Radio-pathomic maps of histo-morphometric features trained with whole mount prostate histology distinguish prostate cancer on MP-MRI
Year: 2026
Type: preprint
Abstract: Background: Prostate cancer (PCa) is the most prevalent male cancer in the U.S., accounting for 29% of new cancer diagnoses. Multiparametric MRI (MP-MRI), including T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps, is an effective tool for detecting PCa; however, accuracy varies, and false-positives may lead to unnecessary biopsies or overtreatment. Radio-pathomic maps (RPMs), derived from MP-MRI and machine learning, have been advantageous in differentiating clinically significant PCa. This study tested whether RPMs of tissue density and histo-morphometric features could better predict cancer presence than conventional MR imaging. Materials and Methods: MP-MRI from 236 patients prospectively recruited between 2014 and 2023 with confirmed PCa were analyzed. Whole-mount prostate sections sliced to match the MRI were processed, digitized, and Gleason-pattern annotated by a GU pathologist. Automated algorithms identified glands and calculated quantitative histo-morphometric features, which were mapped across whole slide images. Slides were nonlinearly aligned to each patient's T2WI using in-house software, enabling direct comparison of slides, features, and annotations in MR-space. A multi-step prediction model was trained using a 2/3 - 1/3 train/test split to predict histo-morphometric features using 5×5 voxel tiles from T2WI and ADC. These feature maps were then used generate tumor probability maps. Results: Histological feature models produced RMSE values approximately within one standard deviation of the ground truth's variability, indicating acceptable performance. The best RPM, using histological density features, achieved an accuracy of ~80%. Visual inspection of RPMs showed good concordance to high-grade cancer annotations. Conclusion: This study demonstrates that the use of MRI intensities can predict complex histo-morphometric features and delineate regions of PCa non-invasively. Future research is warranted to determine the clinical benefit of using RPMs in treatment guidance.
Source: medRxiv
Authors: Savannah R. Duenweg, Samuel A. Bobholz, Allison K. Lowman, Aleksandra Winiarz, Biprojit Nath, Michael J. Barrett, Fitzgerald Kyereme, Stephanie Vincent-Sheldon, Kathleen Bhatt, Katherine Troy, Michael Kim, Eric Fair, Kenneth A. Iczkowski, Kenneth M. Jacobsohn, Anjishnu Banerjee, William A. Hall, Andrew S. Nencka, Peter S. LaViolette
Institutions: Medical College of Wisconsin, University of California, Davis
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1569032076, Bayesian learning algorithms for identifying and classifying heterogeneity of cell types in variety of solid tumors, G9000175952, Prostate Cancer Radio-Pathomics for Differentiating Clinically Significant Disease, Radiopathomic Modeling of Glioma Heterogeneity Throughout a Patient's Disease Trajectory
OpenAlex ID: W7123478026