Associations between Pretreatment Body Composition Features and Clinical Outcomes among Patients with Metastatic Clear Cell Renal Cell Carcinoma Treated with Immune Checkpoint Blockade
Bibliographic record
Abstract
PURPOSE: High body mass index (BMI) may lead to improved immune-checkpoint blockade (ICB) outcomes in metastatic clear cell renal cell carcinoma (mccRCC). However, BMI is a crude body size measure. We investigated BMI and radiographically assessed body composition (BC) parameters association with mccRCC ICB outcomes. EXPERIMENTAL DESIGN: Retrospective study of ICB-treated patients with mccRCC. BMI and BC variables [skeletal muscle index (SMI) and multiple adiposity indexes] were determined using pretreatment CT scans. We examined the associations between BMI and BC variables with ICB outcomes. Therapeutic responses per RECIST v1.1 were determined. We compared whole-transcriptomic patterns with BC variables in a separate cohort of 62 primary tumor samples. RESULTS: 205 patients with mccRCC were included in the cohort (74% were male, 71% were overweight/obese, and 53% were classified as low SMI). High-BMI patients experienced longer overall survival (OS) than normal-weight patients [unadjusted HR, 0.66; 95% confidence interval (CI), 0.45-0.97; P = 0.035]. The only BC variable associated with OS was SMI [unadjusted HR comparing low vs. high SMI 1.65 (95% CI: 1.13-2.43); P = 0.009]. However, this OS association became nonsignificant after adjusting for International Metastatic Renal Cell Carcinoma Database Consortium score and line of therapy. No OS association was seen for adiposity and no BC variable was associated with progression-free survival or radiological responses. Tumors from patients with low SMI displayed increased angiogenic, inflammatory, and myeloid signals. CONCLUSIONS: Our findings highlight the relevance of skeletal muscle in the BMI paradox. Future studies should investigate if addressing low skeletal muscle in metastatic patients treated with ICB can improve survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".