Radiodensities of Skeletal Muscle and Visceral Adipose Tissues Are Prognostic Factors in Clear-Cell Renal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: Body composition may be related to survival in patients with clear-cell renal cell carcinoma (ccRCC), but studies have not simultaneously considered adipose and muscle tissue quantity and radiodensity. METHODS: We analyzed data from 1,022 patients with ccRCC who underwent nephrectomy between 2000 and 2020 at Memorial Sloan Kettering Cancer Center. Skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue indexes (cm2/m2) and radiodensities [Hounsfield units (HU)] were assessed from noncontrast presurgical CT scans; clinical and demographic characteristics were available from the time of surgery. HRs and confidence intervals were estimated for overall (OS) and disease-free survival (DFS) through March 2023 in multivariable models that simultaneously accounted for all body composition measures. RESULTS: The median age of the patients was 58 years, 69% were male, and 90% were White. There were 169 OS events over 8,392 person-years and 253 DFS events over 7,753 person-years of follow-up. In adjusted analyses, poor OS was associated with lower skeletal muscle radiodensity [-10 HU, HR (95% confidence interval), 1.37 (1.05-1.77)] and greater visceral adipose tissue radiodensity [+10 HU, 1.66 (1.06-2.59)], with similar findings for DFS. Poor survival was also associated with greater visceral adipose tissue index [+40 cm2/m2, OS: 1.32 (0.97, 1.79); DFS: 1.33 (1.04, 1.71)]. Associations with skeletal muscle radiodensity were limited to patients with stage 1/2 disease. CONCLUSIONS: Radiodensities of skeletal muscle and visceral adipose tissues may be novel presurgical prognostic factors for patients with ccRCC. IMPACT: The findings underscore the importance of evaluating the full range of body composition features simultaneously in multivariable models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".