Rationale and Objectives To develop a pathology-derived radiomics signature for detecting clinically significant prostate cancer (csPCa) and to evaluate its performance using lesion diameter-based simplified segmentations. Materials and Methods In this retrospective single-center study, 175 participants (120 radical prostatectomy cases; 55 controls) underwent biparametric MRI during 2013–2022. Whole-mount histopathology was registered to MRI using a patient-specific, mold-based 3D pipeline to generate lesion-level ground truth. Six radiologists from different institutions marked lesion diameters per Prostate Imaging Reporting & Data System (PI-RADS) v2.1, blinded to pathology; automated circular segmentations were generated from these measurements and expanded (±1 slice). Features (PyRadiomics) were preprocessed, filtered, and benchmarked via nested cross-validation. Recursive feature elimination produced a 10-feature pathology-derived radiomics signature. Models (Signature, prostate-specific antigen density [PSAD], PI-RADS, and their combinations) were trained/evaluated using a soft-voting ensemble (logistic regression, random forest, and XGBoost) with patient-level grouping; thresholds were optimized using Youden’s J. DeLong’s and McNemar’s tests were used for model comparisons. Results PSAD+Signature achieved 0.75 area under the curve (AUC) and 68% (211/312) accuracy. PI-RADS+PSAD achieved 0.77 AUC and 74% (230/312) accuracy. Signature-only achieved 0.66 AUC and 62% (194/312) accuracy. A higher AUC for PSAD+Signature versus Signature (|ΔAUC|=0.093, p =0.012) and for the tripartite model versus Signature (|ΔAUC| = 0.11, p =0.007) was found. PSAD+Signature and PI-RADS+PSAD had similar accuracy (p =0.06). Conclusion A histopathology-trained radiomics signature demonstrated moderate standalone performance for lesion-level csPCa detection. When combined with PSAD, diagnostic performance improved and approached that of PI-RADS+PSAD, which achieved the highest absolute accuracy. The PSAD+Signature framework offers a simplified, spatially localized approach that may complement existing PI-RADS-based assessment while maintaining low implementation complexity.
Evaluating Clinically Significant Prostate Cancer with Pathology-Registered Radiomics: A Multi-Reader Assessment Using Lesion Diameter-based Simplified Segmentations on MRI / Esengur, O.T., Harmon, S.A., Gelikman, D.G., Yilmaz, E.C., Zhang, H., Simon, B., An, J.Y., Azamat, S., Law, Y.M., Margolis, D.J.A., Marko, J., Panebianco, V., Merino, M.J., Gurram, S., Wood, B.J., Lis, R., Choyke, P.L., Pinto, P.A., Turkbey, B.. - In: ACADEMIC RADIOLOGY. - ISSN 1076-6332. - (2026). [10.1016/j.acra.2026.04.017]
Evaluating Clinically Significant Prostate Cancer with Pathology-Registered Radiomics: A Multi-Reader Assessment Using Lesion Diameter-based Simplified Segmentations on MRI
Simon, Benjamin;Panebianco, Valeria;
2026
Abstract
Rationale and Objectives To develop a pathology-derived radiomics signature for detecting clinically significant prostate cancer (csPCa) and to evaluate its performance using lesion diameter-based simplified segmentations. Materials and Methods In this retrospective single-center study, 175 participants (120 radical prostatectomy cases; 55 controls) underwent biparametric MRI during 2013–2022. Whole-mount histopathology was registered to MRI using a patient-specific, mold-based 3D pipeline to generate lesion-level ground truth. Six radiologists from different institutions marked lesion diameters per Prostate Imaging Reporting & Data System (PI-RADS) v2.1, blinded to pathology; automated circular segmentations were generated from these measurements and expanded (±1 slice). Features (PyRadiomics) were preprocessed, filtered, and benchmarked via nested cross-validation. Recursive feature elimination produced a 10-feature pathology-derived radiomics signature. Models (Signature, prostate-specific antigen density [PSAD], PI-RADS, and their combinations) were trained/evaluated using a soft-voting ensemble (logistic regression, random forest, and XGBoost) with patient-level grouping; thresholds were optimized using Youden’s J. DeLong’s and McNemar’s tests were used for model comparisons. Results PSAD+Signature achieved 0.75 area under the curve (AUC) and 68% (211/312) accuracy. PI-RADS+PSAD achieved 0.77 AUC and 74% (230/312) accuracy. Signature-only achieved 0.66 AUC and 62% (194/312) accuracy. A higher AUC for PSAD+Signature versus Signature (|ΔAUC|=0.093, p =0.012) and for the tripartite model versus Signature (|ΔAUC| = 0.11, p =0.007) was found. PSAD+Signature and PI-RADS+PSAD had similar accuracy (p =0.06). Conclusion A histopathology-trained radiomics signature demonstrated moderate standalone performance for lesion-level csPCa detection. When combined with PSAD, diagnostic performance improved and approached that of PI-RADS+PSAD, which achieved the highest absolute accuracy. The PSAD+Signature framework offers a simplified, spatially localized approach that may complement existing PI-RADS-based assessment while maintaining low implementation complexity.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
