CT-based deep learning integrating pancreatic imaging and body composition for severity prediction in acute pancreatitis

@article{zeng2026ctbased,
  title = {{CT-based deep learning integrating pancreatic imaging and body composition for severity prediction in acute pancreatitis}},
  author = {Dan Zeng and Meng He and Bingbing Lin and Yu Liu and Xinwei Wang and Jie Lu},
  journal = {Insights into Imaging},
  year = {2026},
  month = oct,
  doi = {10.1186/s13244-026-02443-w},
  url = {https://link.springer.com/article/10.1186/s13244-026-02443-w},
  note = {CC BY 4.0},
}

TY  - JOUR
TI  - CT-based deep learning integrating pancreatic imaging and body composition for severity prediction in acute pancreatitis
AU  - Dan Zeng
AU  - Meng He
AU  - Bingbing Lin
AU  - Yu Liu
AU  - Xinwei Wang
AU  - Jie Lu
PY  - 2026
DA  - 2026/10/28
JO  - Insights into Imaging
DO  - 10.1186/s13244-026-02443-w
UR  - https://link.springer.com/article/10.1186/s13244-026-02443-w
AB  - Objectives: To develop and validate a deep learning (DL)-based CT model integrating pancreatic imaging and CT-derived body composition for severity prediction in acute pancreatitis (AP).  Materials and methods: In this retrospective dual-center study, patients with AP who underwent CT within 72 h of symptom onset were enrolled from January 2021 to September 2025 at Center 1 and January 2023 to September 2024 at Center 2. The primary dataset ( n = 426) was randomly divided into a training set ( n = 298) and an internal test set ( n = 128), while 110 patients from Center 2 served as the external test set. A three-dimensional DL model was developed for pancreatic imaging, and two-dimensional DL models were constructed for L3-level visceral adipose tissue (VAT) and skeletal muscle (SM). Six predictive models were constructed: clinical, pancreas, VAT, SM, pancreas-clinical (PC), and combined. Performance was evaluated using receiver operating characteristic analysis, DeLong testing, calibration, decision curve analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).  Results: The combined model achieved area under the curve (AUCs) of 0.897 (95% CI: 0.862–0.932), 0.909 (95% CI: 0.861–0.958), and 0.910 (95% CI: 0.856–0.964) in the training, internal test, and external test sets, respectively. The combined model outperformed the PC model in the training set ( p = 0.002) and external test set ( p = 0.020). NRI and IDI analyses confirmed the incremental predictive value of body composition features.  Conclusions: A CT-based DL model integrating pancreatic imaging, body composition, and clinical variables demonstrated good performance for AP severity prediction.  Key Points: Question Early severity stratification in AP remains challenging because conventional clinical and imaging assessments may not fully capture pancreatic injury and systemic disease status.  Findings A CT-based DL model integrating pancreatic imaging, body composition, and clinical variables demonstrated good performance for severity prediction in AP.  Critical Relevance Statement This study evaluates the value of integrating pancreatic imaging and CT-derived body composition information and supports the use of routinely acquired CT data for early risk stratification, potentially facilitating more individualized monitoring and management of AP.
SN  - 1869-4101
ER  -