Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.

Journal: Scientific reports
Published Date: Feb 7, 2019

Abstract

Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods. This paper presents a systematic and rigorous framework comprised of classification, cross-validation and statistical analyses that was developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment.

Authors

  • Bino Varghese
    Department of Radiology, University of Southern California, Los Angeles, CA, USA. bino.varghese@med.usc.edu.
  • Frank Chen
    Department of Radiology, University of Southern California, Los Angeles, CA, USA.
  • Darryl Hwang
    Department of Radiology, University of Southern California, Los Angeles, CA, USA.
  • Suzanne L Palmer
    Department of Radiology, University of Southern California, Los Angeles, CA, USA.
  • Andre Luis De Castro Abreu
    USC Institute of Urology, Los Angeles, CA, USA.
  • Osamu Ukimura
    USC Institute of Urology, Los Angeles, CA, USA.
  • Monish Aron
    USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: monisharon@hotmail.com.
  • Manju Aron
    Department of Pathology, Keck School of Medicine, University of Southern California, 2011 Zonal Avenue, Los Angeles, CA, 90033, USA.
  • Inderbir Gill
    USC Institute of Urology, Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Vinay Duddalwar
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
  • Gaurav Pandey
    Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: gaurav.pandey@mssm.edu.

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