Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI
Rattachement africain : us, gb, tr. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract Prostate cancer (PCa) is the second most common cancer and cause of cancer death in American men. Existing risk prediction methods have limited accuracy and reproducibility, resulting in difficulty in predicting disease severity. We demonstrate the development and external validation of an automated multimodal artificial intelligence algorithm using biparametric MRI (bpMRI) and clinical covariates for predicting biochemical recurrence (BCR) after radical prostatectomy (RP) in PCa patients. Development cohort included 80% of patients from center 1 ( n = 240) who underwent prostate MRI prior to RP between January 2008 and December 2018 with a minimum of two years of follow-up after RP. Test cohort included the remaining 20% of center 1 patients ( n = 71), and the external validation cohort from center 2 ( n = 168). Center 2 patients included those who underwent prostate MRI and RP between January 2015 and December 2024 with a minimum of two years of follow-up. Clinical comparisons were CAPRA-S (center 1) and ISUP grade group from post-RP biopsy (center 2). Models developed were a clinical model (M0), an automated clinical model (M1), a radiomics model (M2), and a multimodal model (M3). Clinical variables (M0) included PSA, age, primary Gleason, and ISUP grade group. Automated clinical variables (M1 and M3) included PSA and age. Radiomics features (M2 and M3) were extracted from bpMRI using a lesion detection algorithm. Accuracy, sensitivity, specificity, and AUC were calculated, and log-rank tests compared BCR-free survival to assess the models’ ability to discriminate relative to clinical standards. Intermediate-risk groups were also assessed. The multimodal model (M3) had the highest AUC across test sets (combined: 0.71; center 1: 0.70; center 2: 0.75) and was the only model to significantly differentiate BCR-free survival outcomes in intermediate-risk groups across both centers ( p < 0.05). This automated multimodal model leveraging radiomics and clinical covariates can predict BCR after RP, approaches clinical gold standards, and may enhance imaging-based prognostication following further validation.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI
- Date Crossref
- 22/03/2026
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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National Institutes of Health pays non établi dans la noticeOrganisme public
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University of Oxford Institute of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Hacettepe University Department of Radiology pays non établi dans la noticeUniversité ou école supérieure
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Open Data Institute pays non établi dans la noticeOrganisation à but non lucratif
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Center for Cancer Research pays non établi dans la noticeStructure de recherche
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National Institutes of Health Clinical Center Department of Radiology pays non établi dans la noticeÉtablissement de santé
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Molecular Imaging Branch pays non établi dans la noticeInstitution
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Urologic Oncology Branch pays non établi dans la noticeInstitution
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Center for Interventional Oncology pays non établi dans la noticeInstitution
National Institutes of Health, Institute of Biomedical Engineering — University of Oxford et Department of Radiology — Hacettepe University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.