An explainable AI workflow integrating automated volumetric body composition analysis for predicting pathological grading of gastroenteropancreatic neuroendocrine neoplasms: a multicenter cohort study
Auto-segmented abdominal body composition combined with a machine learning-based model could provide an assisted, non-invasive tool for predicting pathological grade in GEP-NENs.
Authors
- Yaqi Shen (ORCID: https://orcid.org/0000-0002-1468-1092)
- Weinuo Qu
- Shasha Li (ORCID: https://orcid.org/0000-0002-7383-866X)
- Feng Li (ORCID: https://orcid.org/0000-0001-5892-6496)
- Zhen Li
- Huiyu Gan
- Jiali Li
- Zhibo Wang
- Chenxi Lyu
Institutions
- Hubei University of Arts and Science (CN)
- Shanxi Academy of Medical Sciences (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2026-01-29
- DOI
- https://doi.org/10.1097/js9.0000000000004879
- Primary Topic
- Neuroendocrine Tumor Research Advances
- Type
- article
- Field-Weighted Citation Impact
- 0.00