An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

Abstract To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort...

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Bibliographic Details
Main Authors: Nan Yi, Shuangyang Mo, Yan Zhang, Qi Jiang, Yingwei Wang, Cheng Huang, Shanyu Qin, Haixing Jiang
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Pancreatic neuroendocrine tumors
Endoscopic ultrasound
Deep learning
Machine learning
Shapley Additive explanations
Gradient-weighted class activation mapping
Online Access:https://doi.org/10.1038/s41598-024-84749-7
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https://doi.org/10.1038/s41598-024-84749-7

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