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Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer. Int J Radiat Oncol Biol Phys 2018 08 01;101(5):1179-1187



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Pubmed Central ID




Scopus ID

2-s2.0-85048748761   7 Citations


PURPOSE: This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization.

METHODS AND MATERIALS: Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium. Slides were co-registered to the T2-weighted MRI scan. A learning curve was generated to determine the number of patients required for a stable machine-learning model. Patients were randomly stratified into 2 training sets and 1 test set. Two partial least-squares regression models were trained, each capable of predicting lumen and epithelium density. Predicted density values were calculated for each patient in the test dataset, mapped into the MRI space, and compared between regions confirmed as high-grade prostate cancer.

RESULTS: The learning-curve analysis showed that a stable fit was achieved with data from 10 patients. Maps indicated that regions of increased epithelium and decreased lumen density, generated from each independent model, corresponded with pathologist-annotated regions of high-grade cancer.

CONCLUSIONS: We present a radio-pathomic approach to mapping prostate cancer. We find that the maps are useful for highlighting high-grade tumors. This technique may be relevant for dose-painting strategies in prostate radiation therapy.

Author List

McGarry SD, Hurrell SL, Iczkowski KA, Hall W, Kaczmarowski AL, Banerjee A, Keuter T, Jacobsohn K, Bukowy JD, Nevalainen MT, Hohenwalter MD, See WA, LaViolette PS


Anjishnu Banerjee PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
William Adrian Hall MD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin
Mark D. Hohenwalter MD Vice Chair, Professor in the Radiology department at Medical College of Wisconsin
Kenneth A. Iczkowski MD Professor in the Pathology department at Medical College of Wisconsin
Peter LaViolette PhD Associate Professor in the Radiology department at Medical College of Wisconsin
Marja T. Nevalainen MD, PhD Assistant Dean, Professor in the Pathology department at Medical College of Wisconsin
William A. See MD Chair, Professor in the Urologic Surgery department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Contrast Media
False Positive Reactions
Image Interpretation, Computer-Assisted
Learning Curve
Least-Squares Analysis
Machine Learning
Magnetic Resonance Imaging
Middle Aged
Neoplasm Staging
Printing, Three-Dimensional
Prospective Studies
Prostate-Specific Antigen
Prostatic Neoplasms
ROC Curve
Regression Analysis
Reproducibility of Results
jenkins-FCD Prod-468 69a93cef3257f26b866d455c1d2b2d0f28382f14