ArticlemedRxiv : the preprint server for health sciences2026
Radio-pathomic maps of histo-morphometric features trained with whole mount prostate histology distinguish prostate cancer on MP-MRI.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
18 authors.
Funding
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.
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Registered trials
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