Vinay Anant Duddalwar, MD

Research Professor of Radiology (Part-Time)

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Overview

Vinay Duddalwar is a Professor of Radiology, Urology and Biomedical Engineering at the Keck School of Medicine at the University of Southern California. He completed his medical education at Nagpur , radiology residencies at Pune, India and Aberdeen, UK. and a fellowship in abdominal imaging and intervention at the University of British Columbia, Vancouver. He was on the faculty at Grampian University NHS Hospitals, Aberdeen UK and then subsequently moved to USC.
He currently is the Medical Director , Imaging for the Norris Cancer Center at USC. His interests are in the field of abdominal oncologic imaging, especially Genitourinary imaging. His research focus is in the field of radiomics, quantitative and multiomic evaluation of neoplasms. He established and leads the USC Radiomics Lab (radiomicslab.usc.edu) which is an interdisciplinary translational research group interested in the development and use of quantitative methods of evaluating imaging data. His lab is at the intersection of quantifying imaging, artificial intelligence and multiomic analysis. The lab is funded by federal, foundation and industry grants and is currently exploring radio genomics, imaging evaluation of angiogenesis, molecular and immune correlates and biomarkers as well as treatment response in various cancers. This includes developing human allied explainable decision support systems He has extensive collaborations with both clinical and translational researchers. He has funded research currently ongoing in these areas. He is an active member of a number of national and international radiology societies. He is a GU section editor for Clinical Radiology as well as reviewer for a number of radiology and urology journals

Awards

  • USC: Mellon Mentoring Award( Faculty to faculty), 2013
     – 2014
  • Pasadena Magazine: Top Doctor, 2009
     – 2021

Research Keywords

  • Oncologic Imaging, Genitourinary neoplasms, Radiomics, Quantitative Imaging, AI, Multiomics

Publications

  • GUSL: A novel and efficient machine learning model for prostate segmentation on MRI. Comput Biol Med. 2026 Aug 15; 213:111820.. View in PubMed
  • TCIA Radiology Image Processing for AI and Radiomics. medRxiv. 2026 Jun 24.. View in PubMed
  • Comparative Effectiveness and Cost-Effectiveness of an Artificial Intelligence Workflow for Small Renal Mass Diagnosis on Computed Tomography. Urol Pract. 2026 Jun 03; 101097.. View in PubMed
  • Radiogenomic analysis of muscle-invasive bladder cancer using CT-based texture analysis. Bladder Cancer. 2026 Apr-Jun; 12(2):23523735261455396.. View in PubMed
  • Generation of Automated Nephrometry Scores Through Direct Prediction of Each Component. Urology. 2026 May; 211:6-12.. View in PubMed
  • A transparent, lightweight and sustainable Green Learning AI model for prostate cancer detection on MRI. BJU Int. 2026 06; 137(6):1014-1025.. View in PubMed
  • Machine learning based classification of aggressive and malignant renal tumors from multimodal data. PLOS Digit Health. 2026 Feb; 5(2):e0001225.. View in PubMed
  • Multiphase CT-Based Tumor and Peritumoral Radiomics for Characterization of Clear Cell Renal Cell Carcinoma. J Imaging Inform Med. 2026 Jan 05.. View in PubMed
  • Radiomics and Back Pain. Global Spine J. 2026 May; 16(4):1957-1972.. View in PubMed
  • Image Imputation with conditional generative adversarial networks captures clinically relevant imaging features on computed tomography. PLOS Digit Health. 2025 Aug; 4(8):e0000970.. View in PubMed