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

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 under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) on validation and external test datasets. RESULTS: The study comprised 230 patients with PC, with a mean age of 62 years. The incidence of POPF was observed to be 47.4% (Grade B/C). In terms of muscle segmentation, the Dice similarity coefficients (DSCs) for the testing set varied between 91.84 and 98.41% across different regions of the abdominal musculature. For the segmentation of visceral and subcutaneous adipose tissue, the DSCs in the testing set were 97.10 and 98.57%, respectively. The integrated clinical and imaging-based POPF prediction model demonstrated superior performance, achieving an AUC of 0.82, with a sensitivity of 0.81 and a specificity of 0.76 in the external test set. CONCLUSION: 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.