Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

ABSTRACT:

Background

Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC).

Patients and methods

We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out

SUBMITTER: Keyl J 

PROVIDER: S-EPMC10837775 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature