Clinically significant prostate cancer detection with deep learning in a multi-center magnetic resonance imaging study
Accurate early detection of clinically significant prostate cancer is crucial for improving patient outcomes. However, traditional diagnostic methods such as Digital Rectal Exam and Prostate-Specific Antigen (PSA) tests often lack the sensitivity and specificity needed for effective diagnosis. This...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO) |
| Repositorio: | r-FISABIO. Repositorio Institucional de Producción Científica |
| OAI Identifier: | oai:dnet:r-fisabio___::d37d47342bab02cee2267a93a23648dd |
| Acceso en línea: | https://fisabio.portalinvestigacion.com/publicaciones/20887 |
| Access Level: | acceso abierto |
| Palabra clave: | Prostate cancer Deep learning Prostate dataset Magnetic resonance imaging |
| Sumario: | Accurate early detection of clinically significant prostate cancer is crucial for improving patient outcomes. However, traditional diagnostic methods such as Digital Rectal Exam and Prostate-Specific Antigen (PSA) tests often lack the sensitivity and specificity needed for effective diagnosis. This study presents an AI-based approach for csPCa classification using MRI data, incorporating both the PI-CAI Challenge dataset and a newly compiled, diverse BIMCV Prostate dataset comprising over 9000 MRI sessions from 16 healthcare centers in the Valencian Region. The methodology includes a robust preprocessing pipeline, featuring prostate segmentation with a custom-trained nnUNet model, and utilizes a 3D variant of EfficientNet-B7. To ensure robustness, we employed a transfer learning strategy where five models pretrained on PI-CAI were fine-tuned on the BIMCV dataset and aggregated using a stacked meta-learner. This ensemble approach yielded a Receiver Operating Characteristic Area Under the Curve of 0.816 on the independent hold-out set, significantly outperforming a non-pretrained baseline (AUC 0.71). Furthermore, we demonstrated that synthesizing missing ADC maps using a mono-exponential model serves as an effective data augmentation strategy, preventing data loss without introducing domain shift. Interpretability techniques such as occlusion sensitivity and guided backpropagation were employed to provide insights into the model's decision-making process, enhancing transparency. This research highlights the potential of AI-enhanced MRI techniques in advancing csPCa detection and diagnosis. |
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