ArticleMagma (New York, N.Y.)2025
Beyond Gleason grading: MRI radiomics to differentiate cribriform growth from non-cribriform growth in prostate cancer men.
Article in Magma (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Long-term outcomes of cribriform-positive and cribriform-negative prostate cancer treated with radical prostatectomy in the ProtecT trial.BJU international · 2026Trial
- MRI radiomics predicting cribriform growth informs active surveillance decision in intermediate-risk prostate cancer.European radiology experimental · 2026Article
- Hypoxia and cribriform growth in prostate cancer - establishing a link via MRI.BMC medical imaging · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo differentiate cribriform (GP4Crib+) from non-cribriform growth and Gleason 3 patterns (GP4Crib-/GP3) using MRI.
methodsTwo hundred and ninety-one operated prostate cancer men with pre-treatment MRI and whole-mount prostate histology were retrospectively included. T2-weighted, apparent diffusion coefficient (ADC) and fractional blood volume maps from 1.5/3T MRI systems were used. 592 histological GP3, GP4Crib- and GP4Crib+ regions were segmented on whole-mount specimens and manually co-registered to MRI sequences/maps. Radiomics features were extracted, and an erosion process was applied to minimize the impact of delineation uncertainties. A logistic regression model was developed to differentiate GP4Crib+ from GP3/GP4Crib- in the 465 remaining regions. The differences in balanced accuracy between the model and baseline (where all regions are labeled as GP3/GP4Crib-) and 95% confidence intervals (CI) for all metrics were assessed using bootstrapping.
resultsThe logistic regression model, using the 90th percentile ADC feature with a negative coefficient, showed a balanced accuracy of 0.65 (95% CI: 0.48-0.79), receiver operating characteristic area under the curve (AUC) of 0.75 (95% CI: 0.54-0.92), a precision-recall AUC of 0.35 (95% CI: 0.14-0.68).
conclusionThe radiomics MRI-based model, trained on Gleason sub-patterns segmented on whole-mount specimen, was able to differentiate GP4Crib+ from GP3/GP4Crib- patterns with moderate accuracy. The most dominant feature was the 90th percentile ADC. This exploratory study highlights 90th percentile ADC as a potential biomarker for cribriform growth differentiation, providing insights into future MRI-based risk assessment strategies.
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