Integrative deep learning analysis of 2D and 3D body composition features for predicting postoperative pancreatic fistula after distal pancreatectomy.

ABSTRACT:

Background

Accurate prediction of postoperative pancreatic fistula (POPF) after radical distal pancreatectomy (DP) is critical for optimizing surgical strategies. The objective of this study was to develop and validate a deep learning-based framework for three-dimensional (3D) body composition analysis to forecast POPF in patients with pancreatic cancer (PC).

Methods

A retrospective analysis was conducted on patients who underwent radical DP at two institutions between 2015 and 2022. A deep learning-based 2D and 3D segmentation model was developed to assess abdominal muscles and fat using preoperative computed tomography (CT) images. Predictive models for POPF were constructed using Gradient Boosting Decision Trees (GBDT) and their performance was evaluated using the area un

SUBMITTER: Liang K 

PROVIDER: S-EPMC12679842 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature