ArticleCancer medicine2026
Pragmatic Screening for Sarcopenia in Non-Small Cell Lung Cancer: Development and Internal Validation of a Prospective Risk Model Integrating T12 CT Indices and Routine Clinical Variables.
Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundSarcopenia 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.
methodsIn 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).
resultsT12-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.
conclusionT12 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.