There is a rapidly growing recognition of the need to improve how we risk stratify patients with malignancies. Whereas traditional metrics based on granular tumor-based characteristics can be used in a validated nomogram to estimate risks of recurrence and cancer-specific mortality, clinicians are increasingly acknowledging the complex interplay between patient-specific factors and tumors that influence overall and disease-specific survival.Although subjective assessments of a patient's fitness for complex therapies, often colloquially described as the ''eyeball test,'' are commonly employed, we have an imperative need at this point to advance the science of risk stratification incorporating objective, quantifiable, and reproduceable metrics of frailty, vulnerability, or resilience across multiple relevant domains. 1 Improving risk stratification in this manner has implications for how we counsel patients regarding their prognosis and treatment election, and also for how we can determine who might benefit from adjuvant supportive therapies to improve their odds of a successful outcome.During the past decade, detailed body composition analyses, specifically with respect to muscle mass, have garnered significant attention as a potential part of the solution to this problem. This approach is driven by the observations that (1) typical aging-related loss of muscle mass appears to be accelerated by both the presence of malignancies and their treatments, and that (2) severe deficiencies in muscle mass and related functional status,