Body composition analysis using convolutional neural network in predicting postoperative pancreatic fistula and survival after pancreatoduodenectomy for pancreatic cancer

Purpose: To evaluate whether body composition measurements acquired using convolutional neural networks (CNNs) from preoperative CT images could predict postoperative pancreatic fistula (POPF) and overall survival (OS) after pancreaticoduodenectomy in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods: 257 patients (160 men; median age [interquartile range], 67 [60-74]) who underwent pancreaticoduodenectomy for PDAC between January 2013 and December 2017 were included in this retrospective study. Body composition measurements were based on a CNN trained to segment CT images into skeletal muscle area, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Skeletal muscle area, VAT, and SAT were normalized to height square and labeled as skeletal muscle, VAT, and SAT indices, respectively. The independent risk factors for clinically relevant POPF (grade B or C) were determined using a multivariate logistic regression model, and prognostic factors for OS were assessed using Cox proportional hazards regression analyses.

Results: After pancreatioduodenectomy, 27 patients developed POPF grade B or C (10.5 %, 27/257). The VAT index (odds ratio [OR] = 7.43, p < 0.001) was the only independent prognostic factor for POPF grade B or C. During the median follow-up period of 23 months, 205 (79.8 % [205/257]) patients died. For prediction of OS, skeletal muscle index (hazard ratio [HR] = 0.58, p = 0.018) was a significant factor, along with vascular invasion (HR = 1.85, p < 0.001) and neoadjuvant therapy (HR = 0.58, p = 0.011).

Conclusions: A high VAT index and a low skeletal muscle index can be utilized in predicting the occurrence of POPF grade B or C and poor OS, respectively.