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. 2025 Aug 18;43(1):502.
doi: 10.1007/s00345-025-05797-w.

Machine learning driven diagnostic pathway for clinically significant prostate cancer: the role of micro-ultrasound

Cesare Saitta  1   2   3 , NicolòMaria Buffi  1   2 , Pierpaolo Avolio  2 , Edoardo Beatrici  1   2 , Marco Paciotti  1   2 , Massimo Lazzeri  1 , Vittorio Fasulo  1   2 , Ludovica Cella  1   2 , Giuseppe Garofano  1   2 , Andrea Piccolini  1   2 , Roberto Contieri  1   2 , Sebastiano Nazzani  3 , Carlo Silvani  3 , Mario Catanzaro  3 , Nicola Nicolai  3 , Rodolfo Hurle  2 , Paolo Casale  2 , Alberto Saita  2 , Giovanni Lughezzani  4   5
Affiliations

Machine learning driven diagnostic pathway for clinically significant prostate cancer: the role of micro-ultrasound

Cesare Saitta et al. World J Urol. .

Abstract

Introduction & objectives: Detecting clinically significant prostate cancer (csPCa) remains a top priority in delivering high-quality care, yet consensus on an optimal diagnostic pathway is constantly evolving. In this study, we present an innovative diagnostic approach, leveraging a machine learning model tailored to the emerging role of prostate micro-ultrasound (micro-US) in the setting of csPCa diagnosis.

Materials & methods: We queried our prospective database for patients who underwent Micro-US for a clinical suspicious of prostate cancer. CsPCa was defined as any Gleason group grade > 1. Primary outcome was the development of a diagnostic pathway which implements clinical and radiological findings using machine learning algorithm. The dataset was divided into training (70%) and testing subsets. Boruta algorithms was used for variable selection, then based on the importance coefficients multivariable logistic regression model (MLR) was fitted to predict csPCA. Classification and Regression Tree (CART) model was fitted to create the decision tree. Accuracy of the model was tested using receiver characteristic curve (ROC) analysis using estimated area under the curve (AUC).

Results: Overall, 1422 patients were analysed. Multivariable LR revealed PRI-MUS score ≥ 3 (OR 4.37, p < 0.001), PI-RADS score ≥ 3 (OR 2.01, p < 0.001), PSA density ≥ 0.15 (OR 2.44, p < 0.001), DRE (OR 1.93, p < 0.001), anterior lesions (OR 1.49, p = 0.004), prostate cancer familiarity (OR 1.54, p = 0.005) and increasing age (OR 1.031, p < 0.001) as the best predictors for csPCa, demonstrating an AUC in the validation cohort of 83%, 78% sensitivity, 72.1% specificity and 81% negative predictive value. CART analysis revealed elevated PRIMUS score as the main node to stratify our cohort.

Conclusions: By integrating clinical features, serum biomarkers, and imaging findings, we have developed a point of care model that accurately predicts the presence of csPCa. Our findings support a paradigm shift towards adopting MicroUS as a first level diagnostic tool for csPCa detection, potentially optimizing clinical decision making. This approach could improve the identification of patients at higher risk for csPca and guide the selection of the most appropriate diagnostic exams. External validation is essential to confirm these results.

Keywords: AI; Active surveillance; MRI; Machine learning; Micro-Ultrasound; PI-RADS; PRI-MUS; Prostate cancer; Targeted biopsy; imaging-guided diagnosis.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethical approval: The study was approved by the Ethics Committee of Humanitas Research Hospital, Rozzano, Milan, Italy (ICH-006). Consent to participate: Informed consent was obtained from all individual participants included in the study.

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