Abdominal Imaging Fellow, Department of Radiology
Clinical Scholar, Radiology
Honors & Awards
Chief Resident, University of Colorado Diagnostic Radiology Residency Program (July 2021 - June 2022)
Roentgen Resident Research Award, Radiological Society of North America (RSNA) (2022)
Salzman Fund Scholarship Grant, Denver Health Department of Radiology (2022)
Roentgen Resident Research Award, Radiological Society of North American (RSNA) (2021)
Roentgen Resident Research Award, Radiological Society of North America (RSNA) (2020)
Salzman Fund Scholarship Grant, Denver Health Department of Radiology (2020)
Introduction to Academic Radiology (ITAR) Grant, RSNA / AUR / ARRS (2019)
RSNA Research Medical Student Grant, Radiological Society of North America (2016)
SIR Foundation Dr. and Mrs. W.C. Culp Student Research Grant, Society of Interventional Radiology (SIR) (2016)
UCI AGS Research Travel Grant, University of California Irvine Associated Graduate Students (2016)
Residency: University of Colorado Radiology Residency (2022) CO
Internship: Saint Joseph Hospital Internal Medicine Residency (2018) CO
Medical Education: University of California at Irvine School of Medicine Registrar (2016) CA
BA, Claremont McKenna College (2011)
Impact of easing COVID-19 safety measures on trauma computed tomography imaging volumes.
The coronavirus disease 2019 (COVID-19) pandemic has led to substantial disruptions in healthcare staffing and operations. Stay-at-home (SAH) orders and limitations in social gathering implemented in spring 2020 were followed by initial decreases in healthcare and imaging utilization. This study aims to evaluate the impact of subsequent easing of SAH on trauma volumes, demand for, and turnaround times for trauma computed tomography (CT) exams, hypothesizing that after initial decreases, trauma volumes have increased as COVID safety measures have been reduced.Patient characteristics, CT imaging volumes, and turnaround time were analyzed for all adult activated emergency department trauma patients requiring CT imaging at a single Level-I trauma center (1/2018-2/2022) located in the sixth most populous county in the USA. Based on COVID safety measures in place in the state of California, three time periods were compared: baseline (PRE, 1/1/2018-3/19/2020), COVID safety measures (COVID, 3/20/2020-1/25/2021), and POST (1/26/2021-2/28/2022).There were 16,984 trauma patients across the study (PRE = 8289, COVID = 3139, POST = 5556). The average daily trauma patient volumes increased significantly in the POST period compared to the PRE and COVID periods (13.9 vs. 10.3 vs. 10.1, p < 0.001), with increases in both blunt (p < 0.001) and penetrating (p = 0.002) trauma. The average daily number of trauma CT examinations performed increased significantly in the POST period compared to the PRE and COVID periods (56.7 vs. 48.3 vs. 47.6, p < 0.001), with significant increases in average turnaround time (47 min vs. 31 and 37, p < 0.001).After initial decreases in trauma radiology volumes following stay-at-home orders, subsequent easing of safety measures has coincided with increases in trauma imaging volumes above pre-pandemic levels and longer exam turnaround times.
View details for DOI 10.1007/s10140-022-02096-4
View details for PubMedID 36307571
View details for PubMedCentralID PMC9616698
A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MR
AMERICAN JOURNAL OF ROENTGENOLOGY
2021; 216 (1): 111-116
Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI.This retrospective study included patients who underwent prostate MRI and ultrasound-MRI fusion transrectal biopsy between September 2014 and December 2016. Axial T2-weighted images were manually segmented by two abdominal radiologists, which served as ground truth. These manually segmented images were used for training on a customized hybrid 3D-2D U-Net CNN architecture in a fivefold cross-validation paradigm for neural network training and validation. The Dice score, a measure of overlap between manually segmented and automatically derived segmentations, and Pearson linear correlation coefficient of prostate volume were used for statistical evaluation.The CNN was trained on 299 MRI examinations (total number of MR images = 7774) of 287 patients. The customized hybrid 3D-2D U-Net had a mean Dice score of 0.898 (range, 0.890-0.908) and a Pearson correlation coefficient for prostate volume of 0.974.A deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.
