Prognostic Value of Sarcopenia in Elderly Patients with Metastatic Non-Small-Cell Lung Cancer Undergoing Radiotherapy
Bibliographic record
Abstract
Background: Sarcopenia, a syndrome characterized by age-related loss of muscle mass and function, lacks universally accepted diagnostic criteria, particularly for its role as a prognostic factor in elderly patients with non-small-cell lung cancer (NSCLC). This study aimed to evaluate the prognostic significance of sarcopenia, assessed by psoas muscle size on baseline CT scans, in patients over 70 years of age with metastatic NSCLC. Methods: We retrospectively analyzed 85 elderly patients undergoing palliative radiation therapy between August 2022 and July 2024. Using morphometric analysis of psoas size, we investigated its correlation with overall survival (OS) and progression-free survival (PFS). Results: Our results showed that decreased psoas size was significantly associated with shorter OS and PFS, with median OS of 10 months and PFS of 4 months in sarcopenic patients compared to longer survival times in non-sarcopenic patients. Median survival of non-sarcopenic vs. sarcopenic patients was 21 ± 7 months (muscle area > median) versus 5 ± 2.3 months (muscle area < median). Multivariate analysis confirmed that psoas size, along with ECOG performance status and treatment of primary NSCLC, was a significant predictor of survival. Discussion: These findings suggest that psoas muscle size is a valuable prognostic marker for elderly NSCLC patients, potentially guiding treatment decisions and patient management. Further research is needed to validate these results and refine prognostic models for this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".