A Deep Learning Model Integrating Clinical and MRI Features Improves Risk Stratification and Reduces Unnecessary Biopsies in Men with Suspected Prostate Cancer.
Authors
Affiliations (5)
Affiliations (5)
- Institute of Radiology, Department of Medicine (DMED), University of Udine, and University Hospital "Santa Maria della Misericordia", ASU FC, P.le S. M. Della Misericordia 15, 33100 Udine, Italy.
- Artificial Vision and Machine Learning Laboratory (AVML Lab), Department of Mathematics, Computer Science and Physics (DMIF), University of Udine, Via delle Scienze 206, 33100 Udine, Italy.
- Urology Unit, University Hospital "Santa Maria della Misericordia", ASU FC, P.le S. M. Della Misericordia 15, 33100 Udine, Italy.
- Urology Unit, Department of Medicine (DMED), University of Udine, and University Hospital "Santa Maria della Misericordia", ASU FC, P.le S. M. Della Misericordia 15, 33100 Udine, Italy.
- Artificial Vision and Real-Time Systems Laboratory (AViReS Lab), Department of Mathematics, Computer Science and Physics (DMIF), University of Udine, Via Delle Scienze 206, 33100 Udine, Italy.
Abstract
<b>Background:</b> Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. <b>Methods:</b> We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI. <b>Results:</b> Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%. <b>Conclusions:</b> Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions.