Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables : Is Machine Learning Superior to Logistic Regression?
Morote Robles, Juan (Hospital Universitari Vall d'Hebron)
Miró, Berta (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Hernando-Sánchez, Patricia (GMV Innovative Solutions)
Paesano, Nahuel (Universitat Autònoma de Barcelona. Departament de Cirurgia)
Picola, Natàlia (Hospital Universitari de Bellvitge)
Muñoz-Rodríguez, Jesús (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Ruiz-Plazas, Xavier (Hospital Universitari Joan XXIII de Tarragona)
Muñoz Rivero, Marta Viridiana (Hospital Universitari Arnau de Vilanova)
Celma, Ana (Hospital Universitari Vall d'Hebron. Institut de Recerca)
García-de Manuel, Gemma (Hospital Universitari de Girona Doctor Josep Trueta)
Servian, Pol (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Abascal, Jose Maria (Parc de Salut MAR de Barcelona)
Trilla Herrera, Enrique (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Méndez Fernández, Olga (Hospital Universitari Vall d'Hebron. Institut de Recerca)

Date:	2025
Abstract:	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.
Grants:	Instituto de Salud Carlos III PI20/01666
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Predictive models ; Cancer detection ; Learning ; Regression
Published in:	Cancers, Vol. 17 Núm. 7 (april 2025) , p. 1101, ISSN 2072-6694

DOI: 10.3390/cancers17071101
PMID: 40227611

18 p, 3.3 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Parc Taulí Research and Innovation Institute (I3PT
Articles > Research articles
Articles > Published articles