Contributors: Institut Català de la Salut; Morote J, Trilla E Servei d’Urologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Departament de Cirurgia, Universitat Autònoma de Barcelona, Bellaterra, Spain. Grup de Recerca Biomèdica en Urologia, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Miró B Unitat d’Estadística i Bioinformàtica, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Hernando P Department of Artificial Intelligence and Big Data, GMV Innovative Solutions Inc., Madrid, Spain. Paesano N Departament de Cirurgia, Universitat Autònoma de Barcelona, Bellaterra, Spain. Clínica Creu Blanca, Barcelona, Spain. Picola N Servei d'urologia, Hospital Universitari de Bellvitge, Hospitalet de Llobregat, Spain. Muñoz-Rodriguez J Servei d'urologia, Hospital Universitari Parc Tauli, Sabadell, Spain. Celma A Servei d’Urologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Grup de Recerca Biomèdica en Urologia, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Méndez O Grup de Recerca Biomèdica en Urologia, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain; Vall d'Hebron Barcelona Hospital Campus
نبذة مختصرة : Logistic regression; Predictive models; Prostate cancer detection ; Regresión logística; Modelos predictivos; Detección de cáncer de próstata ; Regressió logística; Models predictius; Detecció de càncer de pròstata ; Objective: This study compares machine learning (ML) and logistic regression (LR) algorithms in developing a predictive model for sPCa using the seven predictive variables from the Barcelona (BCN-MRI) predictive model. Method: A cohort of 5005 men suspected of having PCa who underwent MRI and targeted and/or systematic biopsies was used for training, testing, and validation. A feedforward neural network (FNN)-based SimpleNet model (GMV) and a logistic regression-based model (BCN) were developed. The models were evaluated for discrimination ability, precision-recall, net benefit, and clinical utility. Both models demonstrated strong predictive performance. Results: The GMV model achieved an area under the curve of 0.88 in training and 0.85 in test cohorts (95% CI: 0.83-0.90), while the BCN model reached 0.85 and 0.84 (95% CI: 0.82-0.87), respectively (p > 0.05). The GMV model exhibited higher recall, making it more suitable for clinical scenarios prioritizing sensitivity, whereas the BCN model demonstrated higher precision and specificity, optimizing the reduction of unnecessary biopsies. Both models provided similar clinical benefit over biopsying all men, reducing unnecessary procedures by 27.5-29% and 27-27.5% of prostate biopsies at 95% sensitivity, respectively (p > 0.05). Conclusions: Our findings suggest that both ML and LR models offer high accuracy in sPCa detection, with ML exhibiting superior recall and LR optimizing specificity. These results highlight the need for model selection based on clinical priorities. ; This research was funded by the Ministerio de Asuntos Económicos y Transformación Digital (SP) (MIA.2021.M02.0005) and the Instituto de Salut Carlos III (SP) through the project “PI20/01666” (Co-funded by European Regional Development Fund “A way to make Europe”).
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