Differentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach.

Journal: The oncologist
PMID: 39963829

Abstract

BACKGROUND: The objective of this study was to develop and validate a radiomics-based machine learning (ML) model to differentiate between renal medullary carcinoma (RMC) and clear cell renal carcinoma (ccRCC).

Authors

  • Rahim Jiwani
    Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Koustav Pal
    Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Iwan Paolucci
    Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. ipaolucci@mdanderson.org.
  • Bruno Odisio
    Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Kristy Brock
  • Nizar M Tannir
    Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Daniel D Shapiro
    Department of Urology, University of Wisconsin School of Medicine and Public Health, Madison, WI 77030, United States.
  • Pavlos Msaouel
    Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Rahul A Sheth
    Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.

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