# Association of sarcopenia with survival and treatment response in brain metastasis of non-small cell lung cancer

## TL;DR

This study shows that muscle volume predicts survival and treatment response in brain metastasis patients with non-small cell lung cancer.

## Contribution

The study identifies sarcopenia as a key predictor of survival and immunotherapy response in brain metastasis surgery patients.

## Key findings

- Temporal muscle volume (TMV) predicts survival after brain metastasis resection.
- Sarcopenia is more strongly linked to postoperative complications than frailty.
- Absence of sarcopenia improves response to immunotherapy in these patients.

## Abstract

Brain metastases are common in non-small cell lung cancer (NSCLC) and affect prognosis and survival. While frailty and sarcopenia are associated with the overall survival in NSCLC the impact on outcome and survival after surgery for brain metastasis is unknown. We therefore analyzed 179 patients (81 women) with NSCLC undergoing resection for brain metastasis between 2011 and 2020 retrospectively. Frailty was measured using the Clinical Frailty Scale (CFS). Temporal Muscle Volume (TMV) was assessed in preoperative T1w MRI. The median age was 63 years. Clinical frailty was present in about 20.6%. Mean follow-up was 11 months. Frailty correlated significantly with age (r = 0.36, p < 0.001) and smaller TMV (r=-0.24, p = 0.002). However, only measurement of TMV predicted impaired survival (median OS 34.5 vs. 10.3 months, p < 0.001). Physical performance after surgery was negatively affected by frailty (r=-0.72, p < 0.001) and positively by TMV (r = 0.2, p = 0.038). Major postoperative complications were more strongly associated with sarcopenia rather than frailty. Treatment response towards immunotherapy improved in the absence of sarcopenia (B = 2.48, p = 0.031). TMV is a predictor for survival after resection of brain metastasis and an indicator of treatment response to immunotherapy in patients with NSCLC. Accounting for sarcopenia in surgical decision making could improve patient selection for different treatment modalities.