Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort

Objectives Deep learning neural network (DLNN)-based tools can automate body composition analysis for cancer cachexia research. We aimed to evaluate a DLNN tool trained on a European population in Chinese cancer patients. Methods Computer tomography (CT) images at the 3rd lumbar vertebral (L3) level of Chinese gastric cancer patients were retrospectively collected. An externally validated DLNN tool (Mosamatic) was used to segment skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Manual segmentation was performed using SliceOmatic software (TomoVision, version 5.0). Geometric similarity between automated and manual segmentation and measure reliability was assessed. Results The cohort comprised 203 patients with a median body mass index (BMI) of 22.2 kg/m2, and 604 CT images at L3 were collected. The median DSC (interquartile range) of skeletal muscle, VAT and SAT were 0.973 (0.961-0.980), 0.980 (0.964-0.989), and 0.967 (0.945-0.977), respectively. The median Lin’s Concordance Correlation Coefficient (CCC) for skeletal muscle area (0.983), VAT area (1.000), SAT area (0.998), skeletal muscle radiation attenuation (0.995), VAT radiation attenuation (0.994) and SAT radiation attenuation (0.997) demonstrated excellent reliability. Low BMI (<18.5 kg/m2) and ascites impaired agreement between the two methods. The automated method showed high diagnostic concordance with manual segmentation for sarcopenia (κ = 0.843, P < 0.001) and myosteatosis (κ = 0.946, P < 0.001). Conclusions The Mosamatic tool display excellent generalizability to analyze body compositions in Chinese gastric cancer patients and can facilitate cachexia research. Advances in knowledge The Mosamatic tool displayed excellent generalizability without recalibration to analyze body composition on the 3rd lumbar vertebral CT images in Chinese gastric cancer patients.