Academic Appointments

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  • Stereotactic radiosurgery for trigeminal neuralgia secondary to tumor: a single-institution retrospective series. Neurosurgical focus Hall, J. C., Ung, T. H., McCleary, T. L., Chuang, C., Gibbs, I. C., Soltys, S. G., Hayden Gephart, M., Li, G., Pollom, E. L., Chang, S. D., Meola, A. 2022; 53 (5): E3


    Trigeminal neuralgia (TN) secondary to tumor represents a rare and diverse entity, and treatment for secondary TN remains controversial. This report reviews a single institution's experience in treating secondary TN with stereotactic radiosurgery (SRS) and focuses on the durability of pain relief with respect to various treatment targets, i.e., the trigeminal nerve, offending tumor, or both.Between the years 2009 and 2021, 21 patients with TN secondary to benign (n = 13) or malignant (n = 8) tumors underwent SRS. Barrow Neurological Institute (BNI) pain intensity scale scores were collected from patient electronic medical records at baseline, initial follow-up, and 1 and 3 years post-SRS. The interval change in BNI scale score (ΔBNI) at the various follow-up time points was also calculated to assess the durability of pain relief following SRS.The median follow-up period was 24 (range 0.5-155) months. Five patients (24%) received treatment to the trigeminal nerve only, 10 (48%) received treatment to the tumor only, and 6 (29%) had treatment to both the nerve and tumor. The overall radiation dosage ranged from 14 to 60 Gy delivered in 1-5 fractions, with a median overall dose of 26 Gy. The median dose to the tumor was 22.5 (range 14-35) Gy, delivered in 1-5 fractions. Of the treatments targeting the tumor, 25% were delivered in a single fraction with doses ranging from 14 to 20 Gy, 60% were delivered in 3 fractions with doses ranging from 18 to 27 Gy, and 15% were delivered in 5 fractions with doses ranging from 25 to 35 Gy. The most common dose regimen for tumor treatment was 24 Gy in 3 fractions. The median biologically effective dose (with an assumed alpha/beta ratio of 10 [BED10]) for tumor treatments was 43.1 (range 13.3-60.0) Gy. There was a significant difference in the proportion of patients with recurrent pain (ΔBNI score ≥ 0) at the time of last follow-up across the differing SRS treatment targets: trigeminal nerve only, tumor only, or both (p = 0.04). At the time of last follow-up, the median ΔBNI score after SRS to the nerve only was -1, 0 after SRS to tumor only, and -2 after SRS to both targets.SRS offers clinical symptomatic benefit to patients with TN secondary to tumor. For optimal pain relief and response durability, treatment targeting both the tumor and the trigeminal nerve appears to be most advantageous.

    View details for DOI 10.3171/2022.8.FOCUS22381

    View details for PubMedID 36321284

  • Mitigating the uncertainty in small field dosimetry by leveraging machine learning strategies. Physics in medicine and biology Zhao, W., Yang, Y., Xing, L., Chuang, C. F., Schüler, E. 2022


    Small field dosimetry is significantly different from the dosimetry of broad beams due to loss of electron side scatter equilibrium, source occlusion, and effects related to the choice of detector. However, use of small fields is increasing with the increase in indications for intensity-modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT), and thus the need for accurate dosimetry is ever more important. Here we propose to leverage machine learning (ML) strategies to reduce the uncertainties and increase the accuracy in determining small field output factors (OFs). Linac OFs from a Varian TrueBeam STx were calculated either by the treatment planning system (TPS) or measured with a W1 scintillator detector at various multi-leaf collimator (MLC) positions, jaw positions, and with and without contribution from leaf-end transmission. The fields were defined by the MLCs with the jaws at various positions. Field sizes between 5 and 100 mm were evaluated. Separate ML regression models were generated based on the TPS calculated or the measured datasets. Accurate predictions of small field OFs at different field sizes (FSs) were achieved independent of jaw and MLC position. A mean and maximum % relative error (RE) of 0.380.39% and 3.62%, respectively, for the best-performing models based on the measured datasets were found. The prediction accuracy was independent of contribution from leaf-end transmission. Several ML models for predicting small field OFs were generated, validated, and tested. Incorporating these models into the dose calculation workflow could greatly increase the accuracy and robustness of dose calculations for any radiotherapy delivery technique that relies heavily on small fields.

