Bio

Clinical Focus


  • Radiology

Academic Appointments


  • Clinical Assistant Professor, Radiology

Professional Education


  • Residency: Kaiser Permanente Los Angeles Radiology Residency (2015) CA
  • Internship: Kaiser Permanente Los Angeles Internal Medicine Residency (2011) CA
  • Board Certification: American Board of Radiology, Radiology (2016)
  • Medical Education: Case Western Reserve School of Medicine (2010) OH
  • Fellowship: Duke University Hospital (2016) NC

Publications

All Publications


  • Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans. JCO clinical cancer informatics Shah, R. P., Selby, H. M., Mukherjee, P., Verma, S., Xie, P., Xu, Q., Das, M., Malik, S., Gevaert, O., Napel, S. 2021; 5: 746-757

    Abstract

    PURPOSE: Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size.MATERIALS AND METHODS: Computed tomography scans from a single institution were selected and resampled to 1 * 1 * 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance.RESULTS: A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B.CONCLUSION: A machine learning radiomics model may help differentiate SCLC from other lung lesions.

    View details for DOI 10.1200/CCI.21.00021

    View details for PubMedID 34264747

  • ACR Appropriateness Criteria Infective Endocarditis. Journal of the American College of Radiology : JACR Expert Panel on Cardiac Imaging, Malik, S. B., Hsu, J. Y., Hurwitz Koweek, L. M., Ghoshhajra, B. B., Beache, G. M., Brown, R. K., Davis, A. M., Johri, A. M., Kligerman, S. J., Litmanovich, D., Mace, S. E., Maroules, C. D., Meyersohn, N., Villines, T. C., Wann, S., Weissman, G., Abbara, S. 2021; 18 (5S): S52–S61

    Abstract

    Infective endocarditis can involve a normal, abnormal, or prosthetic cardiac valve. The diagnosis is typically made clinically with persistently positive blood cultures, characteristic signs and symptoms, and echocardiographic evidence of valvular vegetations or valvular complications such as abscess, dehiscence, or new regurgitation. Imaging plays an important role in the initial diagnosis of infective endocarditis, identifying complications, prognostication, and informing the next steps in therapy. This document outlines the initial imaging appropriateness of a patient with suspected infective endocarditis and for additional imaging in a patient with known or suspected infective endocarditis. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

    View details for DOI 10.1016/j.jacr.2021.01.010

    View details for PubMedID 33958118

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