Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques

Abstract In 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 p...

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Bibliographic Details
Main Authors: Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Clinically significant prostate cancer
Machine learning
Clinical utility
SHAP values
Online Access:https://doi.org/10.1038/s41598-025-88297-6
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https://doi.org/10.1038/s41598-025-88297-6

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