A Deep Learning Model Integrating Clinical and MRI Features Improves Risk Stratification and Reduces Unnecessary Biopsies in Men with Suspected Prostate Cancer.

Authors

Bacchetti E,De Nardin A,Giannarini G,Cereser L,Zuiani C,Crestani A,Girometti R,Foresti GL

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.

Topics

Journal Article

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