Risk of primary lung cancer after breast cancer radiotherapy: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: Epidemiological studies have shown that the risk of secondary malignancies may increase by radiotherapy. Lung cancer is the most important long-term complication of breast cancer radiotherapy.Methods: Major electronic databases including Scopus, Web of Science, and MEDLINE were searched. All cohort studies that investigated the association between radiotherapy for breast cancer and risk of primary lung, bronchus and trachea cancers conducted until March 2021 were included. The study participants were evaluated regardless of their age and ethnicity. The Newcastle-Ottawa Scale was used to assess the quality of the studies. The designated effects were risk ratio (RR). The random-effects model was used to estimate the average effects.Results: Fifteen studies including 1,640,247 women with primary breast cancer were identified of which 937,151 had not received radiotherapy and 703,096 subjects had received radiotherapy. In general, there was no significant association between breast cancer radiotherapy and lung cancer based on 10 studies (RR=0.95, 95%CI: 0.87-1.02, P=0.15), There was no association between breast cancer radiotherapy and lung, bronchus, and trachea cancers either based on 5 studies (RR=0.98, 95%CI: 0.93-1.02, P=0.32).Conclusion: Radiotherapy for breast cancer is not associated with an excess risk of lung cancer. Due to the limited number of studies, further research about lung doses from breast cancer radiotherapy varied substantially worldwide, is suggested to the risk of lung cancer after breast cancer radiotherapy.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".