Purpose: This study investigates the feasibility of computed tomography (CT)-defined sarcopenia assessment using a prediction model for estimating the cross-sectional area (CSA) of skeletal muscle (SM) in CT scans at the third lumbar vertebra (L3), using measures at the third cervical level (C3) in a predominantly overweight population with head and neck cancer (HNC).
Methods: Analysis was conducted on adult patients with newly diagnosed HNC who had a diagnostic positron emission tomography-CT scan. CSA of SM in CT images was measured at L3 and C3 in each patient, and a predictive formula developed using fivefold cross-validation and linear regression modelling. Correlation and agreement between measured CSA at L3 and predicted values were evaluated using intraclass correlation coefficients (ICC) and Bland-Altman plot. The model's ability to identify sarcopenia was investigated using Cohen's Kappa (k).
Results: A total of 109 patient scans were analysed, with 64% of the cohort being overweight or obese. The prediction model demonstrated high level of correlation between measured and predicted CSA measures (ICC 0.954, r = 0.916, p < 0.001), and skeletal muscle index (SMI) (ICC 0.939, r = 0.883, p < 0.001). Bland-Altman plot showed good agreement in SMI, with mean difference (bias) = 0.22% (SD 8.65, 95% CI - 3.35 to 3.79%), limits of agreement (- 16.74 to 17.17%). The model had a sensitivity of 80.0% and specificity of 85.0%, with moderate agreement on sarcopenia diagnosis (k = 0.565, p = 0.004).
Conclusion: This model is effective in predicting lumbar SM CSA using measures at C3, and in identifying low SM in a predominately overweight group of patients with HNC.
Keywords: Body composition; Computed tomography; Head and neck cancer; Sarcopenia; Skeletal muscle.
© 2022. The Author(s).