Defining Sarcopenia in Oncology by CT-Based Muscle Mass: The Clinical and Research Consequences of a Diagnostic Surrogate

Sarcopenia is increasingly invoked as a determinant of treatment-related toxicity, perioperative morbidity, treatment intolerance, and survival in oncology; however, contemporary international consensus frameworks define sarcopenia as a multidimensional neuromuscular syndrome centered on impaired muscle strength, physical performance, and muscle quality, whereas most oncologic studies operationalize sarcopenia using computed tomography (CT)-derived skeletal muscle mass alone. In this context, muscle quantity is effectively employed as a diagnostic surrogate for a function-centered syndrome. CT-defined skeletal muscle depletion—more precisely described as myopenia—remains a reproducible and clinically informative structural biomarker, yet defining sarcopenia by muscle mass alone aggregates biologically heterogeneous phenotypes, including neuromuscular dysfunction, inflammation-driven cachexia, and substrate-related malnutrition. Such surrogate-based definitions contribute to variable prevalence estimates, inconsistent prognostic associations, and interpretive instability across studies. Clinically, reliance on CT-based muscle mass as a surrogate for sarcopenia may influence chemotherapy dosing, perioperative risk stratification, and supportive care allocation without direct assessment of neuromuscular function; in research settings, mass-based definitions may dilute treatment effects in exercise or nutritional trials and complicate meta-analytic synthesis by conflating structural and functional constructs. This analysis does not question the value of radiologic muscle assessment but argues that CT-derived muscle mass should be recognized as a structural biomarker within a multidimensional framework rather than as a standalone diagnostic surrogate for sarcopenia. A tiered, oncology-adapted approach integrating functional assessment, muscle quality, and relevant metabolic context may enhance risk discrimination, improve trial design, and strengthen translational precision in supportive oncology.