109/TPAMI.2023.3289667
View details for PubMedID 37363838
Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.
The Lancet. Digital health
Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context.In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics.Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features.This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer.National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.
View details for DOI 10.1016/S2589-7500(23)00082-1
View details for PubMedID 37268451
Unilateral diaphragmatic paralysis after stereotactic ablative radiotherapy to a lung tumor abutting the course of the phrenic nerve.
Practical radiation oncology
We present the case of a woman with metastatic adenoid cystic carcinoma who received stereotactic ablative radiotherapy (SABR) with a total dose of 50 Gy in 4 fractions to two lung metastases and developed symptomatic left phrenic nerve injury 2 years after radiation. The maximum dose to the approximate location of the phrenic nerve was 57.7 Gy which corresponds to a biologically effective dose for late effects (using α/β ratio = 3) of 335.14 Gy. Here, we discuss the case, planning considerations by radiation oncologists and medical physicists, and the multidisciplinary medical management of this patient.
View details for DOI 10.1016/j.prro.2023.04.010
View details for PubMedID 37150318
Patient-specific Auto-segmentation on Daily kVCT Images for Adaptive Radiotherapy.
International journal of radiation oncology, biology, physics
This study explored deep learning-based patient-specific auto-segmentation using transfer learning on daily kVCT images to facilitate adaptive radiotherapy, based on data from the first group of patients treated with the innovative RefleXion system.For head and neck (HaN) site and pelvic site, a deep convolutional segmentation network was initially trained on a population dataset, which contained 67 and 56 patient cases respectively. Then the pre-trained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning CT and 5-26 sets of daily RefleXion kVCT were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric impacts resulting from different auto-segmentation and registration methods were also investigated.The proposed patient-specific network achieved mean DSC results of 0.88 for three HaN organs at risk (OARs) of interest and 0.90 for eight pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour.Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiotherapy.
View details for DOI 10.1016/j.ijrobp.2023.04.026
View details for PubMedID 37141982
Pulmonary Hemorrhage in Patients Treated with Thoracic Stereotactic Ablative Radiotherapy and Anti-Angiogenic Agents.
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
Severe pulmonary hemorrhage can occur in patients treated with thoracic stereotactic ablative radiotherapy (SABR) and vascular endothelial growth factor inhibitors (VEGFi). There is limited understanding of which patients are at risk for toxicity with the combination of thoracic SABR and VEGFis or how the risk differs over either therapy alone.We evaluated a prospectively maintained cohort of 690 patients with 818 pulmonary tumors treated with highly conformal SABR. Rates of any grade and grade-three-plus (G3+) pulmonary hemorrhage were compared between patients treated with or without VEGFi therapy across tumor locations. Outcomes were compared between patients treated with SABR + VEGFi and a propensity-matched cohort of those treated with VEGFi therapy alone.Treatment with VEGFi + SABR was associated with higher rates of G3+ pulmonary hemorrhage compared to those treated with SABR alone for the overall cohort (3-year incidence: 7.9% vs 0.6%, p<0.01) and those with central tumors (19.1% vs 3.3%, p=0.04). When further subdivided, there were significantly higher toxicity rates with VEGFi for the ultracentral (9.0% vs 45.0%, p = 0.044), but not central non-abutting tumors (0.0% vs 1.3% p = 0.69). There was an increased incidence of G3+ hemorrhage in patients treated with VEGFi + SABR compared to VEGFi alone (9.6 vs 1.3%, p=0.04).The combination of VEGFi and SABR was associated with an increased risk of high-grade pulmonary hemorrhage over either therapy alone. Low rates of toxicity were observed when excluding patients with SABR to ultracentral tumors and applying highly conformal SABR techniques.
View details for DOI 10.1016/j.jtho.2023.04.007
View details for PubMedID 37085030
Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery.
Radiation oncology (London, England)
2023; 18 (1): 61
Artificial intelligence-based tools can be leveraged to improve detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain by Vysioneer Inc. is a deep learning algorithm with recent FDA clearance to assist in brain tumor contouring. We aimed to assess the performance of this tool by various demographic and clinical characteristics among patients with brain metastases treated with SRS.We randomly selected 100 patients with brain metastases who underwent initial SRS on the CyberKnife from 2017 to 2020 at a single institution. Cases with resection cavities were excluded from the analysis. Computed tomography (CT) and axial T1-weighted post-contrast magnetic resonance (MR) image data were extracted for each patient and uploaded to VBrain. A brain metastasis was considered "detected" when the VBrain- "predicted" contours overlapped with the corresponding physician contours ("ground-truth" contours). We evaluated performance of VBrain against ground-truth contours using the following metrics: lesion-wise Dice similarity coefficient (DSC), lesion-wise average Hausdorff distance (AVD), false positive count (FP), and lesion-wise sensitivity (%). Kruskal-Wallis tests were performed to assess the relationships between patient characteristics including sex, race, primary histology, age, and size and number of brain metastases, and performance metrics such as DSC, AVD, FP, and sensitivity.We analyzed 100 patients with 435 intact brain metastases treated with SRS. Our cohort consisted of patients with a median number of 2 brain metastases (range: 1 to 52), median age of 69 (range: 19 to 91), and 50% male and 50% female patients. The primary site breakdown was 56% lung, 10% melanoma, 9% breast, 8% gynecological, 5% renal, 4% gastrointestinal, 2% sarcoma, and 6% other, while the race breakdown was 60% White, 18% Asian, 3% Black/African American, 2% Native Hawaiian or other Pacific Islander, and 17% other/unknown/not reported. The median tumor size was 0.112 c.c. (range: 0.010-26.475 c.c.). We found mean lesion-wise DSC to be 0.723, mean lesion-wise AVD to be 7.34% of lesion size (0.704 mm), mean FP count to be 0.72 tumors per case, and lesion-wise sensitivity to be 89.30% for all lesions. Moreover, mean sensitivity was found to be 99.07%, 97.59%, and 96.23% for lesions with diameter equal to and greater than 10 mm, 7.5 mm, and 5 mm, respectively. No other significant differences in performance metrics were observed across demographic or clinical characteristic groups.In this study, a commercial deep learning algorithm showed promising results in segmenting brain metastases, with 96.23% sensitivity for metastases with diameters of 5 mm or higher. As the software is an assistive AI, future work of VBrain integration into the clinical workflow can provide further clinical and research insights.
View details for DOI 10.1186/s13014-023-02246-z
View details for PubMedID 37016416
View details for PubMedCentralID 7174761
Mitigation of IMRT/SBRT treatment planning errors on the RefleXion X1 system using FMEA within Six Sigma framework
Advances in Radiation Oncology
View details for DOI 10.1016/j.adro.2023.101186
Improving lung cancer screening rates among patients with head and neck cancer in a radiation oncology clinic.
Journal of thoracic disease
2022; 14 (12): 4633-4640
The United States Preventive Services Task Force (USPSTF) recommends lung cancer screening via annual low dose computed tomography (LDCT) for high risk patients. Despite the strong evidence of a mortality benefit from several randomized clinical trials, rates of lung cancer screening remain low. We plan to assess how screening guidelines are implemented in a radiation oncology clinic for patients with head and neck cancer.A single institution, retrospective chart review was used to identify patients with head and neck cancer seen in a radiation oncology clinic who were potentially eligible for lung cancer screening under the current USPSTF guidelines. Patients who were potentially screening-eligible were enrolled in a phone survey to assess their knowledge about lung cancer screening and willingness to be screened.Of the 184 patients with head and neck cancer seen in the clinic, 8 (4%) patients were eligible for lung cancer screening under the previous USPSTF recommendations, including 1 (0.5%) patient already being screened. One patient (0.5%) became eligible under the expanded guidelines. All 184 patients had smoking history documented. Of the 87 current or former smokers, there were 24 (28%) who did not have pack-years documented; of the 82 former smokers, there were 8 (10%) who did not have quit date documented. Among the 16 phone survey participants (response rate: 70%) only 6 (38%) were aware there is a way to screen for lung cancer and 12 (75%) patients would be interested in screening if they are found to be eligible.These findings highlight a potential opportunity to increase rates of lung cancer screening among patients with head and neck cancer by both enhancing provider awareness as well as patient education at the community level.
View details for DOI 10.21037/jtd-22-787
View details for PubMedID 36647458
View details for PubMedCentralID PMC9840013
Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer.
JCO oncology practice
Patients with metastatic cancer benefit from advance care planning (ACP) conversations. We aimed to improve ACP using a computer model to select high-risk patients, with shorter predicted survival, for conversations with providers and lay care coaches. Outcomes included ACP documentation frequency and end-of-life quality measures.In this study of a quality improvement initiative, providers in four medical oncology clinics received Serious Illness Care Program training. Two clinics (thoracic/genitourinary) participated in an intervention, and two (cutaneous/sarcoma) served as controls. ACP conversations were documented in a centralized form in the electronic medical record. In the intervention, providers and care coaches received weekly e-mails highlighting upcoming clinic patients with < 2 year computer-predicted survival and no prior prognosis documentation. Care coaches contacted these patients for an ACP conversation (excluding prognosis). Providers were asked to discuss and document prognosis.In the four clinics, 4,968 clinic visits by 1,251 patients met inclusion criteria (metastatic cancer with no prognosis previously documented). In their first visit, 28% of patients were high-risk (< 2 year predicted survival). Preintervention, 3% of both intervention and control clinic patients had ACP documentation during a visit. By intervention end (February 2021), 35% of intervention clinic patients had ACP documentation compared with 3% of control clinic patients. Providers' prognosis documentation rate also increased in intervention clinics after the intervention (2%-27% in intervention clinics, P < .0001; 0%-1% in control clinics). End-of-life care intensity was similar in intervention versus control clinics, but patients with ≥ 1 provider ACP edit met fewer high-intensity care measures (P = .04).Combining a computer prognosis model with care coaches increased ACP documentation.
View details for DOI 10.1200/OP.22.00128
View details for PubMedID 36395436
- Improving lung cancer screening rates among patients with head and neck cancer in a radiation oncology clinic JOURNAL OF THORACIC DISEASE 2022
Safety of nivolumab added to chemoradiotherapy platforms for intermediate and high-risk local-regionally advanced head and neck squamous cell carcinoma: RTOG Foundation 3504.
International journal of radiation oncology, biology, physics
PURPOSE: Programmed death-1 immune checkpoint blockade (PD-1 ICB) improves survival of patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), but the benefits of addition to (chemo)radiation for newly diagnosed HNSCC patients remain unknown.METHODS AND MATERIALS: We evaluated the safety of nivolumab concomitant with 70Gy intensity-modulated radiotherapy and weekly cisplatin (arm 1), every three-week cisplatin (arm 2), cetuximab (arm 3), or alone for platinum-ineligible patients (arm 4), in newly diagnosed intermediate or high-risk local-regionally advanced HNSCC. Patients received nivolumab from two weeks prior to three months post radiotherapy. The primary endpoint was dose-limiting toxicity (DLT). If ≤ 2 of the first 8 evaluable patients experience a DLT, an arm was considered safe. Secondary endpoints included toxicity and feasibility of adjuvant nivolumab to one year, defined as all 7 additional doses received by ≥4 of the first 8 evaluable patients across arms.RESULTS: Of 39 patients (10 in arms 1, 3, 4, and 9 in arm 2), 72% had T3-4 tumors; 85% had N2-3 nodal disease, and 67% had >10 pack-years of smoking. There were no DLTs in arms 1 and 2, 1 in arm 3 (mucositis), and 2 in arm 4 (lipase elevation and mucositis in one and fatigue in another). The most common grade ≥3 nivolumab-related adverse events were lipase increase, mucositis, diarrhea, lymphopenia, hyponatremia, leukopenia, fatigue, and serum amylase increase. Adjuvant nivolumab was feasible as defined in the protocol.CONCLUSIONS: Concomitant nivolumab with the four tested regimens was safe for patients with intermediate and high-risk HNSCC and subsequent adjuvant nivolumab was feasible as defined (NCT# xxxx).
View details for DOI 10.1016/j.ijrobp.2022.10.008
View details for PubMedID 36228746
Dosimetric Predictors of Local Control after Stereotactic Ablative Radiotherapy (SABR) for Lung Tumors: A Secondary Analysis of a Phase II Prospective Trial of Individualized SABR (iSABR)
LIPPINCOTT WILLIAMS & WILKINS. 2022: S16
View details for Web of Science ID 000847787800034
Posttreatment FDG-PET/CT Hopkins criteria predict locoregional recurrence after definitive radiotherapy for oropharyngeal squamous cell carcinoma.
Head & neck
BACKGROUND: Metabolic response assessment for oropharyngeal squamous cell carcinoma (OPSCC) aids in identifying locoregional persistence/recurrence (LRR). The Hopkins Criteria are a standardized qualitative response assessment system using posttreatment FDG-PET/CT.METHODS: We conducted a retrospective cohort study of patients with node-positive OPSCC treated with definitive (chemo)radiotherapy. We assessed Hopkins Criteria performance for LRR, then developed and validated a competing-risks model.RESULTS: Between 2004 and 2018, 259 patients were included with median follow-up of 43months. The Hopkins Criteria sensitivity, specificity, negative predictive value, and accuracy were 68%, 88%, 95%, and 85%. The 36-month cumulative incidence of LRR was greater with positive scores (45% vs. 5%, HR 12.60, p<0.001). PET/CTs performed ≤10weeks after radiotherapy were associated with a four-fold increase in pathologically negative biopsies/surgeries (36% vs. 9%, p=0.03). The AUC for LRR was 0.89 using a model integrating the Hopkins score.CONCLUSIONS: The Hopkins Criteria predict LRR with high accuracy for OPSCC response assessment.
View details for DOI 10.1002/hed.27160
View details for PubMedID 35920790
Use of systemic cancer treatments based on a validated survival prediction model in metastatic cancer.
LIPPINCOTT WILLIAMS & WILKINS. 2022
View details for Web of Science ID 000863680302618
Accuracy of Electronic Medical Record Follow-Up Data for Estimating the Survival Time of Patients With Cancer.
JCO clinical cancer informatics
2022; 6: e2200019
For real-world evidence, it is convenient to use routinely collected data from the electronic medical record (EMR) to measure survival outcomes. However, patients can become lost to follow-up, causing incomplete data and biased survival time estimates. We quantified this issue for patients with metastatic cancer seen in an academic health system by comparing survival estimates from EMR data only and from EMR data combined with high-quality cancer registry data.Patients diagnosed with metastatic cancer from 2008 to 2014 were included in this retrospective study. Patients who were diagnosed with cancer or received their initial treatment within our system were included in the institutional cancer registry and this study. Overall survival was calculated using the Kaplan-Meier method. Survival curves were generated in two ways: using EMR follow-up data alone and using EMR data supplemented with data from the Stanford Cancer Registry/California Cancer Registry.Four thousand seventy-seven patients were included. The median follow-up using EMR + Cancer Registry data was 19.9 months, and the median follow-up in surviving patients was 67.6 months. There were 1,301 deaths recorded in the EMR and 3,140 deaths recorded in the Cancer Registry. The median overall survival from the date of cancer diagnosis using EMR data was 58.7 months (95% CI, 54.2 to 63.2); using EMR + Cancer Registry data, it was 20.8 months (95% CI, 19.6 to 22.3). A similar pattern was seen using the date of first systemic therapy or date of first hospital admission as the baseline date.Using EMR data alone, survival time was overestimated compared with EMR + Cancer Registry data.
View details for DOI 10.1200/CCI.22.00019
View details for PubMedID 35802836
- De-escalating elective nodal irradiation for nasopharyngeal carcinoma. The Lancet. Oncology 2022
Uncovering interpretable potential confounders in electronic medical records.
2022; 13 (1): 1014
Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We develop a framework based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to four cohorts built from localized prostate and lung cancer datasets from the Stanford Cancer Institute and show that our method shifts the HR estimate towards the RCT results. The uncovered terms can also be interpreted by oncologists for clinical insights. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.
View details for DOI 10.1038/s41467-022-28546-8
View details for PubMedID 35197467
Considerations in the reliability and fairness audits of predictive models for advance care planning
Frontiers in Digital Health
View details for DOI 10.3389/fdgth.2022.943768
IMRT and SBRT Treatment Planning Study for the First Clinical Biology-Guided Radiotherapy System.
Technology in cancer research & treatment
2022; 21: 15330338221100231
Purpose: The first clinical biology-guided radiation therapy (BgRT) system-RefleXionTM X1-was installed and commissioned for clinical use at our institution. This study aimed at evaluating the treatment plan quality and delivery efficiency for IMRT/SBRT cases without PET guidance. Methods: A total of 42 patient plans across 6 cancer sites (conventionally fractionated lung, head, and neck, anus, prostate, brain, and lung SBRT) planned with the EclipseTM treatment planning system (TPS) and treated with either a TrueBeam or Trilogy were selected for this retrospective study. For each Eclipse VMAT plan, 2 corresponding plans were generated on the X1 TPS with 10mm jaws (X1-10mm) and 20mm jaws (X1-20mm) using our institutional planning constraints. All clinically relevant metrics in this study, including PTV D95%, PTV D2%, Conformity Index (CI), R50, organs-at-risk (OAR) constraints, and beam-on time were analyzed and compared between 126 VMAT and RefleXion plans using paired t-tests. Results: All but 3 planning metrics were either equivalent or superior for the X1-10mm plans as compared to the Eclipse VMAT plans across all planning sites investigated. The Eclipse VMAT and X1-10mm plans generally achieved superior plan quality and sharper dose fall-off superior/inferior to targets as compared to the X1-20mm plans, however, the X1-20mm plans were still considered acceptable for treatment. On average, the required beam-on time increased by a factor of 1.6 across all sites for X1-10mm compared to X1-20mm plans. Conclusions: Clinically acceptable IMRT/SBRT treatment plans were generated with the X1 TPS for both the 10mm and 20mm jaw settings.
View details for DOI 10.1177/15330338221100231
View details for PubMedID 35579876
Acute and Late Esophageal Toxicity Following Stereotactic Ablative Radiotherapy to Thoracic Tumors near or Abutting the Esophagus.
International journal of radiation oncology, biology, physics
PURPOSE: To evaluate the incidence of acute and late esophageal toxicity in patients with thoracic tumors near or abutting the esophagus treated with stereotactic ablative radiotherapy (SABR).METHODS AND MATERIALS: Among patients with thoracic tumors treated with SABR, we identified those with tumors near or abutting the esophagus. Using the linear-quadratic model with an alpha/SS ratio of 10, we determined the correlation between dosimetric parameters and esophageal toxicity graded using the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0.RESULTS: Out of 2200 patients treated with thoracic SABR, 767 patients were analyzable for esophageal dosimetry. We identified 55 patients with tumors near the esophagus (52 evaluable for esophagitis grade), 28 with PTV overlapping the esophagus. Median follow-up and overall survival were 16 and 23 months respectively. Thirteen patients (25%) developed temporary grade 2 acute esophageal toxicity, 11 (85%) of whom had PTV overlapping the esophagus. Symptoms resolved within 1-3 months in 12 patients, and 6 months in all patients. No grade 3-5 toxicity was observed. Only 3 patients (6%) developed late or persistent grade 2 dysphagia or dyspepsia of uncertain relationship to SABR. Cumulative incidence of acute esophagitis was 15% and 25% at 14 days and 60 days respectively. Acute toxicity correlated on univariate analysis with esophageal Dmax, D1cc, D2cc, Dmax/Dprescription and whether the PTV was overlapping the esophagus. Esophageal Dmax (BED10) < 62 Gy, D1cc (BED10) < 48 Gy, D2cc (BED10) < 43 Gy, and Dmax/Dprescription < 85% was associated with <20% risk of grade 2 acute esophagitis. Only 2 local recurrences occurred.CONCLUSIONS: Although 25% of patients with tumors near the esophagus developed acute esophagitis (39% of those with PTV overlapping the esophagus), these toxicities were all grade 2 and all temporary. This suggests the safety and efficacy of thoracic SABR for tumors near or abutting the esophagus when treating with high conformity and sharp dose gradients.
View details for DOI 10.1016/j.ijrobp.2021.12.008
View details for PubMedID 34942312
Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy.
Computers in biology and medicine
1800; 141: 105139
PURPOSE: To develop a deep unsupervised learning method with control volume (CV) mapping from patient positioning daily CT (dCT) to planning computed tomography (pCT) for precise patient positioning.METHODS: We propose an unsupervised learning framework, which maps CVs from dCT to pCT to automatically generate the couch shifts, including translation and rotation dimensions. The network inputs are dCT, pCT and CV positions in the pCT. The output is the transformation parameter of the dCT used to setup the head and neck cancer (HNC) patients. The network is trained to maximize image similarity between the CV in the pCT and the CV in the dCT. A total of 554 CT scans from 158 HNC patients were used for the evaluation of the proposed model. At different points in time, each patient had many CT scans. Couch shifts are calculated for the testing by averaging the translation and rotation from the CVs. The ground-truth of the shifts come from bone landmarks determined by an experienced radiation oncologist.RESULTS: The system positioning errors of translation and rotation are less than 0.47mm and 0.17°, respectively. The random positioning errors of translation and rotation are less than 1.13mm and 0.29°, respectively. The proposed method enhanced the proportion of cases registered within a preset tolerance (2.0mm/1.0°) from 66.67% to 90.91% as compared to standard registrations.CONCLUSIONS: We proposed a deep unsupervised learning architecture for patient positioning with inclusion of CVs mapping, which weights the CVs regions differently to mitigate any potential adverse influence of image artifacts on the registration. Our experimental results show that the proposed method achieved efficient and effective HNC patient positioning.
View details for DOI 10.1016/j.compbiomed.2021.105139
View details for PubMedID 34942395
Local Recurrence Outcomes of Colorectal Cancer Oligometastases Treated With Stereotactic Ablative Radiotherapy.
American journal of clinical oncology
PURPOSE: The aim of this study was to report local failure (LF) outcomes and associated predictors in patients with oligometastatic colorectal cancer (CRC) treated with stereotactic ablative radiotherapy (SABR).MATERIALS AND METHODS: We retrospectively reviewed patients with CRC metastases to the brain, liver, spine, or lung treated with SABR between 2001 and 2016. Time to LF was summarized using cumulative incidence of LF curves with death as a competing risk.RESULTS: The analysis included a total of 130 patients and 256 lesions. Of the metastases treated, 129 (50%) were brain, 50 (20%) liver, 49 (19%) spine, and 28 (11%) lung. Median gross tumor volume was 24 mL for liver metastases, 2 mL for brain metastases, 4 mL for spine metastases, and 1 mL for lung metastases. The overall 1, 2, and 3-year cumulative incidence of LF rates were 21.6% (16.5, 27.1), 28.2% (22.3, 34.4), and 31.5% (25.2, 38.0), respectively. LF was highest among the liver metastases (1 y: 26.0%, 2 y: 38.5%), followed by spine (1 y: 25.1%, 2 y: 31.1%), brain (1 y: 20%, 2 y: 25.2%), and lung (1 y: 13.7%, 2 y: insufficient data). Metastases from right-sided primary CRC were significantly more likely to have LF (P=0.0146, HR=2.23). Biologically effective dose>70 Gy, defined using a standard linear quadratic model using alpha/beta ratio of 10 on the individual lesion level, and pre-SABR chemotherapy were also significant predictors of LF (P= 0.0009 and 0.018, respectively).CONCLUSIONS: CRC metastases treated with SABR had significantly higher rates of LF if they originated from right-sided primary CRC, compared with left-sided. Liver metastases had the highest rates of LF compared with other metastatic sites. Thus, CRC liver metastases and metastases from right-sided CRC may benefit from more aggressive radiotherapy.
View details for DOI 10.1097/COC.0000000000000864
View details for PubMedID 34534143
Radiological tumor classification across imaging modality and histology.
Nature machine intelligence
2021; 3: 787-798
Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.
View details for DOI 10.1038/s42256-021-00377-0
View details for PubMedID 34841195
View details for PubMedCentralID PMC8612063
- Radiological tumour classification across imaging modality and histology NATURE MACHINE INTELLIGENCE 2021
Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.
JCO clinical cancer informatics
2021; 5: 379–93
PURPOSE: Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer.METHODS: We used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured (bag-of-words, doc2vec, fasttext), and combinations of both (structured + bow, structured + doc2vec, structured + fasttext). We optimized the identification model among five machine learning methods (logistic regression, multilayer perceptrons, random forest, support vector machines, and stochastic gradient boosting). The treatment information recorded in the cancer registry is the gold standard and compares our methods to an identification baseline with billing codes.RESULTS: For prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using structured + doc2vec. For oropharynx cancer, we achieved an f1-score of 0.78 (95% CI, 0.58 to 0.93) for chemoradiation and 0.83 (95% CI, 0.69 to 0.95) for surgery using doc2vec. For esophagus cancer, we achieved an f1-score of 1.0 (95% CI, 1.0 to 1.0) for both chemoradiation and surgery using all combinations of structured and unstructured data. We found that employing the free-text clinical notes outperforms using the billing codes or only structured data for all three cancer types.CONCLUSION: Our results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.
View details for DOI 10.1200/CCI.20.00173
View details for PubMedID 33822653
Postoperative Observation Versus Radiotherapy for Pathologic N1 Oral Cavity Squamous Cell Carcinoma.
American journal of clinical oncology
2021; Publish Ahead of Print
To investigate the benefit of postoperative radiotherapy (PORT) for low-volume (pN1) nodal disease after resection of oral cavity squamous cell carcinoma.The National Cancer Database was queried for adults with nonmetastatic squamous cell carcinoma of the oral cavity treated by surgical resection with pathologic stage T1-2 N0-2 (American Joint Committee on Cancer 7th edition) and with the maximal exclusion of standard indications for PORT. Overall survival was compared within pN1 for observation versus PORT and then compared for pN1 versus pN0 and versus pN2 stratified by receipt of observation or PORT. Multivariable Cox regression was used to adjust for potential confounders between PORT and survival, including comorbidity and age.Overall 5017 pN0, 530 pN1, and 253 pN2 patients were identified, of whom 9%, 35%, and 64% received PORT, respectively. Within the pN1 cohort, PORT was associated with improved survival versus observation (adjusted hazard ratio, 0.66; 95% confidence interval, 0.46-0.97; P=0.03). Among observed patients, the prognosis of pN1 was equivalent to pN2 and inferior to pN0; in contrast, among patients treated with PORT, the prognosis of pN1 was equivalent to pN0 and superior to pN2. Without PORT, pN1 remained an adverse risk factor relative to pN0 regardless of the depth of invasion, lymph node size, lymph node location, and extent of lymph node dissection.PORT was associated with a survival benefit compared with observation. Notably, pN1 was an adverse risk factor relative to pN0 if, and only if, patients did not receive PORT, suggesting pN1 by itself may be an indication for PORT.
View details for DOI 10.1097/COC.0000000000000792
View details for PubMedID 33417322
Treatment Breaks During Definitive Head/Neck Radiotherapy: Survival Impact and Predisposing Factors
ELSEVIER SCIENCE INC. 2020: E39
View details for Web of Science ID 000579885400086
Automated model versus treating physician for predicting survival time of patients with metastatic cancer.
Journal of the American Medical Informatics Association : JAMIA
Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model.A machine learning model was trained using 14 600 metastatic cancer patients' data to predict each patient's distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015-2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots.The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63-0.81), 0.77 (0.73-0.81), and 0.68 (0.65-0.71), respectively.The machine learning model's predictions were more accurate than those of the treating physician or a traditional model.
View details for DOI 10.1093/jamia/ocaa290
View details for PubMedID 33313792
Cancer Treatment Classification with Electronic Medical Health Records
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2020: 13981-13982
View details for Web of Science ID 000668126806183
Predicting per-lesion local recurrence in locally advanced non-small cell lung cancer following definitive radiation therapy using pre- and mid-treatment metabolic tumor volume.
Radiation oncology (London, England)
2020; 15 (1): 114
We evaluated whether pre- and mid-treatment metabolic tumor volume (MTV) predicts per lesion local recurrence (LR) in patients treated with definitive radiation therapy (RT, dose≥60 Gy) for locally advanced non-small cell lung cancer (NSCLC).We retrospectively reviewed records of patients with stage III NSCLC treated from 2006 to 2018 with pre- and mid-RT PET-CT. We measured the MTV of treated lesions on the pre-RT (MTVpre) and mid-RT (MTVmid) PET-CT. LR was defined per lesion as recurrence within the planning target volume. Receiver operating characteristic (ROC) curves, cumulative incidence rates, and uni- and multivariable (MVA) competing risk regressions were used to evaluate the association between MTV and LR.We identified 111 patients with 387 lesions (112 lung tumors and 275 lymph nodes). Median age was 68 years, 69.4% were male, 46.8% had adenocarcinoma, 39.6% had squamous cell carcinoma, and 95.5% received concurrent chemotherapy. Median follow-up was 38.7 months. 3-year overall survival was 42.3%. 3-year cumulative incidence of LR was 26.8% per patient and 11.9% per lesion. Both MTVpre and MTVmid were predictive of LR by ROC (AUC = 0.71 and 0.76, respectively) and were significantly associated with LR on MVA (P = 0.004 and P = 7.1e-5, respectively). Among lesions at lower risk of LR based on MTVpre, higher MTVmid was associated with LR (P = 0.001).Per-lesion, larger MTVpre and MTVmid predicted for increased risk of LR. MTVmid was more highly predictive of LR than MTVpre and if validated may allow for further discrimination of high-risk lesions at mid-RT informing dose painting strategies.
View details for DOI 10.1186/s13014-020-01546-y
View details for PubMedID 32429982
Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer.
2020; 10 (25): 11707–18
Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.
View details for DOI 10.7150/thno.50565
View details for PubMedID 33052242
- Multicenter Clinical Cancer Research After COVID-19: A Perspective From NRG Oncology. International journal of radiation oncology, biology, physics 2020; 108 (2): 483–85
Prolongation of definitive head and neck cancer radiotherapy: Survival impact and predisposing factors.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
To quantify the survival impact of prolongation of definitive radiotherapy (RT) for head and neck cancer in a national, modern cohort, and to identify predictive factors for prolongation.The National Cancer Database was queried for adults with non-metastatic cancer of the nasopharynx, oropharynx, larynx, or hypopharynx diagnosed 2004-2015, treated with definitive RT to 66-70 Gy in 30-35 fractions at 2-2.2 Gy per fraction. Multivariable Cox regression and propensity score matching were used to model the survival impact of RT prolongation, adjusting for potential confounders such as age and comorbidity. Predictors of RT prolongation were identified using multivariable multinomial logistic regression.In total, 36,367 patients were identified. As a continuous variable, RT prolongation increased the relative hazard of death by 2% per day (P < .0001). In the matched cohorts, patients with short (4-8 days) or long prolongation (> 8 days) had lower absolute 4-year overall survival by 4% and 12% respectively (P < .0001), while prolongation of 1-3 days was not significantly adverse. Major predictors of increased risk of prolongation were administration of systemic therapy, baseline comorbidity, lack of private insurance, and tumor/nodal stage. Conversely, higher facility volume was significantly protective, with a 55% lower risk of long prolongation within the topmost quartile (> 11.5 patients/year).RT prolongation, especially > 8 days, is significantly deleterious. Systemic therapy and facility volume were major predictors. Early identification of patients at increased risk of treatment interruptions may facilitate implementation of preventive measures.
View details for DOI 10.1016/j.radonc.2020.12.025
View details for PubMedID 33383061
Proton radiotherapy and treatment delay in head and neck squamous cell carcinoma.
OBJECTIVE: For patients with head and neck squamous cell carcinoma (HNSCC), delays in the initiation of radiotherapy (RT) have been closely associated with worse outcomes. We sought to investigate whether RT modality (proton vs. photon) is associated with differences in the time to initiation of RT.METHODS: The National Cancer Database was queried for patients diagnosed with nonmetastatic HNSCC between 2004 and 2015 who received either proton or photon RT as part of their initial treatment. Wilcoxon rank-sum and chi-square tests were used to compare continuous and categorical variables, respectively. Multivariable logistic regression was used to determine the association between use of proton RT and delayed RT initiation.RESULTS: A total of 175,088 patients with HNSCC receiving either photon or proton RT were identified. Patients receiving proton RT were more likely to be white, reside in higher income areas, and have private insurance. Proton RT was associated with delayed RT initiation compared to photon RT (median 59days vs. 45, P <0.001). Receipt of proton therapy was independently associated with RT initiation beyond 6weeks after diagnosis (adjusted OR [aOR, definitive RT] = 1.69; 95% confidence interval [CI] 1.26-2.30) or surgery (aOR [adjuvant RT] = 4.08; 95% CI 2.64-6.62). In the context of adjuvant proton RT, increases in treatment delay were associated with worse overall survival (weeks, adjusted hazard ratio =1.099, 95% CI 1.011-1.194).CONCLUSION: Use of proton therapy is associated with delayed RT in both the definitive and adjuvant settings for patients with HNSCC and could be associated with poorer outcomes.LEVEL OF EVIDENCE: 2b Laryngoscope, 122:0000-0000, 2019 Laryngoscope, 2019.
View details for DOI 10.1002/lary.28458
View details for PubMedID 31837165
Predicting Survival for Patients with Metastatic Disease.
International journal of radiation oncology, biology, physics
PURPOSE: This prospective study aimed to determine the accuracy of radiation oncologists in predicting the survival of patients with metastatic disease receiving radiotherapy and to understand factors associated with their accuracy.METHODS AND MATERIALS: This single-institution study surveyed 22 attending radiation oncologists to estimate patient survival. Survival predictions were defined as accurate if the observed survival (OS) was within the correct survival prediction category (0-6 months, >6-12 months, >12-24 months, and >24 months). The physicians made survival estimates for each course of radiation, yielding 877 analyzable predictions for 689 unique patients. Data analysis included Stuart's Tau C, logistic regression models, ordinal logistic regression models, and stepwise selection to examine variable interactions.RESULTS: Of the 877 radiation oncologists' predictions, 39.7% were accurate, 26.5% underestimations, and 33.9% overestimations. Stuart's Tau C showed low correlation between OS and survival estimates (0.3499), consistent with the inaccuracy reported in literature. However, results showed less systematic over-prediction than reported in the literature. Karnofsky performance status (KPS) was the most significant predictor of accuracy with greater accuracy for patients with shorter OS. Estimates were also more accurate for patients with lower KPS. Accuracy by patient age varied by primary site and race. Physician years of experience did not correlate with accuracy.CONCLUSIONS: The sampled radiation oncologists have relatively low accuracy in predicting patient survival. Future investigation should explore how survival estimates influence treatment decisions and how to improve survival prediction accuracy.
View details for DOI 10.1016/j.ijrobp.2019.10.032
View details for PubMedID 31682969
Tumor Subregion Evolution-based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Background: The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for de-intensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Purpose: To develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on fluorine 18 (18F) fluorodeoxyglucose (FDG) PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumor/nodal tissue between baseline and mid-treatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified three phenotypically distinct intratumoral habitats, which were (1) metabolically active and heterogeneous, (2) enhancing and heterogeneous, and (3) metabolically inactive and homogeneous. The final Cox model consisted of four habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics including mid-treatment metabolic tumor volume for predicting PFS, with C-index: 0.72 vs 0.67 (training) and 0.66 vs 0.56 (validation). The imaging signature stratified patients into high-risk vs low-risk groups with 2-year PFS rates: 59.1% vs 89.4% (HR=4.4, 95% CI: 1.4-13.4, training), and 61.4% vs 87.8% (HR=4.6, 95% CI: 1.7-12.1, validation). It remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.
View details for DOI 10.2967/jnumed.119.230037
View details for PubMedID 31420498
- Radiographic Extranodal Extension in Human Papillomavirus-Associated Oropharyngeal Carcinoma: Can it Help Tailor Treatment? International journal of radiation oncology, biology, physics 2019; 104 (5): 1028–29
- Adverse Radiation Effect and Disease Control in Patients Undergoing Stereotactic Radiosurgery and Immune Checkpoint Inhibitor Therapy for Brain Metastases WORLD NEUROSURGERY 2019; 126: E1399–E1411
- Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE 2019; 111 (6): 568–74
Increases in Serial Pretreatment 18F-FDG PET-CT Metrics Predict Survival in Early Stage Non-Small Cell Lung Cancer Treated With Stereotactic Ablative Radiation Therapy.
Advances in radiation oncology
2019; 4 (2): 429–37
Purpose: Quantitative changes in positron emission tomography with computed tomography imaging metrics over serial scans may be predictive biomarkers. We evaluated the relationship of pretreatment metabolic tumor growth rate (MTGR) and standardized uptake value velocity (SUVV) with disease recurrence or death in patients with early-stage non-small cell lung cancer treated with stereotactic ablative radiation therapy (SABR).Methods and Materials: Under institutional review board approval, we retrospectively identified patients who underwent positron emission tomography with computed tomography at diagnosis and staging and simulation for SABR. Two cohorts underwent SABR between November 2005 to October 2012 (discovery) and January 2012 to April 2016 (validation). MTGR and SUVV were calculated as the daily change in metabolic tumor volume and maximum standardized uptake value, respectively. Cox proportional hazard models identified predictors of local, regional, and distant recurrence and death for the combined cohort. MTGR and SUVV thresholds dichotomizing risk of death in the discovery cohort were applied to the validation cohort.Results: A total of 152 lesions were identified in 143 patients (92 lesions in 83 discovery cohort patients). In multivariable models, increasing MTGR trended toward increased hazard of distant recurrence (hazard ratio, 6.98; 95% confidence interval, 0.67-72.61; P=.10). In univariable models, SUVV trended toward risk of death (hazard ratio, 11.8, 95% confidence interval, 0.85-165.1, P=.07). MTGR greater than 0.04mL/d was prognostic of decreased survival in discovery (P=.048) and validation cohorts (P<.01).Conclusions: MTGR greater than 0.04mL/d is prognostic of death in patients with non-small cell lung cancer treated with SABR. Increasing SUVV trends, nonsignificantly, toward increased risk of recurrence and death. MTGR and SUVV may be candidate imaging biomarkers to study in trials evaluating systemic therapy with SABR for patients at high risk of out-of-field recurrence.
View details for PubMedID 31011689
- Increases in Serial Pretreatment F-18-FDG PET-CT Metrics Predict Survival in Early Stage Non-Small Cell Lung Cancer Treated With Stereotactic Ablative Radiation Therapy ADVANCES IN RADIATION ONCOLOGY 2019; 4 (2): 429–37
Adverse Radiation Effect and Disease Control in Patients Undergoing Stereotactic Radiosurgery and Immune Checkpoint Inhibitor Therapy for Brain Metastases.
BACKGROUND: Immune checkpoint inhibitors (ICIs) and stereotactic radiosurgery (SRS) are increasingly used together to manage brain metastases (BMs). We assessed adverse radiation effect, disease control, and overall survival in patients with BMs who received SRS with anti-CTLA-4 and/or anti-PD-1/PD-L1 therapies.METHODS: We retrospectively reviewed the records of patients with intact or resected BMs treated with SRS and ICIs within 5 months of SRS between 2010 and 2018. Patients were defined as receiving 'concurrent' SRS and ICI if a dose of ICI was given within 4 weeks of SRS. Local failure (LF), distant intracranial failure (DIF), extracranial failure (EF), and adverse radiation effect (ARE) were assessed using cumulative incidence rates and competing risk regressions with death as a competing risk. Overall survival was assessed using the Kaplan-Meier method and Cox proportional hazards models.RESULTS: A total of 97 patients with 580 BMs were included in our analysis. Competing risk analyses demonstrated that concurrent SRS-ICI therapy is associated with higher rates of ARE (6.4% vs 2.0% at 1 year; multivariable HR 4.47; 95% CI, 1.57-12.73; p=0.005), lower rates of EF (69.7% vs 80.8% at 1 year; multivariable HR 0.60; 95% CI, 0.42-0.87; p=0.007), and better overall survival (48.6% vs 25.4% at 1 year; multivariable HR 0.57; 95% CI, 0.33-0.99; p=0.044) as compared to non-concurrent therapy. SRS-ICI timing was not associated with LF or DIF.CONCLUSIONS: Concurrent SRS-ICI therapy has a tolerable adverse event profile and may improve extracranial disease control and overall survival, supporting concurrent use in the management of BMs.
View details for PubMedID 30902777
- A scalable discrete-time survival model for neural networks PEERJ 2019; 7
Impact of Accuracy of Survival Predictions on Quality of End-of-Life Care Among Patients With Metastatic Cancer Who Receive Radiation Therapy.
Journal of oncology practice
PURPOSE:: For patients treated with palliative radiation, we examined the association between life expectancy predictions by radiation oncologists and aggressive end-of-life care.MATERIALS AND METHODS:: We included decedents from a study that assessed the ability of oncologists to predict survival of patients with metastatic cancer who received radiation. We identified patients who died within 12 months of study enrollment to assess accuracy of predictions. Aggressive end-of-life care was defined by the National Quality Forum, ASCO Quality Oncology Practice Initiative metrics, and advanced radiation modalities in the last month of life. Survival predictions were categorized as follows: correct (< 12 months), 12 to 18 months, 18 to 24 months, and more than 24 months. We assessed association between prediction and aggressive end-of-life care using a generalized estimation equation.RESULTS:: Of 489 decedents, we identified 467 encounters with survival estimates. Overall, 156 decedents (32%) met at least one metric of aggressive end-of-life care. Factors associated with aggressive end-of-life care included younger age, female sex, primary cancer diagnosis, no brain metastases, and private insurance. In each encounter when an oncologist predicted survival, 363 predictions (78%) were correct (< 12 months), 54 (11%) incorrectly predicted 12 to 18 months, 27 (6%) predicted 18 to 24 months, and 23 (5%) predicted more than 24 months. Compared with patients who had encounters that had correct survival predictions, patients predicted to live more than 24 months were more likely to meet at least one metric of aggressive end-of-life care (odds ratio, 2.55; 95% CI, 1.09 to 5.99; P = .03).CONCLUSION:: Inaccurate survival predictions by oncologists are associated with more aggressive end-of-life care for patients with advanced cancer.
View details for DOI 10.1200/JOP.18.00516
View details for PubMedID 30620629
Predictors of Respiratory Decline Following Stereotactic Ablative Radiotherapy to Multiple Lung Tumors.
Clinical lung cancer
Stereotactic ablative radiotherapy (SABR) is highly effective at controlling early stage primary lung cancer and lung metastases. Although previous studies have suggested that treating multiple lung tumors with SABR is safe, post-treatment changes in respiratory function have not been analyzed in detail.We retrospectively identified patients with 2 or more primary lung cancers or lung metastases treated with SABR and analyzed clinical outcomes and predictors of toxicity. We defined a composite respiratory decline endpoint to include increased oxygen requirement, increased dyspnea scale, or death from respiratory failure not owing to disease progression.A total of 86 patients treated with SABR to 203 lung tumors were analyzed. A total of 21.8% and 41.8% of patients developed composite respiratory decline at 2 and 4 years, respectively. When accounting for intrathoracic disease progression, 12.7% of patients developed composite respiratory decline at 2 years. Of the patients, 7.9% experienced grade 2 or greater radiation pneumonitis. No patient- or treatment-related factor predicted development of respiratory decline. The median overall survival was 46.9 months, and the median progression-free survival was 14.8 months. The cumulative incidence of local failure was 9.7% at 2 years.Although our results confirm that SABR is an effective treatment modality for patients with multiple lung tumors, we observed a high rate of respiratory decline after treatment, which may be owing to a combination of treatment and disease effects. Future studies may help to determine ways to avoid pulmonary toxicity from SABR.
View details for DOI 10.1016/j.cllc.2019.05.015
View details for PubMedID 31377143
A scalable discrete-time survival model for neural networks.
2019; 7: e6257
There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD). The use of SGD enables rapid convergence and application to large datasets that do not fit in memory. The model is flexible, so that the baseline hazard rate and the effect of the input data on hazard probability can vary with follow-up time. It has been implemented in the Keras deep learning framework, and source code for the model and several examples is available online. We demonstrate the performance of the model on both simulated and real data and compare it to existing models Cox-nnet and Deepsurv.
