Validation of skeletal muscle and adipose tissue measurements using a fully automated body composition analysis neural network versus a semi-automatic reference program with human correction in patients with lung cancer.

ABSTRACT:

Rationale and objectives

To validate skeletal muscle and adipose tissues cross sectional area (CSA) and densities between a fully automated neural network (test program) and a semi-automated program requiring human correction (reference program) for lumbar 1 (L1) and lumbar 2 (L2) CT scans in patients with lung cancer.

Materials and methods

Agreement between the reference and test programs was measured using Dice-similarity coefficient (DSC) and Bland-Altman plots with limits of agreement within 1.96 standard deviation.

Results

A total of 49 L1 and 47 L2 images were analyzed from patients with lung cancer (mean age = 70.51 ± 9.48 years; mean BMI = 27.45 ± 6.06 kg/m²; 71% female, 55% self-identified as Black and 96% as non-Hispanic ethnicity). The DSC indica

SUBMITTER: Byrne CA 

PROVIDER: S-EPMC9816970 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature