Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.

ABSTRACT:

Introduction

The importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.

Materials and methods

A total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebra and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. Th

SUBMITTER: Delrieu L 

PROVIDER: S-EPMC11440831 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature