Comparative analysis of machine learning-derived nomogram and biomarkers in predicting side-specific extraprostatic extension: Preliminary findings.

Journal: Clinical imaging
Published Date: Jun 28, 2025

Abstract

AIM: This study aimed to assess and compare the performance of nomograms and machine learning (ML) techniques using preoperative biomarkers for predicting side-specific extraprostatic extension (EPE) in prostate cancer, which is linked to poor outcomes and early recurrence. Accurate preoperative prediction can guide clinical decisions and improve treatment.

Authors

  • Ruchika Reddy Chimmula
    Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University, West Lafayette, IN, United States of America.
  • Mark Green
    Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States of America.
  • Mark Tann
    Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States of America.
  • Michael Koch
    Veterinary Specialists and Emergency Services, 825 White Spruce Blvd, Rochester, NY 14623, United States. Electronic address: kochm@att.net.
  • Ronald Boris
    Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Katrina Collins
    Department of Pathology, Indiana University School of Medicine, Indianapolis, IN, United States of America.
  • Clint Bahler
    Department of Urology, Indiana University School of Medicine, Indianapolis, IN, United States of America.
  • Oluwaseyi Oderinde
    Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Lab, School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN, 47907, USA. ooderind@purdue.edu.

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