View details for PubMedID 30701130
A radiation oncology-specific automated trigger indicator tool for high-risk near-miss safety events.
Practical radiation oncology
Error detection in radiation oncology relies heavily on voluntary reporting and many adverse events and near-misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology-specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS).Twenty triggers were derived from an electronic radiation oncology information system (OIS). Data from the ILS and OIS over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near-miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near-misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near-misses.3,159 treatment courses were analyzed; 357 had a grade 3-4 ILS entry. 2,210 courses comprised the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, nine reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near-miss with an ILS entry had a direct relationship between the triggers and the near-miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near-miss without an ILS entry found two unreported near-miss events.The radiation oncology-specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration.
View details for DOI 10.1016/j.prro.2019.10.017
View details for PubMedID 31783170
- In Regard to Valdes et al. International journal of radiation oncology, biology, physics 2018; 102 (5): 1593-1594
- A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS 2018; 102 (4): 1098–1106
- Adaptive radiotherapy for head and neck cancer: Are we ready to put it into routine clinical practice? ORAL ONCOLOGY 2018; 86: 19–24
Adaptive radiotherapy for head and neck cancer: Are we ready to put it into routine clinical practice?
2018; 86: 19–24
Patients with head and neck cancer who are treated with radiotherapy often have significant weight loss or tumor regression during treatment. Adaptive radiotherapy refers to acquiring new imaging during treatment and changing the parameters of the radiation plan based on the new imaging findings. There is accumulating evidence that adaptive radiotherapy can reduce toxicity and improve tumor control, though it is not yet known which patients benefit most. For patients with profound tumor regression, there is also uncertainty about how much to shrink the region receiving high radiation dose. Another form of adaptive radiotherapy uses advanced imaging such as positron emission tomography to visualize changes in tumor biology during treatment. Tumor regions that are thought to be more radioresistant can then be treated to a higher radiation dose, and vice-versa. Studies employing this strategy to boost radiation dose have shown a high rate of late toxicity, specifically the development of persistent mucosal ulcers. Therefore, this sort of adaptive radiotherapy is currently confined to the research setting.
View details for PubMedID 30409300
Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data.
Journal of the National Cancer Institute
Background: Oncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer.Methods: The model was trained and tested using 12588 patients treated for metastatic cancer in the Stanford Health Care system from 2008 to 2017. Data sources included provider note text, labs, vital signs, procedures, medication orders, and diagnosis codes. Patients were divided randomly into a training set used to fit the model coefficients and a test set used to evaluate model performance (80%/20% split). A regularized Cox model with 4126 predictor variables was used. A landmarking approach was used due to the multiple observations per patient, with t0 set to the time of metastatic cancer diagnosis. Performance was also evaluated using 399 palliative radiation courses in test set patients.Results: The C-index for overall survival was 0.786 in the test set (averaged across landmark times). For palliative radiation courses, the C-index was 0.745 (95% confidence interval [CI] = 0.715 to 0.775) compared with 0.635 (95% CI = 0.601 to 0.669) for a published model using performance status, primary tumor site, and treated site (two-sided P<.001). Our model's predictions were well-calibrated.Conclusions: The model showed high predictive performance, which will need to be validated using external data. Because it is fully automated, the model can be used to examine providers' practice patterns and could be deployed in a decision support tool to help improve quality of care.
View details for PubMedID 30346554
Factors Associated With Treatment of Clinical Stage I Non-Small-cell Lung Cancer: A Population-based Analysis.
Clinical lung cancer
2018; 19 (5): e745–e758
BACKGROUND: The present study examined clinical stage I non-small-cell lung cancer (NSCLC) treatment in the population-based California Cancer Registry.PATIENTS AND METHODS: The characteristics associated with first clinical stage I NSCLC treatment (surgery, radiation, no local therapy) from 2003 to 2014 were identified using logistic regression. Survival was evaluated using Kaplan-Meier and Cox proportional hazard analyses.RESULTS: Surgery was used in most patients who met the inclusion criteria (14,545 of 19,893; 73.1%), although relatively similar numbers had undergone radiation (n= 2848; 14.3%) or not received therapy (n= 2500; 12.6%). Surgery use ranged from 68.5% to 77.2% patients annually. The percentage of patients with no therapy decreased from 18.1% (315 of 1737) in 2003 to 10.3% (176 of 1703) in 2014, and radiation use increased from 10.7% (185 of 1737) in 2003 to 21.2% (361 of 1703) in 2014. Patients who did not receive therapy were more likely to be older, not white, male, and unmarried, to have no insurance or public insurance other than Medicare, to live in a lower socioeconomic status neighborhood, to have been seen at a non-National Cancer Institute cancer center hospital or hospital serving lower socioeconomic status patients, and to have larger tumors. The 5-year all-cause survival after no therapy (12.7%) was significantly worse than that after surgery (64.9%) or radiation (21.5%; P< .0001).CONCLUSION: In the present population-based analysis, surgery was the most common treatment for clinical stage I NSCLC but was not used for almost 27% of patients. Radiation use increased and the proportion of patients who did not receive any therapy decreased over time.
View details for PubMedID 30149883
- Prognostic Value of Pretreatment FDG-PET Parameters in High-dose Image-guided Radiotherapy for Oligometastatic Non-Small-cell Lung Cancer CLINICAL LUNG CANCER 2018; 19 (5): E581–E588
- Factors Associated With Treatment of Clinical Stage I Non-Small-cell Lung Cancer: A Population-based Analysis CLINICAL LUNG CANCER 2018; 19 (5): E745–E758
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives.
2018; 8 (1): 10037
We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model's predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.
View details for PubMedID 29968730
- Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives SCIENTIFIC REPORTS 2018; 8
- Chest wall dose reduction using noncoplanar volumetric modulated arc radiation therapy for lung stereotactic ablative radiation therapy PRACTICAL RADIATION ONCOLOGY 2018; 8 (4): E199–E207
A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer.
International journal of radiation oncology, biology, physics
Prognostic biomarkers are needed to guide the management of early-stage non-small cell lung cancer (NSCLC). This work aims to develop an image-based prognostic signature and assess its complementary value to existing biomarkers.We retrospectively analyzed data of stage I NSCLC in 8 cohorts. On the basis of an analysis of 39 computed tomography (CT) features characterizing tumor and its relation to neighboring pleura, we developed a prognostic signature in an institutional cohort (n = 117) and tested it in an external cohort (n = 88). A third cohort of 89 patients with CT and gene expression data was used to create a surrogate genomic signature of the imaging signature. We conducted further validation using data from 5 gene expression cohorts (n = 639) and built a composite signature by integrating with the cell-cycle progression (CCP) score and clinical variables.An imaging signature consisting of a pleural contact index and normalized inverse difference was significantly associated with overall survival in both imaging cohorts (P = .0005 and P = .0009). Functional enrichment analysis revealed that genes highly correlated with the imaging signature were related to immune response, such as lymphocyte activation and chemotaxis (false discovery rate < 0.05). A genomic surrogate of the imaging signature remained a significant predictor of survival when we adjusted for known prognostic factors (hazard ratio, 1.81; 95% confidence interval, 1.34-2.44; P < .0001) and stratified patients within subgroups as defined by stage, histology, or CCP score. A composite signature outperformed the genomic surrogate, CCP score, and clinical model alone (P < .01) regarding concordance index (0.70 vs 0.62-0.63).The proposed CT imaging signature reflects fundamental biological differences in tumors and predicts overall survival in patients with stage I NSCLC. When combined with established prognosticators, the imaging signature improves survival prediction.
View details for PubMedID 29439884
Prognostic Value of Pretreatment FDG-PET Parameters in High-dose Image-guided Radiotherapy for Oligometastatic Non-Small-cell Lung Cancer.
Clinical lung cancer
Emerging data support aggressive local treatment of oligometastatic non-small-cell lung cancer (NSCLC) patients. We sought to determine whether the metabolic burden of disease found by fluorodeoxyglucose positron emission tomography at the time of high-dose radiotherapy (RT) for oligometastatic NSCLC can serve as a prognostic biomarker.We conducted a retrospective cohort study of 67 RT treatment courses in 55 patients with oligometastatic NSCLC who had undergone high-dose RT to all sites of active disease at our institution. The metabolic tumor volume, total lesion glycolysis (TLG), and maximum standardized uptake value of all lesions were measured on the pretreatment fluorodeoxyglucose positron emission tomography scans. Cox regression analysis was used to assess the influence of imaging and clinical factors on overall survival (OS).On univariate analysis, a greater metabolic tumor volume and TLG were predictive of shorter OS (hazard ratio of death, 2.42 and 2.14, respectively; P = .009 and P = .004, respectively). The effects remained significant on multivariate analysis. Neither the maximum standardized uptake value nor the number of lesions was significantly associated with OS. Patients within the highest quartile of TLG values (> 86.8 units) had a shorter median OS than those within the lower 3 quartiles (12.4 vs. 30.1 months; log-rank P = .014).The metabolic tumor burden was prognostic of OS and might help to better select oligometastatic NSCLC patients for locally ablative therapy.
View details for PubMedID 29759331
Mid-radiotherapy PET/CT for prognostication and detection of early progression in patients with stage III non-small cell lung cancer
RADIOTHERAPY AND ONCOLOGY
2017; 125 (2): 338–43
Pre- and mid-radiotherapy FDG-PET metrics have been proposed as biomarkers of recurrence and survival in patients treated for stage III non-small cell lung cancer. We evaluated these metrics in patients treated with definitive radiation therapy (RT). We also evaluated outcomes after progression on mid-radiotherapy PET/CT.Seventy-seven patients treated with RT with or without chemotherapy were included in this retrospective study. Primary tumor and involved nodes were delineated. PET metrics included metabolic tumor volume (MTV), total lesion glycolysis (TLG), and SUVmax. For mid-radiotherapy PET, both absolute value of these metrics and percentage decrease were analyzed. The influence of PET metrics on time to death, local recurrence, and regional/distant recurrence was assessed using Cox regression.91% of patients had concurrent chemotherapy. Median follow-up was 14months. None of the PET metrics were associated with overall survival. Several were positively associated with local recurrence: pre-radiotherapy MTV, and mid-radiotherapy MTV and TLG (p=0.03-0.05). Ratio of mid- to pre-treatment SUVmax was associated with regional/distant recurrence (p=0.02). 5/77 mid-radiotherapy scans showed early out-of-field progression. All of these patients died.Several PET metrics were associated with risk of recurrence. Progression on mid-radiotherapy PET/CT was a poor prognostic factor.
View details for PubMedID 28830717
Practical workflow for rapid prototyping of radiation therapy positioning devices
PRACTICAL RADIATION ONCOLOGY
2017; 7 (6): 442–45
View details for PubMedID 28668669
- Circulating Tumor DNA Detects Residual Disease and Anticipates Tumor Progression Earlier Than CT Imaging ELSEVIER SCIENCE INC. 2017: E4
- Stereotactic Ablative Radiotherapy for Stage I Non-Small-Cell Lung Cancer Tumors Greater Than 5 cm ELSEVIER SCIENCE INC. 2017: E38
Pulmonary function after lung tumor stereotactic ablative radiotherapy depends on regional ventilation within irradiated lung.
Radiotherapy and oncology
2017; 123 (2): 270-275
To determine if regional ventilation within irradiated lung volume predicts change in pulmonary function test (PFT) measurements after stereotactic ablative radiotherapy (SABR) of lung tumors.We retrospectively identified 27 patients treated from 2007 to 2014 at our institution who received: (1) SABR without prior thoracic radiation; (2) pre-treatment 4-dimensional computed tomography (4-D CT) imaging; (3) pre- and post-SABR PFTs <15months from treatment. We defined the ventilation ratio (VR20BED3) as the quotient of mean ventilation (mean Jacobian-based per-voxel volume change on deformably registered inhale/exhale 4-D CT phases) within the 20Gy biologically effective dose (α/β=3Gy) isodose volume and that of the total lung volume (TLV).Most patients had moderate to very severe COPD by GOLD criteria (n=19, 70.1%). Higher VR20BED3 significantly predicted worse change in Forced Expiratory Volume/s normalized by baseline value (ΔFEV1/FEV1pre, p=0.04); n=7 had VR20BED3>1 (high regional ventilation) and worse ΔFEV1/FEV1pre (median=-0.16, range=-0.230 to -0.20). Five had VR20BED3<1 (low regional ventilation) and improved ΔFEV1/FEV1pre (median=0.13, range=0.07 to 0.20). In a multivariable linear model, increasing VR20BED3 and time to post-SABR PFT predicted decreasing ΔFEV1/FEV1pre (R(2)=0.25, p=0.03).After SABR to high versus low functioning lung regions, we found worsened or improved global pulmonary function, respectively. If pre-SABR VR20BED3 is validated as a predictor of eventual post-SABR PFT in larger studies, it may be used for individualized treatment planning to preserve or even improve pulmonary function after SABR.
View details for DOI 10.1016/j.radonc.2017.03.021
View details for PubMedID 28460826
Optimal Radiation Therapy for Small Cell Lung Cancer.
Current treatment options in oncology
2017; 18 (4): 21-?
Radiation therapy plays an important role in the management of both limited stage and extensive stage small cell lung cancer. For limited stage disease, there has been a trend toward reduced size of thoracic radiation fields, which has the potential to reduce toxicity. FDG-PET staging helps make this possible by more accurately identifying areas of nodal and metastatic involvement. Trials have demonstrated similar outcomes using a range of radiation fractionation schedules, allowing flexibility in individualizing treatment. Using advanced radiation therapy techniques such as intensity-modulated radiation therapy, it may be possible to deliver fewer, higher dose fractions and achieve similar results to the hyperfractionated regimen. For extensive stage disease, consolidative thoracic radiation therapy after chemotherapy was recently shown to improve overall survival in certain patient subsets. Prophylactic cranial irradiation continues to play an important role in management of all stages of small cell lung cancer. Debate continues about the neurocognitive effects of this treatment, and whether MRI surveillance is an acceptable alternative. Strategies such as hippocampal avoidance may reduce the cognitive effects of prophylactic cranial irradiation in the future. Finally, in the last few years stereotactic ablative radiation therapy followed by chemotherapy has emerged as a promising treatment for stage I small cell lung cancer. This radiation treatment is usually given over 1-5 fractions and appears to provide a good rate of local control with a low rate of serious toxicity.
View details for DOI 10.1007/s11864-017-0467-z
View details for PubMedID 28391424
Sinoatrial node dysfunction after stereotactic ablative radiation therapy in the chest
AMER SOC CLINICAL ONCOLOGY. 2017
View details for Web of Science ID 000443300500123
Prognostic value and molecular correlates of a CT image-based quantitative pleural contact index in early stage NSCLC.
To evaluate the prognostic value and molecular basis of a CT-derived pleural contact index (PCI) in early stage non-small cell lung cancer (NSCLC).We retrospectively analysed seven NSCLC cohorts. A quantitative PCI was defined on CT as the length of tumour-pleura interface normalised by tumour diameter. We evaluated the prognostic value of PCI in a discovery cohort (n = 117) and tested in an external cohort (n = 88) of stage I NSCLC. Additionally, we identified the molecular correlates and built a gene expression-based surrogate of PCI using another cohort of 89 patients. To further evaluate the prognostic relevance, we used four datasets totalling 775 stage I patients with publically available gene expression data and linked survival information.At a cutoff of 0.8, PCI stratified patients for overall survival in both imaging cohorts (log-rank p = 0.0076, 0.0304). Extracellular matrix (ECM) remodelling was enriched among genes associated with PCI (p = 0.0003). The genomic surrogate of PCI remained an independent predictor of overall survival in the gene expression cohorts (hazard ratio: 1.46, p = 0.0007) adjusting for age, gender, and tumour stage.CT-derived pleural contact index is associated with ECM remodelling and may serve as a noninvasive prognostic marker in early stage NSCLC.• A quantitative pleural contact index (PCI) predicts survival in early stage NSCLC. • PCI is associated with extracellular matrix organisation and collagen catabolic process. • A multi-gene surrogate of PCI is an independent predictor of survival. • PCI can be used to noninvasively identify patients with poor prognosis.
View details for PubMedID 28786009
Sinoatrial node toxicity after stereotactic ablative radiation therapy to lung tumors.
Practical radiation oncology
Stereotactic ablative radiation therapy (SABR) is an established treatment for selected lung tumors. Sinoatrial node (SAN) toxicity after thoracic SABR has not been reported in the literature. We sought to understand the risk of SAN toxicity owing to incidental dose to the SAN from SABR.We conducted a retrospective review of patients with early-stage lung cancer or limited pulmonary metastases who underwent thoracic SABR to a right-sided central lung tumor (within 2 cm of the mainstem bronchus or other mediastinal structures) between January 2008 and December 2014, analyzed a subset whose treatment imparted dose to the SAN exceeding 10% of the prescription dose, and examined patient and treatment dosimetric characteristics. Mean follow-up interval was 28 months. Time to toxicity was defined from start of SABR.Of 47 patients with central tumors in the right lung treated with SABR reviewed, 13 met our study criteria. A contouring atlas of regional cardiac anatomy was created. One patient treated with SABR for non-small cell lung cancer at the right hilum developed symptomatic sick sinus syndrome, requiring pacemaker placement 6 months after treatment. Her acute presentation and short interval between SABR and onset of symptoms suggest that SAN toxicity was likely due to radiation-induced injury. Both her age and mean dose to her SAN were the third highest in our cohort. She remained free from cancer progression at 24 months' follow-up. Twelve additional patients who received significant dose to the SAN from SABR did not develop toxicity.While uncommon, SAN toxicity from SABR to right-sided central thoracic tumors should be recognized and followed in future studies.
View details for PubMedID 28669706
Chest wall dose reduction using noncoplanar volumetric modulated arc radiation therapy for lung stereotactic ablative radiation therapy.
Practical radiation oncology
Stereotactic ablative radiation therapy (SABR) to lung tumors close to the chest wall can cause rib fractures or chest wall pain. We evaluated and propose a clinically practical solution of using noncoplanar volumetric modulated arc radiation therapy (VMAT) to reduce chest wall dose from lung SABR.Twenty lung SABR VMAT plans in which the chest wall volume receiving 30 Gy or higher (V30) exceeded 30 mL were replanned by noncoplanar VMAT with opposite 15° couch kicks. Dosimetric parameters including chest wall V30 and V40; lung V5, V10, V20, and mean dose; Paddick high-dose conformity index; intermediate-dose conformity index; and monitor units (MU) for each plan were used to evaluate the plan quality. The treatment time was also estimated by delivering the entire treatment. Two-sided paired t test was used to evaluate the difference of the dosimetric parameters between coplanar 1 arc (cVMAT1), coplanar 2 arcs (cVMAT2), and noncoplanar two arcs (nVMAT2) plans; differences with P < .05 were considered statistically significant.V30 and V40 for chest wall were reduced on average by 20% ± 9% and 15% ± 11% (mean ± standard deviation) from cVMAT2 plans to nVMAT2 plans (P < .01 for both comparisons) and by 8% ± 7% and 16% ± 13% from cVMAT1 plans to cVMAT2 plans (P < .003 for both comparisons). The differences in lung mean dose were <0.2 Gy among cVMAT1, cVMAT2, and nVMAT2. There were no significant differences in lung V5, V10, and V20. On average, the number of MU increased 14% for nVMAT2 compared with cVMAT2. The Paddick high-dose conformity indexes were 0.88 ± 0.03, 0.89 ± 0.04, and 0.91 ± 0.03, and intermediate-dose conformity indexes were 3.88 ± 0.49, 3.80 ± 0.44 and 3.51 ± 0.38 for cVMAT1, cVMAT2, and nVMAT2, respectively.We found that noncoplanar VMAT plans are feasible, clinically practical to deliver, and significantly reduce V30 and V40 of chest wall without increasing lung dose.
View details for PubMedID 29452868
Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling.
Identifying molecular residual disease (MRD) after treatment of localized lung cancer could facilitate early intervention and personalization of adjuvant therapies. Here we apply Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) circulating tumor DNA (ctDNA) analysis to 255 samples from 40 patients treated with curative intent for stage I-III lung cancer and 54 healthy adults. In 94% of evaluable patients experiencing recurrence, ctDNA was detectable in the first post-treatment blood sample, indicating reliable identification of MRD. Post-treatment ctDNA detection preceded radiographic progression in 72% of patients by a median of 5.2 months and 53% of patients harbored ctDNA mutation profiles associated with favorable responses to tyrosine kinase inhibitors or immune checkpoint blockade. Collectively, these results indicate that ctDNA MRD in lung cancer patients can be accurately detected using CAPP-Seq and may allow personalized adjuvant treatment while disease burden is lowest.
View details for PubMedID 28899864
Contribution of submandibular gland and swallowing structure sparing to post-radiation therapy PEG dependence in oropharynx cancer patients treated with split-neck IMRT technique
Radiation therapy-related dysphagia is worsened by xerostomia. The submandibular glands (SMG) produce saliva rich in lubricating mucins, and sparing the SMG has been shown to reduce xerostomia. The goal of this study was to determine whether SMG sparing IMRT is associated with reduced post-treatment PEG dependence in locally advanced oropharynx cancer patients.Patients treated with definitive radiation therapy for oropharynx cancer were included in this retrospective study. Those with disease recurrence were excluded. Salivary glands and swallowing-related organs at risk, including pharyngeal constrictors, were contoured. Primary endpoint was time from end of radiation treatment to freedom from gastrostomy (PEG) tube dependence. Cox proportional hazards regression and logistic regression were used to assess influence of normal tissue doses on swallowing related endpoints.Sixty-nine patients were included. All had stage III/IV disease and 97% received concurrent systemic therapy. Fifty-seven percent had contralateral SMG (cSMG) mean dose <50 Gy, a level shown to predict for xerostomia. Eighty four percent of patients had a PEG tube placed electively. On univariate analysis, the strongest predictor of time to freedom from PEG tube dependence was cSMG dose (HR 0.97 per Gy (95% CI 0.95-0.98), p < 0.0001). This relationship persisted on multivariate analysis (p = 0.052). The dose to superior and middle pharyngeal constrictor muscles, and larynx were also significant on univariate analysis. Patients with cSMG dose less than median (42 Gy, n = 34) had a significantly shorter time to freedom from PEG dependence: median 1.9 vs. 3.5 months, p < 0.0001. At 6 months, 3% of patients with cSMG dose < 42 Gy were PEG dependent compared to 31% with cSMG dose > 42 Gy (p = 0.002).Patients treated with cSMG sparing radiotherapy had significantly shorter time to PEG tube removal after treatment, suggesting a clinically meaningful reduction in subacute dysphagia compared to non-cSMG sparing treatment.
View details for DOI 10.1186/s13014-016-0726-3
View details for Web of Science ID 000388245500002
View details for PubMedID 27846899
View details for PubMedCentralID PMC5111199
Hypofractionated Intensity-Modulated Radiotherapy for Patients With Non-Small-Cell Lung Cancer.
Clinical lung cancer
2016; 17 (6): 588-594
Alternative treatment regimens are needed for patients with non-small cell lung cancer (NSCLC) who cannot receive definitive treatment with concurrent chemoradiotherapy, surgery, or stereotactic ablative radiotherapy (SABR).We report survival, patterns of failure and toxicity outcomes for patients with NSCLC who were not eligible for surgical resection, concurrent chemoradiotherapy, or SABR and underwent hypofractionated intensity-modulated radiotherapy (IMRT). Kaplan-Meier survival analysis was used to evaluate the progression-free and overall survival. Competing risk analysis was used to evaluate in-field, locoregional, and distant failure.A total of 42 patients treated to 52.5 to 60 Gy in 15 fractions were included. Most of the patients had metastatic or recurrent disease (64%) and a relatively large, centrally located tumor burden (74%). The median follow-up period was 13 months (interquartile range, 6-18 months). All patients received the total prescribed dose. The median survival was 15.1 months. The overall and progression-free survival rates at 1 year were 63% and 22.5%, respectively. The pattern of failure was predominantly distant, with only 2% of patients experiencing isolated in-field recurrence. The cumulative incidence of in-field failure at 6 and 12 months was 2.5% (95% confidence interval, 0.4%-15.6%) and 16.1% (95% confidence interval, 7.5%-34.7%), respectively. The risk of esophageal toxicity was associated with the esophageal mean dose, maximal point dose, and dose to the 5 cm(3) volume. The risk of pneumonitis was associated with the lung mean dose and volume receiving 18 Gy.Hypofractionated IMRT without concurrent chemotherapy provides favorable rates of local control and survival for well-selected patients with NSCLC who cannot tolerate standard definitive therapy.
View details for DOI 10.1016/j.cllc.2016.05.024
View details for PubMedID 27378172
Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.
International journal of radiation oncology, biology, physics
2016; 95 (5): 1504-1512
To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer.In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.
View details for DOI 10.1016/j.ijrobp.2016.03.018
View details for PubMedID 27212196
- SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer. Medical physics 2016; 43 (6): 3349-?
Pre-treatment non-target lung FDG-PET uptake predicts symptomatic radiation pneumonitis following Stereotactic Ablative Radiotherapy (SABR).
Radiotherapy and oncology
2016; 119 (3): 454-460
To determine if pre-treatment non-target lung FDG-PET uptake predicts for symptomatic radiation pneumonitis (RP) following lung stereotactic ablative radiotherapy (SABR).We reviewed a 258 patient database from our institution to identify 28 patients who experienced symptomatic (grade ⩾ 2) RP after SABR, and compared them to 57 controls who did not develop symptomatic RP. We compared clinical, dosimetric and functional imaging characteristics between the 2 cohorts including pre-treatment non-target lung FDG-PET uptake.Median follow-up time was 26.9 months. Patients who experienced symptomatic RP had significantly higher non-target lung FDG-PET uptake as measured by mean SUV (p < 0.0001) than controls. ROC analysis for symptomatic RP revealed area under the curve (AUC) of 0.74, with sensitivity 82.1% and specificity 57.9% with cutoff mean non-target lung SUV > 0.56. Predictive value increased (AUC of 0.82) when mean non-target lung SUV was combined with mean lung dose (MLD). We developed a 0-2 point model using these 2 variables, 1 point each for SUV > 0.56 or MLD > 5.88 Gy equivalent dose in 2 Gy per fraction (EQD2), predictive for symptomatic RP in our cohort with hazard ratio 10.01 for score 2 versus 0 (p < 0.001).Patients with elevated pre-SABR non-target lung FDG-PET uptake are at increased risk of symptomatic RP after lung SABR. Our predictive model suggests patients with mean non-target lung SUV > 0.56 and MLD > 5.88 Gy EQD2 are at highest risk. Our predictive model should be validated in an external cohort before clinical implementation.
View details for DOI 10.1016/j.radonc.2016.05.007
View details for PubMedID 27267049
Influence of planning time and treatment complexity on radiation therapy errors.
Practical radiation oncology
2016; 6 (3): 187-193
Radiation treatment planning is a complex process with potential for error. We hypothesized that shorter time from simulation to treatment would result in rushed work and higher incidence of errors. We examined treatment planning factors predictive for near-miss events.Treatments delivered from March 2012 through October 2014 were analyzed. Near-miss events were prospectively recorded and coded for severity on a 0 to 4 scale; only grade 3-4 (potentially severe/critical) events were studied in this report. For 4 treatment types (3-dimensional conformal, intensity modulated radiation therapy, stereotactic body radiation therapy [SBRT], neutron), logistic regression was performed to test influence of treatment planning time and clinical variables on near-miss events.There were 2257 treatment courses during the study period, with 322 grade 3-4 near-miss events. SBRT treatments had more frequent events than the other 3 treatment types (18% vs 11%, P = .04). For the 3-dimensional conformal group (1354 treatments), univariate analysis showed several factors predictive of near-miss events: longer time from simulation to first treatment (P = .01), treatment of primary site versus metastasis (P < .001), longer treatment course (P < .001), and pediatric versus adult patient (P = .002). However, on multivariate regression only pediatric versus adult patient remained predictive of events (P = 0.02). For the intensity modulated radiation therapy, SBRT, and neutron groups, time between simulation and first treatment was not found to be predictive of near-miss events on univariate or multivariate regression.When controlling for treatment technique and other clinical factors, there was no relationship between time spent in radiation treatment planning and near-miss events. SBRT and pediatric treatments were more error-prone, indicating that clinical and technical complexity of treatments should be taken into account when targeting safety interventions.
View details for DOI 10.1016/j.prro.2015.10.017
View details for PubMedID 26725961
Mentorship Programs in Radiation Oncology Residency Training Programs: A Critical Unmet Need
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
2016; 94 (1): 27-30
To conduct a nationwide survey to evaluate the current status of resident mentorship in radiation oncology.An anonymous electronic questionnaire was sent to all residents and recent graduates at US Accreditation Council for Graduate Medical Education-accredited radiation oncology residency programs, identified in the member directory of the Association of Residents in Radiation Oncology. Factors predictive of having a mentor and satisfaction with the mentorship experience were identified using univariate and multivariate analyses.The survey response rate was 25%, with 85% of respondents reporting that mentorship plays a critical role in residency training, whereas only 53% had a current mentor. Larger programs (≥ 10 faculty, P=.004; and ≥ 10 residents, P<.001) were more likely to offer a formal mentorship program, which makes it more likely for residents to have an active mentor (88% vs 44%). Residents in a formal mentoring program reported being more satisfied with the overall mentorship experience (univariate odds ratio 8.77, P<.001; multivariate odds ratio 5, P<.001). On multivariate analysis, women were less likely to be satisfied with the mentorship experience.This is the first survey focusing on the status of residency mentorship in radiation oncology. Our survey highlights the unmet need for mentorship in residency programs.
View details for DOI 10.1016/j.ijrobp.2015.09.021
View details for Web of Science ID 000366574700019
View details for PubMedID 26700700
Simple tool for prediction of parotid gland sparing in intensity-Modulated radiation therapy
2015; 40 (3): 232-234
Sparing one or both parotid glands is a key goal when planning head and neck cancer radiation treatment. If the planning target volume (PTV) overlaps one or both parotid glands substantially, it may not be possible to achieve adequate gland sparing. This finding results in physicians revising their PTV contours after an intensity-modulated radiation therapy (IMRT) plan has been run and reduces workflow efficiency. We devised a simple formula for predicting mean parotid gland dose from the overlap of the parotid gland and isotropically expanded PTV contours. We tested the tool using 44 patients from 2 institutions and found agreement between predicted and actual parotid gland doses (mean absolute error = 5.3Gy). This simple method could increase treatment planning efficiency by improving the chance that the first plan presented to the physician will have optimal parotid gland sparing.
View details for DOI 10.1016/j.meddos.2015.01.002
View details for Web of Science ID 000359449400012
View details for PubMedID 25704638
Trimodality Treatment of Malignant Pleural Mesothelioma: An Institutional Review.
American journal of clinical oncology
Malignant pleural mesothelioma (MPM) is a deadly disease with varying treatment options. This study retrospectively describes treatment practices at the University of Washington Medical System from 1980 to 2011, and evaluates the impact of trimodality therapy and radiation (photon and neutron) on survival.A retrospective study was conducted on patients treated for MPM. Univariate and multivariate methods were utilized to evaluate potential factors associated with survival. Treatments received and baseline characteristics were included. Survival analysis of trimodality therapy was performed using a propensity score method to control for baseline characteristics.Among 78 eligible patients, the median age at diagnosis was 59 years and the median survival was 13.7 months. On multivariate analysis, the significant predictors of improved survival were age, smoking history, location, and receipt of radiation therapy or chemotherapy. In the 48 patients receiving radiation therapy, the difference in survival between neutron therapy and non-neutron therapy patients was not statistically significant: hazard ratio, 1.20 (95% confidence interval, 0.68-2.13), P=0.52. Patients receiving trimodality therapy were more likely to have early-stage disease (60% vs. 30%) and epithelioid histology (86% vs. 58%). In a propensity score-weighted Cox proportional hazards model, trimodality therapy patients had improved overall survival, hazard ratio 0.45, P=0.004, median 14.6 versus 8.6 months.Trimodality therapy was significantly associated with prolonged survival in patients with MPM, even when adjusting for baseline patient factors. Radiation therapy was associated with improved survival, but the modality of radiation therapy used was not associated with outcome.
View details for PubMedID 26353120
Assessing the scale of tumor heterogeneity by complete hierarchical segmentation of MRI
PHYSICS IN MEDICINE AND BIOLOGY
2015; 60 (3): 977-993
In many cancers, intratumoral heterogeneity has been found in histology, genetic variation and vascular structure. We developed an algorithm to interrogate different scales of heterogeneity using clinical imaging. We hypothesize that heterogeneity of perfusion at coarse scale may correlate with treatment resistance and propensity for disease recurrence. The algorithm recursively segments the tumor image into increasingly smaller regions. Each dividing line is chosen so as to maximize signal intensity difference between the two regions. This process continues until the tumor has been divided into single voxels, resulting in segments at multiple scales. For each scale, heterogeneity is measured by comparing each segmented region to the adjacent region and calculating the difference in signal intensity histograms. Using digital phantom images, we showed that the algorithm is robust to image artifacts and various tumor shapes. We then measured the primary tumor scales of contrast enhancement heterogeneity in MRI of 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival. Coarser scale of maximum signal intensity heterogeneity was prognostic of shorter survival (p = 0.05). By contrast, two fractal parameters and three Haralick texture features were not prognostic. In summary, our algorithm produces a biologically motivated segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. If validated on a larger dataset, this prognostic imaging biomarker could be useful to identify patients at higher risk for recurrence and candidates for alternative treatment.
View details for DOI 10.1088/0031-9155/60/3/977
View details for Web of Science ID 000349438400009
View details for PubMedID 25575341
The use of stereotactic radiosurgery for brain metastases from breast cancer: Who benefits most?
BREAST CANCER RESEARCH AND TREATMENT
2015; 149 (3): 743-749
Brain metastases (BM) from primary breast cancer can arise despite use of systemic therapies that provide excellent extracranial disease control. Local modalities for treating BM include surgery, whole brain radiation therapy (WBRT), and stereotactic radiosurgery (SRS). We sought to determine the benefits of SRS for management of BM arising from different biologic breast cancer subtypes. We reviewed records of 131 patients who received SRS for breast cancer BM between 2001 and 2013. Survival was estimated by the Kaplan-Meier method. Effects of tumor biology, number and location of lesions, and number of SRS sessions on survival were evaluated by Cox proportional hazards regression. Of the 122 patients with subtypes available, 41 patients (31%) were classified as estrogen receptor positive/HER2 negative (ER(+)HER2(-)); 30 patients (23%), ER(+)HER2(+); 23 patients (18%), ER(-)HER2(+); and 28 patients (21%), ER(-)HER2(-) (or triple negative breast cancer, TNBC). Median age at first SRS was 50 years. Median overall survival for ER(+)HER2(-), ER(+)HER2(+), ER(-)HER2(+), and TNBC was 16, 26, 23, and 7 months, respectively (p < 0.001 for difference between groups). Patients with TNBC had the shortest time to retreatment with WBRT or SRS or death with hazard ratio of 3.12 (p < 0.001) compared to ER(+)HER2(-). In all subtypes other than TNBC, SRS can provide meaningful control of BM even in the setting of multiple lesions and may be worth repeating for new lesions that develop metachronously. For patients with TNBC, prognosis is guarded following SRS, and there is an urgent need to develop more effective treatment strategies.
View details for DOI 10.1007/s10549-014-3242-x
View details for Web of Science ID 000349761200016
View details for PubMedID 25638395
View details for PubMedCentralID PMC4494730
Submandibular gland-sparing radiation therapy for locally advanced oropharyngeal squamous cell carcinoma: patterns of failure and xerostomia outcomes
Saliva from submandibular glands (SMG) is necessary to minimize xerostomia. It is unclear whether SMG can be safely spared in patients undergoing bilateral neck radiotherapy for locally advanced oropharyngeal cancer without increasing the risk of marginal recurrence. We evaluated the outcomes of contralateral submandibular gland (cSMG) sparing intensity-modulated radiation therapy (IMRT).All patients with stage III/IV oropharyngeal squamous cell carcinoma treated with bilateral neck IMRT from 2006-2012 at our institution were included. Appropriately selected patients with favorable primary tumor characteristics and no definite contralateral neck disease were treated with cSMG-sparing IMRT. Patterns of failure and xerostomia outcomes were retrospectively analyzed.114 patients were treated. 89% had stage IV disease and 89% received definitive radiation therapy. 76 patients (67%) received cSMG sparing IMRT. With a median follow-up of 30 months, there were 10 local, 9 regional, and 10 distant recurrences. 2-year overall survival was 86% and 2-year loco-regional control was 87%. In cSMG spared patients, the mean cSMG dose was 30.7 Gy. Late grade 2+ xerostomia was significantly reduced in the cSMG spared group compared to those without SMG sparing (6 months: 23% vs. 72%, 12 months: 6% vs. 41%, 24 months: 3% vs. 36%, all p < 0.0007). There were no peri-SMG marginal recurrences in the cSMG-spared cohort.cSMG sparing IMRT did not increase marginal failures in this series of locally advanced oropharyngeal SCC patients. Xerostomia was significantly reduced in cSMG spared patients.
View details for DOI 10.1186/s13014-014-0255-x
View details for Web of Science ID 000349258200001
View details for PubMedID 25424729
View details for PubMedCentralID PMC4262974
- Differentiation of overall survival by breast cancer tumor subtype following stereotactic radiosurgery for brain metastasis. AMER SOC CLINICAL ONCOLOGY. 2014
Neutron Radiotherapy for Adenoid Cystic Carcinoma of the Lacrimal Gland
OPHTHALMIC PLASTIC AND RECONSTRUCTIVE SURGERY
2013; 29 (4): 256-260
Lacrimal gland adenoid cystic carcinomas are rare, aggressive orbital tumors that share histopathologic similarities with salivary gland malignancies. Neutron radiotherapy may be useful for treatment due to its high biological effectiveness for salivary malignancies.The authors retrospectively reviewed the outcomes for 11 lacrimal gland adenoid cystic carcinoma patients treated with neutrons from 1988 to 2011. Most had undergone surgery prior to radiation therapy. However, gross residual disease was present in 8 patients. The most common American Joint Committee on Cancer stage was T4cN0M0. Four patients with skull base involvement received a radiosurgery boost and 1 received a proton therapy boost.Median follow up was 6.2 years. Median overall survival was 11.1 years and median disease-free survival was 6.3 years. Five-year local control was estimated by the Kaplan-Meier method as 80%. Three patients had a local recurrence; 4 developed distant metastases. Six patients died. Seven patients had intact vision in the affected eye before neutron radiation. Two required enucleation for a painful dry eye. Of the 5 who avoided an enucleation, 3 had either severe visual impairment (20/400) or only light perception and 2 were without known vision compromise or complications at the time of their death. One patient developed asymptomatic frontal lobe radionecrosis after 2 courses of radiation therapy.Neutron radiation therapy achieved excellent 5-year local control in this series of high-risk patients, with most cases having gross residual disease. Late recurrences and distant metastases remain a challenge. Meaningful ipsilateral vision preservation was not possible in most cases in the long term, although only 2 patients required an enucleation for treatment effects.
View details for DOI 10.1097/IOP.0b013e318295f99b
View details for Web of Science ID 000321698400012
View details for PubMedID 23839633
Challenges and opportunities in patient-specific, motion-managed and PET/CT-guided radiation therapy of lung cancer: review and perspective.
Clinical and translational medicine
2012; 1 (1): 18-?
The increasing interest in combined positron emission tomography (PET) and computed tomography (CT) to guide lung cancer radiation therapy planning has been well documented. Motion management strategies during treatment simulation PET/CT imaging and treatment delivery have been proposed to improve the precision and accuracy of radiotherapy. In light of these research advances, why has translation of motion-managed PET/CT to clinical radiotherapy been slow and infrequent? Solutions to this problem are as complex as they are numerous, driven by large inter-patient variability in tumor motion trajectories across a highly heterogeneous population. Such variation dictates a comprehensive and patient-specific incorporation of motion management strategies into PET/CT-guided radiotherapy rather than a one-size-fits-all tactic. This review summarizes challenges and opportunities for clinical translation of advances in PET/CT-guided radiotherapy, as well as in respiratory motion-managed radiotherapy of lung cancer. These two concepts are then integrated into proposed patient-specific workflows that span classification schemes, PET/CT image formation, treatment planning, and adaptive image-guided radiotherapy delivery techniques.
View details for DOI 10.1186/2001-1326-1-18
View details for PubMedID 23369522
View details for PubMedCentralID PMC3560984
IN VIVO PROTON BEAM RANGE VERIFICATION USING SPINE MRI CHANGES
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
2010; 78 (1): 268-275
In proton therapy, uncertainty in the location of the distal dose edge can lead to cautious treatment plans that reduce the dosimetric advantage of protons. After radiation exposure, vertebral bone marrow undergoes fatty replacement that is visible on magnetic resonance imaging (MRI). This presents an exciting opportunity to observe radiation dose distribution in vivo. We used quantitative spine MRI changes to precisely detect the distal dose edge in proton radiation patients.We registered follow-up T1-weighted MRI images to planning computed tomography scans from 10 patients who received proton spine irradiation. A radiation dose-MRI signal intensity curve was created using the lateral beam penumbra in the sacrum. This curve was then used to measure range errors in the lumbar spine.In the lateral penumbra, there was an increase in signal intensity with higher dose throughout the full range of 0-37.5 Gy (RBE). In the distal fall-off region, the beam sometimes appeared to penetrate farther than planned. The mean overshoot in 10 patients was 1.9 mm (95% confidence interval, 0.8-3.1 mm), on the order of the uncertainties inherent to our range verification method.We have demonstrated in vivo proton range verification using posttreatment spine MRI changes. Our analysis suggests the presence of a systematic overshoot of a few millimeters in some proton spine treatments, but the range error does not exceed the uncertainty incorporated into the treatment planning margin. It may be possible to extend our technique to MRI sequences that show early bone marrow changes, enabling adaptive treatment modification.
View details for DOI 10.1016/j.ijrobp.2009.11.060
View details for Web of Science ID 000281304600037
View details for PubMedID 20472369
Administration of oxaliplatin to a pregnant woman with rectal cancer
CANCER CHEMOTHERAPY AND PHARMACOLOGY
2009; 63 (2): 371-373
The platinum agent oxaliplatin could be useful in treatment of cancer in pregnant women, but it is fetotoxic in rats and its effect on the human fetus is unknown.Oxaliplatin was administered to a 25-year-old pregnant woman with metastatic rectal cancer from 20 to 30 weeks gestational age as part of the mFOLFOX-6 regimen.The patient gave birth to a healthy girl at 33 weeks gestational age. At follow-up, the 3-year-old child had achieved all appropriate growth and developmental milestones.Oxaliplatin is a component of several modern chemotherapy regimens. This report demonstrates the administration of oxaliplatin in the second and third trimesters of pregnancy without apparent fetal harm.
View details for DOI 10.1007/s00280-008-0731-9
View details for Web of Science ID 000261286800020
View details for PubMedID 18357450
Chalcone isomerase family and fold: No longer unique to plants
2004; 13 (2): 540-544
Chalcone isomerase, an enzyme in the isoflavonoid pathway in plants, catalyzes the cyclization of chalcone into (2S)-naringenin. Chalcone isomerase sequence family and three-dimensional fold appeared to be unique to plants and has been proposed as a plant-specific gene marker. Using sensitive methods of sequence comparison and fold recognition, we have identified genes homologous to chalcone isomerase in all completely sequenced fungi, in slime molds, and in many gammaproteobacteria. The residues directly involved in the enzyme's catalytic function are among the best conserved across species, indicating that the newly discovered homologs are enzymatically active. At the same time, fungal and bacterial species that have chalcone isomerase-like genes tend to lack the orthologs of the upstream enzyme chalcone synthase, suggesting a novel variation of the pathway in these species.
View details for DOI 10.1110/ps.03395404
View details for Web of Science ID 000188411000024
View details for PubMedID 14718655