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A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging

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Title: A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging
Authors: Molière, Sébastien, Hamzaoui, Dimitri, Ploussard, Guillaume, Mathieu, Romain, Fiard, Gaëlle, Baboudjian, Michael, granger, benjamin, Roupret, Morgan, Delingette, Hervé, Renard-Penna, Raphaële
Contributors: Service de radiologie Strasbourg, Centre Hospitalier Universitaire Strasbourg (CHU Strasbourg), Les Hôpitaux Universitaires de Strasbourg (HUS)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Hôpital de Hautepierre Strasbourg, Institut de Cancérologie de Strasbourg Europe (ICANS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Aalto University, Clinique La Croix du Sud, Pôle IUCT CHU Toulouse, Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Institut de recherche en santé, environnement et travail (Irset), Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique EHESP (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes (Biosit : Biologie - Santé - Innovation Technologique), Translational Innovation in Medicine and Complexity / Recherche Translationnelle et Innovation en Médecine et Complexité - UMR 5525 (TIMC), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP), Université Grenoble Alpes (UGA), Hôpital Nord CHU - APHM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Groupe de Recherche Clinique Onco-Urologie Prédictive (GRC 5), Sorbonne Université (SU), CHU Pitié-Salpêtrière AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Société Française de Radiologie, ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
Source: European Urology Oncology. 8:1182-1202
Publisher Information: Elsevier BV, 2025.
Publication Year: 2025
Subject Terms: Artificial intelligence, Magnetic resonance imaging, Prostate cancer, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Systematic review, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Deep learning, [SDV.CAN]Life Sciences [q-bio]/Cancer, Diagnostic accuracy, [SDV.MHEP.UN]Life Sciences [q-bio]/Human health and pathology/Urology and Nephrology
Description: Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.A systematic search was conducted across Medline/PubMed, Embase, Web of Science, and ScienceDirect for studies published between January 2020 and September 2023. Studies were included if these presented and validated fully automated DL models for csPCa detection on MRI, with pathology confirmation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Checklist for Artificial Intelligence in Medical Imaging.Twenty-five studies met the inclusion criteria, showing promising results in detecting and characterizing csPCa. However, significant heterogeneity in study designs, validation strategies, and datasets complicates direct comparisons. Only one-third of studies performed external validation, highlighting a critical gap in generalizability. The reliance on internal validation limits a broader application of these findings, and the lack of standardized methodologies hinders the integration of DL models into clinical practice.DL models demonstrate significant potential in improving prostate cancer diagnostics on MRI. However, challenges in validation, generalizability, and clinical implementation must be addressed. Future research should focus on standardizing methodologies, ensuring external validation and conducting prospective clinical trials to facilitate the adoption of artificial intelligence (AI) in routine clinical settings. These findings support the cautious integration of AI into clinical practice, with further studies needed to confirm their efficacy in diverse clinical environments.In this study, we reviewed how artificial intelligence (AI) models can help doctors better detect and understand aggressive prostate cancer using magnetic resonance imaging scans. We found that while these AI tools show promise, these tools need more testing and validation in different hospitals before these can be used widely in patient care.
Document Type: Article
Language: English
ISSN: 2588-9311
DOI: 10.1016/j.euo.2024.11.001
Access URL: https://pubmed.ncbi.nlm.nih.gov/39547898
Rights: CC BY
Accession Number: edsair.doi.dedup.....142978e69b4765eb520a5c58aa2102a2
Database: OpenAIRE
Description
Abstract:Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.A systematic search was conducted across Medline/PubMed, Embase, Web of Science, and ScienceDirect for studies published between January 2020 and September 2023. Studies were included if these presented and validated fully automated DL models for csPCa detection on MRI, with pathology confirmation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Checklist for Artificial Intelligence in Medical Imaging.Twenty-five studies met the inclusion criteria, showing promising results in detecting and characterizing csPCa. However, significant heterogeneity in study designs, validation strategies, and datasets complicates direct comparisons. Only one-third of studies performed external validation, highlighting a critical gap in generalizability. The reliance on internal validation limits a broader application of these findings, and the lack of standardized methodologies hinders the integration of DL models into clinical practice.DL models demonstrate significant potential in improving prostate cancer diagnostics on MRI. However, challenges in validation, generalizability, and clinical implementation must be addressed. Future research should focus on standardizing methodologies, ensuring external validation and conducting prospective clinical trials to facilitate the adoption of artificial intelligence (AI) in routine clinical settings. These findings support the cautious integration of AI into clinical practice, with further studies needed to confirm their efficacy in diverse clinical environments.In this study, we reviewed how artificial intelligence (AI) models can help doctors better detect and understand aggressive prostate cancer using magnetic resonance imaging scans. We found that while these AI tools show promise, these tools need more testing and validation in different hospitals before these can be used widely in patient care.
ISSN:25889311
DOI:10.1016/j.euo.2024.11.001