Abstract 2298: Is smoking a risk factor for second primary lung cancer
Bibliographic record
Abstract
Abstract Background: Lung cancer (LC) survivors in the U.S. are increasing in number, with 5-year survival rates improving by 26% over the past decade. Although LC survivors are at high risk of developing a second primary lung cancer (SPLC), risk factors for SPLC have not been established and the impact of tobacco smoking remains controversial. In this study, we examined risk factors for SPLC among participants in the Multiethnic Cohort (MEC) study, validated our findings with two epidemiologic cohorts–the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and the European Prospective Investigation into Cancer and Nutrition (EPIC)–and evaluated the impact of smoking cessation on SPLC risk. Methods: We analyzed data from 7,299 initial primary lung cancer (IPLC) cases in MEC who were diagnosed from 1993-2017. Incident IPLC and SPLC were identified via linkage to SEER registries, with SPLC defined by Martini and Melamed criteria. Baseline smoking data were obtained at the time of enrollment (1993-1996) and updated with 10-year follow-up data close to IPLC diagnosis, if available. Fine-Gray regression was used to take into account competing risks and to evaluate the associations between risk factors and SPLC, adjusting for age at IPLC diagnosis and IPLC histology and stage. We conducted validation studies with PLCO (N=3,423 LC patients) and EPIC (N=4,605 LC patients) and evaluated the combined effects of risk factors from all three cohorts using meta-analysis. Results: Among 7,299 MEC participants with IPLC, 167 (2.3%) developed a SPLC. Fine-Gray regression analyses identified several factors that were significantly associated with SPLC risk (P<0.05), which included smoking pack-years (HR 1.12 per 10 pack-years (PY); P=0.004) and smoking intensity (HR 1.21 per 10 cigarettes per day (CPD); P=0.017). Individuals who met the U.S. Preventative Services Task Force's (USPSTF) screening criteria (i.e., aged 55-80, smoked ≥30 PY, and ≤15 years since smoking cessation) at the time of IPLC had a 68% increase in SPLC risk (HR 1.68; P=0.001). Validation studies with PLCO and EPIC showed consistent results; the combined effects based on meta-analysis showed a HR 1.15 per 10 PY (Pmeta=0.022) for smoking pack-years, HR 1.18 per 10 CPD (Pmeta=6.0x10-4) for smoking intensity, and HR 1.70 (Pmeta = 1.9x10-5) for meeting the USPSTF criteria. Subset analysis of MEC participants (N=156) who were current smokers at baseline, had 10-year follow-up smoking data, and were diagnosed with IPLC between baseline and 10-year follow-up showed that smoking cessation was associated with a reduced risk of SPLC (HR=0.25; P=0.005). Conclusions: Smoking is a risk factor for SPLC among LC patients and the USPSTF criteria can potentially aid in identifying those at high risk of SPLC. Smoking cessation may reduce SPLC risk after IPLC diagnosis. Further analysis is required to stratify SPLC risk based on comprehensive risk factors and identify LC survivors at high risk of SPLC for CT screening. Citation Format: Jacqueline V. Aredo, Sophia J. Luo, Rebecca Gardner, Thomas P. Hickey, Thomas L. Riley, Lynne R. Wilkens, Loic Le Marchand, Christopher I. Amos, Rayjean J. Hung, Mattias Johansson, Iona Cheng, Heather A. Wakelee, Summer S. Han. Is smoking a risk factor for second primary lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2298.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".