ArticleEuropean radiology2025
End-to-end prognostication in pancreatic cancer by multimodal deep learning: a retrospective, multicenter study.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Interpretable machine learning prediction of 1-year overall survival in pancreatic cancer patients aged 65 years and older.Translational cancer research · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- From Routine Imaging to Risk Stratification: Multimodal Vision-Language Survival Modeling for Pancreatic Cancer.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Application of machine learning for prognostic modeling in unresectable pancreatic cancer treated with chemoradiotherapy.Frontiers in oncology · 2025Article
- The place of advanced machine learning techniques in building pancreatic adenocarcinoma survival and recurrence prognosis models.Frontiers in oncology · 2025Review
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Authors and funding
11 authors.
Funding
Abstract
objectivesPancreatic cancer treatment plans involving surgery and/or chemotherapy are highly dependent on disease stage. However, current staging systems are ineffective and poorly correlated with survival outcomes. We investigate how artificial intelligence (AI) can enhance prognostic accuracy in pancreatic cancer by integrating multiple data sources. MATERIALS AND
methodsPatients with histopathology and/or radiology/follow-up confirmed pancreatic ductal adenocarcinoma (PDAC) from a Dutch center (2004-2023) were included in the development cohort. Two additional PDAC cohorts from a Dutch and Spanish center were used for external validation. Prognostic models including clinical variables, contrast-enhanced CT images, and a combination of both were developed to predict high-risk short-term survival. All models were trained using five-fold cross-validation and assessed by the area under the time-dependent receiver operating characteristic curve (AUC).
resultsThe models were developed on 401 patients (203 females, 198 males, median survival (OS) = 347 days, IQR: 171-585), with 98 (24.4%) short-term survivors (OS < 230 days) and 303 (75.6%) long-term survivors. The external validation cohorts included 361 patients (165 females, 138 males, median OS = 404 days, IQR: 173-736), with 110 (30.5%) short-term survivors and 251 (69.5%) longer survivors. The best AUC for predicting short vs. long-term survival was achieved with the multi-modal model (AUC = 0.637 (95% CI: 0.500-0.774)) in the internal validation set. External validation showed AUCs of 0.571 (95% CI: 0.453-0.689) and 0.675 (95% CI: 0.593-0.757).
conclusionMultimodal AI can predict long vs. short-term survival in PDAC patients, showing potential as a prognostic tool in clinical decision-making. KEY POINTS: Question Prognostic tools for pancreatic ductal adenocarcinoma (PDAC) remain limited, with TNM staging offering suboptimal accuracy in predicting patient survival outcomes. Findings The multimodal AI model demonstrated improved prognostic performance over TNM and unimodal models for predicting short- and long-term survival in PDAC patients. Clinical relevance Multimodal AI provides enhanced prognostic accuracy compared to current staging systems, potentially improving clinical decision-making and personalized management strategies for PDAC patients.
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