Does Pre-Checkpoint Inhibitor Sarcopenia, Visceral, or Subcutaneous Fat Predict Survival in Non-Small Cell Lung Cancer Patients? 

Abstract

Background The role of skeletal muscle area (SMA), subcutaneous, and visceral fat area (SFA and VFA) in cancer survivorship is inconsistent. We investigated the prognostic significance of the skeletal muscle index, subcutaneous and visceral fat area specifically via CT scans around the time of checkpoint inhibitor therapy in patients with non-small cell lung cancer (NSCLC). Methods CT scans of patients within 60 days of checkpoint inhibitor medication use were utilized to assess skeletal muscle area visceral fat index (VFA), subcutaneous fat area (SFA), and visceral and subcutaneous fat ratio corrected by patients’ height in meters squared. Skeletal muscle and fat areas at L3 were read by a single trained reader using TeraRecon software. Survival (in days) was calculated from the first CT scan to the death date. Survival analysis was performed using a Cox proportional hazards model to evaluate the association between body composition metrics and patient survival outcomes at one and two years. Multiple regression models were utilized with all CT parameters in a single model Results With 46 patients included in the analysis, our results did not show a significant relationship between any parameters assessed (SMA, SFA, VFA, visceral and subcutaneous fat ratio, and days from checkpoint inhibitor therapy to initial scan) and cancer survivorship in either female or male patients. Discussion Our results demonstrate no significant relationship between the parameters assessed and NSCLC survivorship in either male or female patients, which is consistent with small studies. However, meta-analyses of multiple studies support the association of pre-immunotherapy with reduced survival. Conclusion Pre-treatment Sarcopenia, SFA, and VFA do not appear to predict cancer survival on checkpoint inhibitors in small studies. Larger studies are needed to explore the utility of CT scan-derived SMI and fat area in predicting checkpoint inhibitor benefits in patients with lung cancer.