Integrative deep learning analysis of 2D and 3D body composition features for predicting postoperative pancreatic fistula after distal pancreatectomy
The implementation of a deep learning-based 2D and 3D body composition analysis pipeline exhibited high accuracy in predicting POPF following radical DP for PC. However, further validation in larger, multicenter cohorts is required to confirm the generalizability of these findings.
Authors
- Guoguang Fan (ORCID: https://orcid.org/0000-0001-8114-5727)
- Ruimei Chai (ORCID: https://orcid.org/0000-0002-4553-2145)
- Jingao Xu (ORCID: https://orcid.org/0000-0002-8347-2657)
- Keke Liang (ORCID: https://orcid.org/0000-0002-0007-6326)
- Yi Jing (ORCID: https://orcid.org/0009-0002-7778-7051)
- Kexin Zhu (ORCID: https://orcid.org/0000-0002-8892-4077)
- Qi Miao (ORCID: https://orcid.org/0000-0003-2235-8997)
- Xiaodong Tan (ORCID: https://orcid.org/0000-0003-0862-1306)
- He Zhang
- Yashu Liu
Institutions
- Neusoft (China) (CN)
- First Hospital of China Medical University (CN)
- Intelligent Health (United Kingdom) (GB)
- China Medical University (CN)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2025-12-04
- DOI
- https://doi.org/10.1080/07853890.2025.2597067
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00