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. 2025 Oct;38(5):817-827.
doi: 10.1007/s10334-025-01251-5. Epub 2025 Apr 29.

Beyond Gleason grading: MRI radiomics to differentiate cribriform growth from non-cribriform growth in prostate cancer men

Mar Fernandez Salamanca  1 , Rita Simões  2 , Malgorzata Deręgowska-Cylke  3 , Pim J van Leeuwen  4 , Henk G van der Poel  4   5 , Elise Bekers  6 , Marcos A S Guimaraes  6 , Uulke A van der Heide  2 , Ivo G Schoots  7   8
Affiliations

Beyond Gleason grading: MRI radiomics to differentiate cribriform growth from non-cribriform growth in prostate cancer men

Mar Fernandez Salamanca et al. MAGMA. 2025 Oct.

Abstract

Objective: To differentiate cribriform (GP4Crib+) from non-cribriform growth and Gleason 3 patterns (GP4Crib-/GP3) using MRI.

Methods: Two 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.

Results: The 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).

Conclusion: The 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.

Keywords: ADC; Cribriform growth; MRI; Prostatectomy; Radiomics.

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Conflict of interest statement

Declarations. Conflict of interest: The authors have no competing interests to declare that are relevant to the content of this article. Ethical approval: The studies involving humans were approved by the Netherlands Cancer Institute—IRBd21-108. The studies were conducted in accordance with the local legislation and institutional requirements. Consent to participate: The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because pursuant to national legislation prior to 25 May 2018 (Opt-out) and general hospital informed consent was considered.

Figures

Fig. 1
Fig. 1
Pipeline used for classification of histopathological regions of cribriform growth based on MRI radiomics. GP3, GP4Crib- and GP4Crib + regions were delineated in whole-mount specimens and subsequently co-registered to MRI. Isotropic erosion was performed in all regions for further radiomics extraction on T2w, ADC and fBV scans. For model development, mRMR selection algorithm was used and selected features were used as input for the logistic regression model to predict GP4Crib+ . Model outcome was shown as a predicted probability of the positive class (GP4Crib+) and binary label (0: GP3 and GP4Crib-; 1: GP4Crib+). T2w: T2-weighted images; ADC: apparent diffusion coefficient; fBV: fractional blood volume; mRMR: minimum redundancy maximum relevance; GP3: Gleason Pattern 3; GP4Crib-: Gleason Pattern 4 subtypes excluding cribriform growth; GP4Crib+ : Gleason Pattern 4 subtype cribriform growth
Fig. 2
Fig. 2
Performance metrics of the radiomics model in the test set. Left plot: ROC curve, right plot: precision–recall curve
Fig. 3
Fig. 3
Model outcome of four prostate cancer examples. Lesion (purple), Gleason Pattern 3 (green), Gleason Pattern 4 subtypes excluding cribriform growth (orange) and Gleason Pattern 4 subtype cribriform growth (white)). The horizontal dotted line at the 90th percentile ADC of 1.13×10−3 mm/s2 corresponds to a predicted probability of 50% chance for any region being characterized as GP4Crib+ . Numbers between brackets show the predicted probability. Taking a cutoff of 1.13×10−3 mm/s2, regions with the 90th percentile ADC below the cutoff are classified as positive for GP4Crib+ , whereas regions above the cutoff are classified as negative. [A] ISUP Grade Group 4 cancer in the left transition zone shows two regions, GP4Crib- and GP4Crib+ . Both regions were correctly predicted. [B] IUSP Grade Group 2 cancer in the left peripheral zone shows three regions, GP3, GP4Crib- and GP4Crib+ , which were correctly predicted, respectively, being true negative, true negative and true positive. [C] ISUP Grade Group 3 cancer in left peripheral zone shows three regions. While the GP4Crib+ region was correctly predicted (true positive), GP3 and GP4Crib- regions were both falsely predicted as GP4Crib+ positives. Despite the overdiagnosis (false positives), there was no underdiagnosis (no false negatives).The overall presence of cribriform growth within this patient was adequately assessed. [D] ISUP Grade Group 3 cancer in the right peripheral zone shows three regions. For the GP4Crib+ and GP3 regions, the model correctly predicted the presence and absence of cribriform growth, being true positive and true negative, respectively. However, for GP4Crib-, the model incorrectly predicted the presence of cribriform growth, being false positive, contributing to overdiagnosis
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