Integrated impact of multiple body composition parameters on overall survival in gastrointestinal or genitourinary cancers: A descriptive cohort study.

BACKGROUND: This study aimed to evaluate if combining low muscle mass with additional body composition abnormalities, such as myosteatosis or adiposity, could improve survival prediction accuracy in a large cohort of gastrointestinal and genitourinary malignancies.

METHODS: In total, 2015 patients with surgically-treated gastrointestinal or genitourinary cancer were retrospectively analyzed. Skeletal muscle index, skeletal muscle radiodensity, and visceral/subcutaneous adipose tissue index were determined. The primary outcome was overall survival determined by hospital records. Multivariate Cox hazard models were used to identify independent predictors for poor survival. C-statistics were assessed to quantify the prognostic capability of the models with or without incorporating body composition parameters.

RESULTS: Survival curves were significantly demarcated by all 4 measures. Skeletal muscle radiodensity was associated with non-cancer-related deaths but not with cancer-specific survival. The survival outcome of patients with low skeletal muscle index was poor (5-year OS; 65.2%), especially when present in combination with low skeletal muscle radiodensity (5-year overall survival; 50.2%). All examined body composition parameters were independent predictors of lower overall survival. The model for predicting overall survival without incorporating body composition parameters had a c-index of 0.68 but increased to 0.71 with the inclusion of low skeletal muscle index and 0.72 when incorporating both low skeletal muscle index and low skeletal muscle radiodensity/visceral adipose tissue index/subcutaneous adipose tissue index.

CONCLUSION: Patients exhibiting both low skeletal muscle index and other body composition abnormalities, particularly low skeletal muscle radiodensity, had poorer overall survival. Models incorporating multiple body composition prove valuable for mortality prediction in oncology settings.