Faculty Collaboration Database

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

Date

06/18/2018

Pubmed ID

29908785

Pubmed Central ID

PMC6190585

DOI

10.1016/j.ijrobp.2018.04.044

Scopus ID

2-s2.0-85048748761 (requires institutional sign-in at Scopus site)   41 Citations

Abstract

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

Authors

Anjishnu Banerjee PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
William Adrian Hall MD Professor in the Radiation Oncology department at Medical College of Wisconsin
Mark D. Hohenwalter MD Associate Dean, Executive Director, Professor in the Radiology department at Medical College of Wisconsin
Peter LaViolette PhD Professor in the Radiology department at Medical College of Wisconsin

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

Aged
Contrast Media
Epithelium
False Positive Reactions
Humans
Image Interpretation, Computer-Assisted
Learning Curve
Least-Squares Analysis
Machine Learning
Magnetic Resonance Imaging
Male
Middle Aged
Neoplasm Staging
Printing, Three-Dimensional
Prospective Studies
Prostate
Prostate-Specific Antigen
Prostatectomy
Prostatic Neoplasms
ROC Curve
Radiotherapy
Regression Analysis
Reproducibility of Results