Different cutoff values of the skeletal muscle mass and myosteatosis result in different clinical impact on overall survival in oncology. A subanalysis of a clinical trial.

BACKGROUND

Body composition analysis, particularly the assessment of sarcopenia and myosteatosis, has emerged as a potential prognostic tool in oncology. However, the clinical implication of body composition parameters remains inconsistent, largely due to the variability in cutoff values used across studies. This study examines the influence on prevalence and prognostic influence of different cutoff values for sarcopenia and myosteatosis in patients in a standardized cohort from a large clinical trial (SORAMIC).

METHODS

This study included 179 patients with unresectable liver cancer from the palliative arm of the SORAMIC trial. Skeletal muscle index (SMI) was calculated by measuring the cross-sectional area of skeletal muscle at the third lumbar vertebra (L3) on baseline CT scans. We then applied 14 published cutoff definitions for sarcopenia (SMI) and 7 for myosteatosis (muscle attenuation) to determine their prevalence in this cohort. Cox regression models were used to analyze the relationship between sarcopenia, myosteatosis, and OS.

RESULTS

The prevalence of sarcopenia ranged from 8.9% (Van der Werf et al.) to 69.8% (Lanic et al.). Overall, 3 of the 14 cutoffs [Van Vledder et al. (HR = 1.53, p = 0.03), Coelen et al. (HR = 1.46, p = 0.03), and Derstine et al. (HR = 1.47, p = 0.04)] showed a relevant association with OS. Other cut off values were not associated with OS. The prevalence of myosteatosis varied between 10.1% (Nachit et al.) and 53.1% (Zhang et al.). One of the 7 cutoffs (Chu et al.) demonstrated a relevant association with OS (HR = 1.53, p = 0.03).

CONCLUSION

The large variability in prevalence and prognostic impact observed across different cutoff definitions underscores the urgent need for standardized, cancer-specific cutoff values for SMI and muscle attenuation. Establishing uniform criteria will enhance the reliability and clinical applicability of body composition metrics as prognostic tools in oncology. Further research should focus on refining these cutoffs and validating them across diverse cancer populations.