External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.

ABSTRACT:

Objectives

Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images.

Methods

Expert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2535 patients with abdominal cancer. DLNN performance was eva

SUBMITTER: van Dijk DPJ 

PROVIDER: S-EPMC11573127 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature