000150801 001__ 150801
000150801 005__ 20251017144653.0
000150801 0247_ $$2doi$$a10.1038/s41598-025-88297-6
000150801 0248_ $$2sideral$$a142773
000150801 037__ $$aART-2025-142773
000150801 041__ $$aeng
000150801 100__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis Mariano
000150801 245__ $$aIntegrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques
000150801 260__ $$c2025
000150801 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150801 5203_ $$aIn prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging–reporting and data system (PI-RADS) is combined with clinical variables predominantly based on logistic regression models. This study explores modeling using regularization techniques such as ridge regression, LASSO, elastic net, classification tree, tree ensemble models like random forest or XGBoost, and neural networks to predict CsPCa in a dataset of 4799 patients in Catalonia (Spain). An 80–20% split was employed for training and validation. We used predictor variables such as age, prostate-specific antigen (PSA), prostate volume, PSA density (PSAD), digital rectal exam (DRE) findings, family history of PCa, a previous negative biopsy, and PI-RADS categories. When considering a sensitivity of 0.9, in the validation set, the XGBoost model outperforms others with a specificity of 0.640, followed closely by random forest (0.638), neural network (0.634), and logistic regression (0.620). In terms of clinical utility, for a 10% missclassification of CsPCa, XGBoost can avoid 41.77% of unnecessary biopsies, followed closely by random forest (41.67%) and neural networks (41.46%), while logistic regression has a lower rate of 40.62%. Using SHAP values for model explainability, PI-RADS emerges as the most influential risk factor, particularly for individuals with PI-RADS 4 and 5. Additionally, a positive digital rectal examination (DRE) or family history of prostate cancer proves highly influential for certain individuals, while a previous negative biopsy serves as a protective factor for others.
000150801 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T69-23R$$9info:eu-repo/grantAgreement/ES/ISCIII/PI20/01666$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
000150801 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000150801 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150801 700__ $$0(orcid)0000-0003-0178-4567$$aBorque-Fernando, Ángel$$uUniversidad de Zaragoza
000150801 700__ $$0(orcid)0000-0002-1801-2144$$aEscorihuela, Maria Etelvina
000150801 700__ $$0(orcid)0000-0001-7995-6969$$aEsteban-Escaño, Javier
000150801 700__ $$aAbascal, Jose María
000150801 700__ $$aServian, Pol
000150801 700__ $$aMorote, Juan
000150801 7102_ $$11013$$2817$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Urología
000150801 773__ $$g15 (2025), 4261$$pSci. rep. (Nat. Publ. Group)$$tScientific reports (Nature Publishing Group)$$x2045-2322
000150801 8564_ $$s4970416$$uhttps://zaguan.unizar.es/record/150801/files/texto_completo.pdf$$yVersión publicada
000150801 8564_ $$s2585230$$uhttps://zaguan.unizar.es/record/150801/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150801 909CO $$ooai:zaguan.unizar.es:150801$$particulos$$pdriver
000150801 951__ $$a2025-10-17-14:37:06
000150801 980__ $$aARTICLE