View details for DOI 10.2214/AJR.19.22168
View details for Web of Science ID 000600639800029
View details for PubMedID 32812797
PI-RADS Version 2 Is an Excellent Screening Tool for Clinically Significant Prostate Cancer as Designated by the Validated International Society of Urological Pathology Criteria: A Retrospective Analysis
CURRENT PROBLEMS IN DIAGNOSTIC RADIOLOGY
2020; 49 (6): 407-411
To assess the utility of multiparametric MRI in detecting clinically significant prostate cancer (csPCa) by comparing PI-RADSv2 scores with International Society of Urological Pathology (ISUP) pathologic grading criteria.Data from 137 patients were retrospectively analyzed. PI-RADSv2 scores were compared with pathologic grade using ISUP criteria. Pathologic grades were divided into clinically significant (groups 3-5) and clinically insignificant lesions (groups 1-2). Chi-squared analysis was performed for to assess correlation.Sensitivity and specificity of PI-RADSv2 score 3-5 lesions for detecting csPCa was 100% and 18.5%, respectively. Negative predictive value (NPV) is 100% for these lesions. When considering only PI-RADSv2 score 4-5 lesions, sensitivity decreases to 90% and specificity increases to 67.5%, with a NPV of 98.5%. When only PI-RADSv2 score 5 lesions are considered, sensitivity decreases to 50% and specificity increases to 90%, with a NPV of 95%.Multiparametric MRI has excellent sensitivity for detecting csPCa. Specificity is poor for PI-RADSv2 score 3 lesions but improves significantly for PI-RADSv2 score 4 and 5 lesions. Overall, mpMRI is an excellent screening tool for csPCa, as designated by the recently validated ISUP criteria.Multiple limitations of the longstanding Gleason pathologic scoring system have led to the development of new ISUP pathologic criteria, which is more focused on the clinical significance of lesions. There are currently insufficient studies evaluating and validating the ISUP criteria with PIRADS v2 evaluation of the prostate.
View details for DOI 10.1067/j.cpradiol.2019.06.010
View details for Web of Science ID 000577959600011
View details for PubMedID 31350101
Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic
2020; 27 (6): 781-784
The coronavirus disease 2019 (COVID-19) pandemic has led to significant disruptions in the healthcare system including surges of infected patients exceeding local capacity, closures of primary care offices, and delays of non-emergent medical care. Government-initiated measures to decrease healthcare utilization (i.e., "flattening the curve") have included shelter-in-place mandates and social distancing, which have taken effect across most of the USA. We evaluate the immediate impact of the Public Health Messaging and shelter-in-place mandates on Emergency Department (ED) demand for radiology services.We analyzed ED radiology volumes from the five University of California health systems during a 2-week time period following the shelter-in-place mandate and compared those volumes with March 2019 and early April 2019 volumes.ED radiology volumes declined from the 2019 baseline by 32 to 40% (p < 0.001) across the five health systems with a total decrease in volumes across all 5 systems by 35% (p < 0.001). Stratifying by subspecialty, the smallest declines were seen in non-trauma thoracic imaging, which decreased 18% (p value < 0.001), while all other non-trauma studies decreased by 48% (p < 0.001).Total ED radiology demand may be a marker for public adherence to shelter-in-place mandates, though ED chest radiology demand may increase with an increase in COVID-19 cases.
View details for DOI 10.1007/s10140-020-01797-y
View details for Web of Science ID 000538237500001
View details for PubMedID 32504280
View details for PubMedCentralID PMC7273127
Validation of Prostate Imaging-Reporting and Data System Version 2: A Retrospective Analysis
CURRENT PROBLEMS IN DIAGNOSTIC RADIOLOGY
2018; 47 (6): 404-409
Use of magnetic resonance imaging (MRI)/transrectal ultrasound fusion biopsies to determine the accuracy of multiparametric MRI (mpMRI), using Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2), for detecting clinically significant prostate cancer in the overall gland and specifically the peripheral zone (PZ) and transitional zone (TZ).A retrospective analysis of patients who underwent fusion biopsy identified 137 men with 231 prostate lesions was approved by the Institutional Review Board. Subjects initially classified under PI-RADSv1 criteria were regraded using PI-RADSv2 by a radiologist blinded to PI-RADSv1 score and biopsy results. Spearman correlation, chi-squared, and logistic regression analysis were performed.There was positive correlation between PI-RADSv2 and Gleason scores (P < 0.001). In the PZ, mpMRI demonstrated 100% sensitivity, 100% negative predictive value, and 35.9% positive predictive value, compared to 100%, 100%, and 27.1%, respectively, for TZ lesions. When predicting clinically significant prostate cancer, the PI-RADSv2 area under the curve for TZ lesions was 0.844 (95% CI: 0.753-0.935, P < 0.001) and 0.769 (95% CI: 0.684-0.854, P < 0.001) for PZ lesions. Combining PI-RADSv2 with additional risk factors (body mass index, prostate-specific antigen density, digital rectal examination) improved the area under curve.PI-RADSv2 achieves excellent sensitivity and negative predictive value for both PZ and TZ lesions.
View details for DOI 10.1067/j.cpradiol.2017.10.002
View details for Web of Science ID 000444940500009
View details for PubMedID 29126575
View details for PubMedCentralID PMC6193550
Radiogenomics and IR
JOURNAL OF VASCULAR AND INTERVENTIONAL RADIOLOGY
2018; 29 (5): 706-713
Radiogenomics involves the integration of mineable data from imaging phenotypes with genomic and clinical data to establish predictive models using machine learning. As a noninvasive surrogate for a tumor's in vivo genetic profile, radiogenomics may potentially provide data for patient treatment stratification. Radiogenomics may also supersede the shortcomings associated with genomic research, such as the limited availability of high-quality tissue and restricted sampling of tumoral subpopulations. Interventional radiologists are well suited to circumvent these obstacles through advancements in image-guided tissue biopsies and intraprocedural imaging. Comprehensive understanding of the radiogenomic process is crucial for interventional radiologists to contribute to this evolving field.
View details for DOI 10.1016/j.jvir.2017.11.021
View details for Web of Science ID 000432231900018
View details for PubMedID 29551544
Utility of quantitative apparent diffusion coefficient measurements and normalized apparent diffusion coefficient ratios in the diagnosis of clinically significant peripheral zone prostate cancer
BRITISH JOURNAL OF RADIOLOGY
2018; 91 (1088): 20180091
The aim of this study is to evaluate the utility of quantitative apparent diffusion coefficient (ADC) measurements and normalized ADC ratios in multiparametric MRI for the diagnosis of clinically significant peripheral zone (PZ) prostate cancer particularly among equivocally suspicious prostate lesions.A retrospective analysis of 95 patients with PZ lesions by PI-RADSv2 criteria, and who underwent subsequent MRI-US fusion biopsy, was approved by an institutional review board. Two radiologists independently measured ADC values in regions of interest (ROIs) of PZ lesions and calculated normalized ADC ratio based on ROIs in the bladder lumen. Diagnostic performance was evaluated using ROC. Inter observer variability was assessed using intraclass correlation coefficient (ICC).Mean ADC and normalized ADC ratios for clinically significant and non-clinically significant lesions were 0.763 × 10-3 mm2 s-1, 29.8%; and 1.135 × 10-3 mm2 s-1, 47.2% (p < 0.001), respectively. Area under the ROC curve (AUC) was 0.880 [95% CI (0.816-0.944) and 0.885 (95% CI (0.814-0.955)] for ADC and ADC ratio, respectively. Optimal AUC threshold for ADC was 0.843 × 10-3 mm2 s-1 (Sn 70.5%, Sp 88.2%) and for normalized ADC was 33.1% (Sn 75.0%, Sp 95.7%). intraclass correlation coefficient was high at 0.889.Quantitative ADC measurement in PZ prostate lesions demonstrates excellent diagnostic performance in differentiating clinically significant from non-clinically significant prostate cancer with high inter observer correlation. Advances In knowledge: Quantitative ADC is presented as an additional method to evaluate lesions in mpMRI of the prostate. This technique may be incorporated in new and existing methods to improve detection and discrimination of clinically significant prostate cancer.
View details for DOI 10.1259/bjr.20180091
View details for Web of Science ID 000440322900031
View details for PubMedID 29869921
View details for PubMedCentralID PMC6209473
Comparison of Outcomes in Patients Undergoing Percutaneous Renal Cryoablation With Sedation vs General Anesthesia
2015; 85 (1): 130-134
To compare the efficacy and safety of local anesthesia with conscious sedation (LACS) with general anesthesia (GA) in patients undergoing percutaneous renal cryoablation (PRC) for renal cortical neoplasms.We performed a retrospective review of patients undergoing PRC between 2003 and 2013. Patient demographics, tumor characteristics, and perioperative and postoperative follow-up data were recorded and analyzed. We compared 3 principal outcomes across the GA and LACS groups: anesthesia-related outcomes, treatment failure, and complications.A total of 235 patients with available data were included. Of these, 82 underwent PRC under GA and 153 patients under LACS. The 2 groups were similar with regard to age, gender, body mass index, American Society of Anesthesiologists score, tumor features, preoperative serum creatinine level, and hematocrit value. The GA and LACS groups had a similar percentage of patients with biopsy-proven renal cell carcinoma (68.5% and 64.2%, respectively; P = .62). The mean follow-up time for GA and LACS was 37 and 21 months, respectively (P <.0001). The mean procedure time for GA was significantly longer compared with LACS (133 vs 102 minutes; P <.001), and the mean hospital stay was shorter under LACS (1.08 vs 1.95 days; P <.0001). There was no difference in immediate failure (0% and 1.9%; P = .051) or recurrences (11% and 3.9%, respectively; P = .051) between GA and LACS groups. There was no difference in intraoperative and postoperative treatment-related complications between the 2 groups.PRC for small renal masses under LACS is effective and safe. PRC with LACS has the advantage of decreased procedure time and a shorter hospital stay.
View details for DOI 10.1016/j.urology.2014.09.013
View details for Web of Science ID 000346648500031
View details for PubMedID 25440762