Automated CT Volumetry of Peripancreatic Collections for Risk Stratification in Acute Pancreatitis: A Multicenter Retrospective Study.

Authors

Siyuan Ouyang, Yujun Fan, Fuyao Liu, Qi Jiang, Ya Lan, Liming Yang, Hui Zhou, Yichao Xu, Wei Xing, Jie Chen, Jinggang Zhang

Published in

Academic radiology. Aug 07, 2026. Epub Aug 07, 2026.

Abstract

Extrapancreatic necrosis volume is an established prognostic marker in acute necrotizing pancreatitis, yet early diagnosis remains challenging and manual segmentation is labor-intensive. We developed and validated an automated CT volumetry tool for peripancreatic collections (PPCs) on contrast-enhanced CT (CE-CT) and evaluated its utility for early risk stratification.
This retrospective study initially screened 520 patients with acute pancreatitis and ultimately included 394. Patients from the primary center (n = 303) were temporally divided into a development cohort (n = 198) for model construction and an internal test cohort (n = 105). An additional 91 patients from two external hospitals formed the external test cohort. Using manual segmentations verified by two radiologists as the reference standard, model performance was assessed via Dice coefficients and Pearson correlation. Multivariable logistic regression and receiver operating characteristic (ROC) analyses evaluated the association between automated PPC volume and the primary outcome (organ failure) as well as the exploratory secondary outcome (infection).
The development, internal test, and external test cohorts included 198, 105, and 91 patients, respectively (mean age, 46 years; range, 18-90). In the external test cohort, automated segmentation showed excellent agreement with manual assessment [Dice coefficient, 0.89 (95% CI: 0.87-0.90); Pearson r = 0.99 (95% CI: 0.97-1.00; P < 0.001)]. Automated peripancreatic collection (PPC) volume (per 100 mL) was independently associated with organ failure and infection in both test cohorts (all P ≤ 0.006). In the external test cohort, automated PPC volume demonstrated strong performance for risk stratification of organ failure (AUC: 0.82 vs. modified CT severity index [mCTSI]: 0.73) and infection (AUC: 0.79 vs. mCTSI: 0.72). It also significantly outperformed mCTSI in the internal test cohort for both outcomes. The framework was substantially faster than manual segmentation (median, 25.2 s vs. 12.4 min per patient; P < 0.001) and surpassed radiologists' subjective binary classification (low vs. high PPC burden).
This deep learning framework enables rapid, fully automated PPC volumetry on CE-CT and provides clinically meaningful risk stratification for organ failure and infection in patients with acute pancreatitis and early peripancreatic collections.

PMID:
42567794
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.

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