Prognostic value of body adipose tissue parameters in cancer patients treated with immune checkpoint inhibitors
Bibliographic record
Abstract
Objective This study aims to explore the relationship between body adipose tissue characteristics and clinical outcomes in cancer patients receiving immune checkpoint inhibitor (ICI) therapy. Methods We conducted an extensive literature search across three major online databases—Embase, PubMed, and the Cochrane Library—to identify studies examining the link between body adipose tissue and treatment outcomes in cancer patients undergoing ICI therapy, from the inception of each database until February 20, 2024. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale. The primary outcomes analyzed were hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS), as well as odds ratios (ORs) for disease control rate (DCR). Pooled estimates and 95% confidence intervals (CIs) were calculated. Results A total of 23 studies were included, encompassing 2741 cancer patients. The analysis revealed that patients with higher levels of visceral adipose tissue (VAT) exhibited significantly improved OS (HR: 0.72, 95% CI: 0.59–0.89, p < 0.001) and PFS (HR: 0.80, 95% CI: 0.67–0.96, p = 0.015), along with a higher DCR (OR: 1.81, 95% CI: 1.26–2.60, p = 0.001), compared to those with lower VAT levels. Additionally, increased subcutaneous adipose tissue (SAT) levels were associated with significantly better OS (HR: 0.69, 95% CI: 0.58–0.82, p < 0.001) and PFS (HR: 0.82, 95% CI: 0.68–1.00, p = 0.049), and a higher DCR (OR: 1.99, 95% CI: 1.15–3.44, p = 0.014). Elevated total adipose tissue (TAT) levels were also linked to longer OS (HR: 0.73, 95% CI: 0.55–0.97, p = 0.028). However, a higher visceral-to-subcutaneous adipose tissue ratio (VSR) was associated with a shorter OS (HR: 1.43, 95% CI: 1.09–1.87, p = 0.010). No significant relationship was found between TAT (HR: 0.81, 95% CI: 0.54–1.23, p = 0.332) and VSR (HR: 1.20, 95% CI: 0.95–1.51, p = 0.131) with PFS in ICI-treated patients. Conclusion This study highlights the prognostic relevance of VAT and SAT in predicting treatment response and survival outcomes in cancer patients receiving ICIs. These findings suggest that assessments of VAT and SAT should be incorporated into prognostic evaluations for this patient 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".