Abstract
ABSTRACT
Background
Sarcopenia or low muscle mass is common in non‐small cell lung cancer and predicts poorer outcomes, but its assessment typically relies on CT‐based skeletal muscle index at the L3 vertebral level. Standard chest CTs often omit the L3 level, leaving many patients without a straightforward muscle mass measure, so sarcopenia frequently goes undetected. We therefore evaluated whether thoracic CT measurements could reliably substitute for L3 SMI and developed a simple, accurate clinical tool to identify patients at risk of CT‐defined low muscle mass.
Methods
In retrospective (
n
= 192) and prospective (
n
= 177) cohorts of NSCLC patients, cross‐sectional muscle area was quantified on CT at L3, T12, and T4. Deming regression and Bland–Altman analysis were used to assess correlations and agreement between T4‐ or T12‐derived SMI and reference L3 SMI. Candidate clinical predictors were selected based on univariate associations and clinical plausibility, and machine‐learning methods identified five routine variables, which were combined into the Lung Cancer Patients' Sarcopenia Risk Model (LSRM). Model performance was evaluated in terms of discrimination, calibration, and risk stratification and compared with established clinical indices like BMI and the advanced lung cancer inflammation index (ALI).
Results
T12‐derived SMI showed a strong correlation and minimal bias versus L3, outperforming T4‐derived SMI. A conversion equation was established to estimate L3 SMI from T12. The LSRM, incorporating age, body mass index, carcinoembryonic antigen, C‐reactive protein, and lymphocyte count, demonstrated good discrimination, satisfactory calibration, and clear three‐tier risk stratification. It outperformed BMI and ALI in identifying CT‐defined low muscle mass.
Conclusion
T12 measurements on routine chest CT can replace L3 for muscle assessment. The LSRM provides a practical bedside tool for screening patients at risk of CT‐defined low muscle mass in NSCLC without additional imaging, supporting earlier risk identification and integration into routine care.