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

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/m(2); 71% female, 55% self-identified as Black and 96% as non-Hispanic ethnicity). The DSC indicates excellent overlap (>0.944) or agreement between the two measurement methods for muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) CSA and all tissue densities at L1 and L2. The DSC was lowest for intermuscular adipose tissue (IMAT) CSA at L1 (0.889) and L2 (0.919). CONCLUSION: The use of a fully automated neural network to analyze body composition at L1 and L2 in patients with lung cancer is valid for measuring skeletal muscle and adipose tissue CSA and densities when compared to a reference program. Further validation in a more diverse sample and in different disease and health states is warranted to increase the generalizability of the test program at L1 and L2.