    View details for DOI 10.1088/1361-6560/ac7fd6

    View details for PubMedID 35803256

  • Implicit neural representation for radiation therapy dose distribution. Physics in medicine and biology Vasudevan, V., Shen, L., Huang, C., Chuang, C. F., Islam, M. T., Ren, H., Yang, Y., Dong, P., Xing, L. 2022


    OBJECTIVE: Dose distribution data plays a pivotal role in radiotherapy treatment planning. The data is typically represented using voxel grids, and its size ranges from 10^6--10^8. A concise representation of the treatment plan is of great value in facilitating treatment planning and downstream applications. This work aims to develop an implicit neural representation of 3D dose distribution data.APPROACH: Instead of storing the dose values at each voxel, in the proposed approach, the weights of a multilayer perceptron (MLP) are employed to characterize the dosimetric data for plan representation and subsequent applications. We train a coordinate-based MLP with sinusoidal activations to map the voxel spatial coordinates to the corresponding dose values. We identify the best architecture for a given parameter budget and use that to train a model for each patient. The trained MLP is evaluated at each voxel location to reconstruct the dose distribution. We perform extensive experiments on dose distributions of prostate, spine, and head and neck tumor cases to evaluate the quality of the proposed representation. We also study the change in representation quality by varying model size and activation function.MAIN RESULTS: Using coordinate-based MLPs with sinusoidal activations, we can learn implicit representations that achieve a mean-squared error of 10^{-6} and peak signal-to-noise ratio greater than 50 dB at a target bitrate of ~1 across all the datasets, with a compression ratio of ~32. Our results also show that model sizes with a bitrate of 1--2 achieve optimal accuracy. For smaller bitrates, performance starts to drop significantly.SIGNIFICANCE: The proposed model provides a low-dimensional, implicit, and continuous representation of 3D dose data. In summary, given a dose distribution, we systematically show how to find a compact model to fit the data accurately. This study lays the groundwork for future applications of neural representations of dose data in radiation oncology.

    View details for DOI 10.1088/1361-6560/ac6b10

    View details for PubMedID 35477171

  • Small field measurement and monte carlo model validation of a novel image-guided radiotherapy system. Medical physics Shi, M., Chuang, C. F., Kovalchuk, N., Bush, K. K., Zaks, D., Xing, L., Surucu, M., Han, B. 2021


    PURPOSE: The RefleXionTM X1 is a novel radiotherapy system that is designed for image-guided radiotherapy and, eventually, biology-guided radiotherapy (BgRT). BgRT is a treatment paradigm that tracks tumor motion using real-time positron emission signals. This study reports the small field measurement results and the validation of a Monte Carlo (MC) model of the first clinical RefleXion unit.METHODS: The RefleXion linear accelerator (linac) produces a 6 MV flattening filter free (FFF) photon beam and consists of a binary multi-leaf collimator (MLC) system with 64 leaves and two pairs of y-jaws. The maximum clinical field size achievable is 400 * 20 mm2 . The y-jaws provide either a 10 mm or 20 mm opening at source-to-axis distance (SAD) of 850 mm. The width of each MLC leaf at SAD is 6.25 mm. Percentage depth doses (PDDs) and relative beam profiles were acquired using an Edge diode detector in a water tank for field sizes from 12.5 * 10 mm2 to 100 * 20 mm2 . Beam profiles were also measured using films. Output factors of fields ranging from 6.25 * 10 mm2 to 100 * 20 mm2 were measured using W2 scintillator detector, Edge detector, and films. Output correction factors k of the Edge detector for RefleXion were calculated. A MC model of the linac including pre-MLC beam sources and detailed structures of MLC and lower y-jaws was validated against the measurements. Simulation codes BEAMnrc and GATE were utilized.RESULTS: The diode measured PDD at 10 cm depth (PDD10) increases from 53.6% to 56.9% as the field opens from 12.5 * 10 mm2 to 100 * 20 mm2 . The W2-measured output factor increases from 0.706 to 1 as the field opens from 6.25 * 10 mm2 to 100 * 20 mm2 (reference field size). The output factors acquired by diode and film differ from the W2 results by 1.65% (std = 1.49%) and 2.09% (std = 1.41%) on average, respectively. The profile penumbra and full width half maximum (FWHM) measured by diode agree well with the film results with a deviation of 0.60 mm and 0.73% on average, respectively. The averaged beam profile consistency calculated between the diode and film measured profiles among different depths is within 1.72%. By taking the W2 measurements as the ground truth, the output correction factors k for Edge detector ranging from 0.958 to 1 were reported. For the MC model validation, the simulated PDD10 agreed within 0.6% to the diode measurement. The MC simulated output factor differed from the W2 results by 2.3% on average (std = 3.7%) while the MC simulated beam penumbra differed from the diode results by 0.67 mm on average (std = 0.42 mm). The MC FWHM agreed with the diode results to within 1.40% on average. The averaged beam profile consistency calculated between the diode and MC profiles among different depths is less than 1.29%.CONCLUSIONS: This study represents the first small field dosimetry of a clinical RefleXion system. A complete and accurate MC model of the RefleXion linac has been validated. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15273

    View details for PubMedID 34628666

  • Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy. Medical physics Jia, M., Wu, Y., Yang, Y., Wang, L., Chuang, C., Han, B., Xing, L. 2021


    PURPOSE: The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID).METHODS: A deep learning-based signal processing strategy, referred to as 3DosiNet, is utilized to learn a mapping from an EPID image to planar dose distributions at given depths. The network was trained with the volumetric dose exported from the clinical treatment planning system (TPS). Given the latent inconsistency between measurements and corresponding TPS calculations, unsupervised learning is formulated in 3DosiNet to capture abstractive image features that are less sensitive to the potential variations.RESULTS: Validation experiments were performed using five regular fields and three clinical IMRT cases. The measured dose profiles and percentage depth dose (PDD) curves were compared with those measured using standard tools in terms of the 1D gamma index. The mean gamma pass rates (2%/2mm) over the regular fields are 100% and 97.3% for the dose profile and PDD measurements, respectively. The measured volumetric dose was compared to corresponding TPS calculation in terms of the 3D gamma index. The mean 2% / 2mm gamma pass rates are 97.9% for square fields and 94.9% for the IMRT fields.CONCLUSIONS: The system promises to be a practical 3D dosimetric tool for pre-treatment patient-specific quality assurance and further developed for in-treatment patient dose monitoring. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15218

    View details for PubMedID 34519365

  • Medical Physics Practice Guideline (MPPG) 11.a: Plan and chart review in external beam radiotherapy and brachytherapy. Journal of applied clinical medical physics Xia, P., Sintay, B. J., Colussi, V. C., Chuang, C., Lo, Y., Schofield, D., Wells, M., Zhou, S. 2021


    A therapeutic medical physicist is responsible for reviewing radiation therapy treatment plans and patient charts, including initial treatment plans and new chart review, on treatment chart (weekly) review, and end of treatment chart review for both external beam radiation and brachytherapy. Task group report TG 275 examined this topic using a risk-based approach to provide a thorough analysis and guidance for best practice. Considering differences in resources and workflows of various clinical practice settings, the Professional Council of the American Association of Physicists in Medicine assembled this task group to develop a practice guideline on the same topic to provide a minimum standard that balances an appropriate level of safety and resource utilization. This medical physics practice guidelines (MPPG) thus provides a concise set of recommendations for medical physicists and other clinical staff regarding the review of treatment plans and patient charts while providing specific recommendations about who to be involved, and when/what to check in the chart review process. The recommendations, particularly those related to the initial plan review process, are critical for preventing errors and ensuring smooth clinical workflow. We believe that an effective review process for high-risk items should include multiple layers with collective efforts across the department. Therefore, in this report, we make specific recommendations for various roles beyond medical physicists. The recommendations of this MPPG have been reviewed and endorsed by the American Society of Radiologic Technologists and the American Association of Medical Dosimetrists.

    View details for DOI 10.1002/acm2.13366

    View details for PubMedID 34342124

  • The Stanford stereotactic radiosurgery experience on 7000 patients over 2 decades (1999-2018): looking far beyond the scalpel. Journal of neurosurgery Fatima, N., Meola, A., Ding, V. Y., Pollom, E., Soltys, S. G., Chuang, C. F., Shahsavari, N., Hancock, S. L., Gibbs, I. C., Adler, J. R., Chang, S. D. 2021: 1–17


    OBJECTIVE: The CyberKnife (CK) has emerged as an effective frameless and noninvasive method for treating a myriad of neurosurgical conditions. Here, the authors conducted an extensive retrospective analysis and review of the literature to elucidate the trend for CK use in the management paradigm for common neurosurgical diseases at their institution.METHODS: A literature review (January 1990-June 2019) and clinical review (January 1999-December 2018) were performed using, respectively, online research databases and the Stanford Research Repository of patients with intracranial and spinal lesions treated with CK at Stanford. For each disease considered, the coefficient of determination (r2) was estimated as a measure of CK utilization over time. A change in treatment modality was assessed using a t-test, with statistical significance assessed at the 0.05 alpha level.RESULTS: In over 7000 patients treated with CK for various brain and spinal lesions over the past 20 years, a positive linear trend (r2 = 0.80) in the system's use was observed. CK gained prominence in the management of intracranial and spinal arteriovenous malformations (AVMs; r2 = 0.89 and 0.95, respectively); brain and spine metastases (r2 = 0.97 and 0.79, respectively); benign tumors such as meningioma (r2 = 0.85), vestibular schwannoma (r2 = 0.76), and glomus jugulare tumor (r2 = 0.89); glioblastoma (r2 = 0.54); and trigeminal neuralgia (r2 = 0.81). A statistically significant difference in the change in treatment modality to CK was observed in the management of intracranial and spinal AVMs (p < 0.05), and while the treatment of brain and spine metastases, meningioma, and glioblastoma trended toward the use of CK, the change in treatment modality for these lesions was not statistically significant.CONCLUSIONS: Evidence suggests the robust use of CK for treating a wide range of neurological conditions.

    View details for DOI 10.3171/2020.9.JNS201484

    View details for PubMedID 33799297

  • A robotically assisted 3D printed quality assurance lung phantom for Calypso. Physics in medicine and biology Capaldi, D. P., Skinner, L. B., Dubrowski, P. n., Zhang, H. n., Xing, L. n., Chuang, C. F., Loo, B. W., Bush, K. K., Fahimian, B. P., Yu, A. S. 2021


    Purpose:Radiation dose delivered to targets located near the upper-abdomen or in the thorax are significantly affected by respiratory-motion. Relatively large-margins are commonly added to compensate for this motion, limiting radiation-dose-escalation. Internal-surrogates of target motion, such as a radiofrequency (RF) tracking system, i.e. Calypso® System, are used to overcome this challenge and improve normal-tissue sparing. RF tracking systems consist of implanting transponders in the vicinity of the tumor to be tracked using radiofrequency-waves. Unfortunately, although the manufacture provides a universal quality-assurance (QA) phantom, QA-phantoms specifically for lung-applications are limited, warranting the development of alternative solutions to fulfil the tests mandated by AAPM's TG142. Accordingly, our objective was to design and develop a motion-phantom to evaluate Calypso for lung-applications that allows the Calypso® Beacons to move in different directions to better simulate true lung-motion.Methods and Materials:A Calypso lung QA-phantom was designed, and 3D-printed. The design consists of three independent arms where the transponders were attached. A pinpoint-chamber with a buildup-cap was also incorporated. A 4-axis robotic arm was programmed to drive the motion-phantom to mimic breathing. After acquiring a four-dimensional-computed-tomography (4DCT) scan of the motion-phantom, treatment-plans were generated and delivered on a Varian TrueBeam® with Calypso capabilities. Stationary and gated-treatment plans were generated and delivered to determine the dosimetric difference between gated and non-gated treatments. Portal cine-images were acquired to determine the temporal-accuracy of delivery by calculating the difference between the observed versus expected transponders locations with the known speed of the transponders' motion.Results:Dosimetric accuracy is better than TG142 tolerance of 2%. Temporal accuracy is greater than, TG142 tolerance of 100ms for beam-on, but less than 100ms for beam-hold.Conclusions:The robotic QA-phantom designed and developed in this study provides an independent phantom for performing Calypso lung-QA for commissioning and acceptance testing of Calypso for lung treatments.

    View details for DOI 10.1088/1361-6560/abebaa

    View details for PubMedID 33657537

  • ZAP-X: A Novel Radiosurgical Device for the Treatment of Trigeminal Neuralgia CUREUS Romanelli, P., Chuang, C., Meola, A., Bodduluri, R. M., Adler, J. R. 2020; 12 (5)
  • Clinical impact of the VOLO optimizer on treatment plan quality and clinical treatment efficiency for CyberKnife. Journal of applied clinical medical physics Schuler, E., Lo, A., Chuang, C. F., Soltys, S. G., Pollom, E. L., Wang, L. 2020


    With the recent CyberKnife treatment planning system (TPS) upgrade from Precision 1.0 to Precision 2.0, the new VOLO optimizer was released for plan optimization. The VOLO optimizer sought to overcome some of the limitations seen with the Sequential optimizer from previous TPS versions. The purpose of this study was to investigate the clinical impact of the VOLO optimizer on treatment plan quality and clinical treatment efficiency as compared to the Sequential optimizer. Treatment plan quality was evaluated in four categories of patients: Brain Simple (BS), Brain Complex (BC), Spine Complex (SC), and Prostate (PC). A total of 60 treatment plans were compared using both the Sequential and VOLO optimizers with Iris and MLC collimation with the same clinical constraints. Metrics evaluated included estimated treatment time, monitor units (MUs) delivered, conformity index (CI), and gradient index (GI). Furthermore, the clinical impact of the VOLO optimizer was evaluated through statistical analysis of the patient population treated during the 4months before (n=297) and 4months after (n=285) VOLO introduction. Significant MU and time reductions were observed for all four categories planned. MU reduction ranged from -14% (BS Iris) to -52% (BC MLC), and time reduction ranged from -11% (BS Iris) to -22% (BC MLC). The statistical analysis of patient population before and after VOLO introduction for patients using 6D Skull tracking with fixed cone, 6D Skull tracking with Iris, and Xsight Spine tracking with Iris were -4.6%, -22.2%, and -17.8% for treatment time reduction, -1.1%, -22.0%, and -28.4% for beam reduction and -3.2%, -21.8%, and -28.1% for MU reduction, respectively. The VOLO optimizer maintains or improves the plan quality while decreases the plan complexity and improves treatment efficiency. We anticipate an increase in patient throughput with the introduction of the VOLO optimizer.

    View details for DOI 10.1002/acm2.12851

    View details for PubMedID 32212374

  • Successful Use of Frameless Stereotactic Radiosurgery for Treatment of Recurrent Brain Metastases in an 18 Month Old Child. The International journal of neuroscience Rahimy, E., Chuang, C., Spunt, S. L., Mahaney, K., Donaldson, S. S., Gibbs, I. C., Soltys, S. G., Pollom, E., Hiniker, S. M. 2019: 1–6


    There are very few reported cases of stereotactic radiosurgery delivered in children under 3 years of age. We report an 18 month old boy with metastatic recurrence of undifferentiated round cell sarcoma to the brain which was treated with chemotherapy, resection, and robotic frameless stereotactic radiosurgery (SRS). Frameless SRS was delivered without technical difficulties, acute adverse events, or clinical sequelae 1.5 months post-radiation. Longer term follow-up will be needed to evaluate local tumor control and effects on neurocognitive development, endocrine function, and growth. This report adds to the literature of the few reported cases of successfully attempted SRS in very young children.

    View details for DOI 10.1080/00207454.2019.1655015

    View details for PubMedID 31401906

  • Erratum: "Report of AAPM TG 135: Quality assurance for robotic radiosurgery". Medical physics Dieterich, S., Cavedon, C., Chuang, C. F., Cohen, A. B., Garrett, J. A., Lee, C. L., Lowenstein, J. R., D'Souza, M. F., Taylor, D. D., Wu, X., Yu, C. 2011; 38 (9): 5264

    View details for DOI 10.1118/1.3626480

    View details for PubMedID 28524974

  • Report of AAPM TG 135: Quality assurance for robotic radiosurgery (vol 38, pg 2914, 2011) MEDICAL PHYSICS Dieterich, S., Cavedon, C., Chuang, C. F., Cohen, A. B., Garrett, J. A., Lee, C. L., Lowenstein, J. R., d'Souza, M. F., Taylor, D. D., Wu, X., Yu, C. 2011; 38 (9): 5264-5264

    View details for DOI 10.1118/1.3626480

    View details for Web of Science ID 000294482900036

  • Report of AAPM TG 135: Quality assurance for robotic radiosurgery MEDICAL PHYSICS Dieterich, S., Cavedon, C., Chuang, C. F., Cohen, A. B., Garrett, J. A., Lee, C. L., Lowenstein, J. R., d'Souza, M. F., Taylor, D. D., Wu, X., Yu, C. 2011; 38 (6): 2914-2936


    The task group (TG) for quality assurance for robotic radiosurgery was formed by the American Association of Physicists in Medicine's Science Council under the direction of the Radiation Therapy Committee and the Quality Assurance (QA) Subcommittee. The task group (TG-135) had three main charges: (1) To make recommendations on a code of practice for Robotic Radiosurgery QA; (2) To make recommendations on quality assurance and dosimetric verification techniques, especially in regard to real-time respiratory motion tracking software; (3) To make recommendations on issues which require further research and development. This report provides a general functional overview of the only clinically implemented robotic radiosurgery device, the CyberKnife. This report includes sections on device components and their individual component QA recommendations, followed by a section on the QA requirements for integrated systems. Examples of checklists for daily, monthly, annual, and upgrade QA are given as guidance for medical physicists. Areas in which QA procedures are still under development are discussed.

    View details for DOI 10.1118/1.3579139

    View details for Web of Science ID 000291405200011

    View details for PubMedID 21815366