Impact of tobacco smoking on radiotherapy outcomes in 1875 HPV-positive oropharynx cancer patients.
Bibliographic record
Abstract
6047 Background: This study investigates the impact of smoking on radiotherapy (RT) outcome and survival in a population based cohort of HPV+ oropharynx cancer (OPC). Methods: We identified all OPC with positive p16 staining from 2007 –2015 who received curative IMRT according to approved guidelines in two oncology groups. Associations between smoking and locoregional control (LRC) and distant control (DC) were estimated by competing risk regression. Disease free survival (DFS) and overall survival (OS) were estimated by proportional-hazards regression model. Multivariable analyses (MVA) adjusted for age, gender, performance status (PS), T- and N-category, and treatment regimen. Results: A total of 1875 patients were included. Median age was 59.2 [31.3-86.8]; 79% (1481) were males; 96% (1651) had PS <2; 71% (1337) received concurrent chemo-radiotherapy (CRT) +/- hypoxic modification (Nimorazole); and 538 (29%) received RT alone +/- Nimorazole. 23% (425) were current smokers (at time of diagnosis) and 46% (853) were ex-smokers. Median smoking pack-years (PY) was 20.1 in the total cohort, and higher in current smokers vs ex-smokers (38 vs 20 PY, p<0.001). 63% of current smokers had >30 PYs. Median follow-up was 4.8 years. Actuarial 5-year univariate analysis showed that current smokers had a reduced probability of LRC (85% vs 92%, p=0.002), DC (88% vs 92%, p=0.046), DFS (69% vs 84%, p<0.001), and OS (73% vs 88%, p=<0.001) compared to never-smokers (n=567). Outcomes for ex-smokers and never-smokers were similar. In MVA current smoking retained strong independent significance for LRC (HR 1.73 [1.18-2.53]), DFS (1.79 [1.35-2.36]) and OS (2.06 [1.49-2.84]). However, DC was not significantly influenced by current smoking status (1.27 [0.83-1.95]). Similar observations were found for >30PY. Conclusions: Heavy lifetime and current smoking negatively impacts LRC and survival in HPV+ OPC. While smoking mediated hypoxia could interfere with RT efficacy, a putative impact on tumor biology remains uncertain in the absence of a detriment to distant metastasis risk. The findings support encouraging smoking cessation to improve therapeutic efficacy of RT and to avoid excess smoking related mortality.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".