Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning
Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet expert assessment often shows substantial variability. We introduce a fully automated multimodal deep learning framework that jointly analyzes 3D contrast enhanced CT and structured clinical information to classify patients into the three National Comprehensive Cancer Network (NCCN) resectability categories (upfront resectable, borderline resectable, locally advanced). The approach uses a Swin-UNETR backbone to obtain
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- PossiblePossibly related (embedding) · 50%Multi-modal deep learning model for visual acuity prediction from wide field colour fundus imaging - Nature →
- PossiblePossibly related (embedding) · 45%TIP12 Validation of a Multimodal Artificial Intelligence Prognostic Model in Early-Stage HR+/HER2− Breast Cancer - CancerNetwork →
- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “Multimodal Assessment of Pancreatic Cancer Resectability Usi” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Vincent Ochs →
“Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning”
- LinkedLinked via arxiv author · 85%Christoph Kuemmerli →
“Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning”
- LinkedLinked via arxiv author · 85%Florentin Bieder →
“Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning”
- LinkedLinked via arxiv author · 85%Julia Wolleb →
“Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning”
- LinkedLinked via arxiv author · 85%Joel L. Lavanchy →
“Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning”