1. Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer
- Author
-
Sean D. McGarry, Kenneth Jacobsohn, Kenneth A. Iczkowski, William A. See, Marja T. Nevalainen, William A. Hall, Peter S. LaViolette, Mark D. Hohenwalter, Anjishnu Banerjee, Amy Kaczmarowski, Sarah Hurrell, Tucker Keuter, and John D. Bukowy
- Subjects
Male ,Cancer Research ,medicine.medical_treatment ,Contrast Media ,Epithelium ,030218 nuclear medicine & medical imaging ,Machine Learning ,Prostate cancer ,0302 clinical medicine ,Prostate ,Medicine ,Prospective Studies ,Radiation ,medicine.diagnostic_test ,Prostatectomy ,Middle Aged ,Magnetic Resonance Imaging ,3. Good health ,medicine.anatomical_structure ,Oncology ,030220 oncology & carcinogenesis ,Printing, Three-Dimensional ,Regression Analysis ,Radiology ,Learning Curve ,medicine.medical_specialty ,Lumen (anatomy) ,Article ,03 medical and health sciences ,Image Interpretation, Computer-Assisted ,Humans ,Radiology, Nuclear Medicine and imaging ,False Positive Reactions ,Least-Squares Analysis ,Multiparametric Magnetic Resonance Imaging ,Aged ,Neoplasm Staging ,Radiotherapy ,business.industry ,Cancer ,Prostatic Neoplasms ,Reproducibility of Results ,Magnetic resonance imaging ,Prostate-Specific Antigen ,medicine.disease ,Radiation therapy ,ROC Curve ,business - 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. © 2018 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)., Summary This study aims to combine whole-mount prostate pathology with multiparametric magnetic resonance imaging from 39 patients to generate predictive maps of epithelium and lumen density in magnetic resonance imaging space. We show that the new image contrasts generated stratify high-grade tumors from low-grade tumors and healthy tissue. Future studies will explore targeted radiation therapy and clinical disease staging using the radio-pathomic mapping technique.
- Published
- 2018