A non-invasive MRI-based multimodal fusion deep learning model (MF-DLM) for predicting overall survival in bladder cancer: a multicentre retrospective study
Notice bibliographique
Résumé
Background: Accurate prognosis prediction in bladder cancer (BCa) is crucial for personalized treatment. This study aimed to develop and validate a non-invasive model using magnetic resonance imaging (MRI) for predicting the overall survival (OS) in patients with BCa. Methods: This retrospective multicentre study included 1131 patients with BCa from eight institutions in China from June 2011 to March 2024. 871 patients were enrolled from one centre, who were randomly divided (8:2) into training (n = 697) and internal validation (n = 174) sets. For the external test set, 260 patients with BCa from seven centres were retrospectively included. We developed a multimodal fusion deep learning model (MF-DLM), leveraging a cross-attention mechanism to integrate four key preoperative data modalities: three-dimensional (3D) deep learning features using a modified 3D ResNet50 network, 3D radiomics features, morphological MRI features, and clinical features. Patients were stratified into low- and high-risk prognostic groups based on MF-DLM scores, and model interpretability was evaluated using Shapley additive explanations (SHAP) and Gradient-weighted class activation mapping (Grad-CAM). Findings: The median follow-up time for the training, validation, and external test sets are 38.0 months (interquartile ranges [IQR]: 22.0, 62.0), 40.5 months (IQR: 23.0, 71.0), and 38.5 months (IQR: 26.0, 50.0), respectively. The MF-DLM demonstrated excellent performance in predicting OS, achieving higher C-index values than pathological T stage (training: 0.902 vs. 0.793, p < 0.001; validation: 0.864 vs. 0.757, p = 0.014; external test: 0.841 vs. 0.760, p = 0.047). In addition, MF-DLM-based low-risk group demonstrated significantly longer OS in the training, validation, and external test sets (p < 0.001). In the adjuvant therapy (AT) cohort, high-risk patients had significantly worse prognosis compared with low-risk patients (p < 0.0001). Additionally, high-risk pathological T3/4 patients exhibited no statistically significant OS difference between those who received AT and those who did not (p = 0.18), whereas low-risk pathological T3/4 patients experienced significantly improved OS with AT (p = 0.0059). Besides, the low-risk group had better OS than the high-risk group in neoadjuvant therapy cohort (p = 0.0032). Interpretation: The MF-DLM can reliably predict OS in patients with BCa and provide additional prognostic stratification beyond pathological T and N stages. Furthermore, MF-DLM-based risk groups can identify patients most likely to benefit from perioperative therapy. Funding: The Noncommunicated Chronic Diseases-National Science and Technology Major Project (2024ZD0525700); National Natural Science Foundation of China (82273152, 82503879), Jiangsu Province Hospital (the First Affiliated Hospital of Nanjing Medical University) Clinical Capacity Enhancement Project (JSPH-MA-2022-5), China Postdoctoral Science Foundation funded project (2024M761211), and the Nanjing Postdoctoral Science Foundation funded project (2024BHS210).
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».