Skeletal muscle density predicts prognosis in patients with metastatic renal cell carcinoma treated with targeted therapies
Bibliographic record
Abstract
BACKGROUND: Studies have shown that skeletal muscle and adipose tissue are linked to overall survival (OS) and progression-free survival (PFS). Because targeted therapies have improved the outcome in patients with metastatic renal cell carcinoma (mRCC), new prognostic parameters are required. The objective of the current study was to analyze whether body composition parameters play a prognostic role in patients with mRCC. METHODS: Adipose tissue, skeletal muscle, and skeletal muscle density (SMD) were assessed with computed tomography imaging by measuring cross-sectional areas of the tissues and mean muscle Hounsfield units (HU). A high level of mean HU indicates a high SMD and high quality of muscle. OS and PFS were estimated using the Kaplan-Meier method and compared with the log-rank test. The multivariable Cox proportional hazards model was adjusted for Heng risk score and treatment. RESULTS: In the 149 patients studied, the median OS was 21.4 months and was strongly associated with SMD; the median OS in patients with low SMD was approximately one-half that of patients with high SMD (14 months vs 29 months; P = .001). After adjustment for Heng risk score and treatment, high SMD was associated with longer OS (hazards ratio, 1.85; P = .004) and longer PFS (hazards ratio, 1.81; P = .002). Adding SMD will separate the intermediate-risk and favorable-risk groups into 3 groups, with different median OS periods ranging from 8 months (95% confidence interval [95% CI], 6 months-12 months) for an intermediate-risk Heng score/low SMD to 22 months (95% CI, 14 months-27 months) for an intermediate-risk Heng score/high SMD and a favorable-risk Heng score/low SMD to 35 months (95% CI, 24 months-43 months) for a favorable-risk Heng score/high SMD. CONCLUSIONS: High muscle density appears to be independently associated with improved outcome and could be integrated into the prognostic scores thereby enhancing the management of patients with mRCC.
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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".