BMI correlation with outcomes in patients with head and neck cancer undergoing immunotherapy: A comprehensive review and meta-analysis.
Bibliographic record
Abstract
6021 Background: Currently, the landscape of treating advanced malignancies has undergone a transformative shift with the advent of immunotherapy employing immune checkpoint inhibitors (ICIs). Notably, several ICIs have emerged as promising therapeutic modalities for individuals with head and neck cancer (HNC). An emerging body of evidence implies a plausible link between body mass index (BMI) and the effectiveness of ICIs in the broader context of cancer patients. Nevertheless, the specific correlation within the subset of head and neck cancer patients undergoing immunotherapeutic interventions remains unclear and warrants meticulous investigation. Methods: PubMed, Web of Science, and Google Scholar databases were searched extensively for records published until January 2024. Full-text articles aligned with the research objective were included, while records published in English, case reports, reviews, editorials, and studies reporting immunotherapy combined with other cancer therapies were excluded. The data required for review and analysis was abstracted in Excel files by two independent reviewers. Additionally, statistical analyses were performed using the Review Manager software, and methodological quality was assessed using the Newcastle Ottawa scale. Results: Only six studies were eligible for review and analysis. A subgroup analysis of data from these studies showed that obese HNC patients on immunotherapy had significantly better overall survival (OS) rates than non-obese patients (HR: 0.51; 95% CI: 0.29 – 0.93; p=0.03). However, the progression-free survival (PFS) was statistically similar between obese and non-obese patients (HR: 0.72; 95% CI: 0.39 – 1.33; p=0.30). In addition, when BMI was stratified as either low or high, no significant difference was observed in the OS and PFS of HNC patients (HR: 0.99; 95% CI: 0.59 – 1.66; p=0.97 and HR: 0.93; 95% CI: 0.61 – 1.41; p=0.42, respectively). Similarly, the statistical analyses showed that overweight patients have similar OS and PFS as patients with normal BMI (HR: 0.53; 95% CI: 0.15 – 1.92; p=0.33 and HR: 0.55; 95% CI: 0.20 – 1.52; p = 0.25, respectively). In contrast, underweight patients demonstrated poor OS and PFS (HR: 2.56; 95% CI: 1.29 – 5.12; p=0.008 and HR: 2.76; 95% CI: 1.17 – 6.52; p=0.02, respectively). Conclusions: Obese HNC patients on immunotherapy tend to have improved OS than non-obese patients, while underweight patients have worse clinical prognoses than those with normal or above BMI.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".