Estimation of Annual Secondary Lung Cancer Deaths Using Various Adjuvant Breast Radiotherapy Techniques for Early-Stage Cancers
Bibliographic record
Abstract
PURPOSE: Secondary lung cancer (SLC) can offset the benefit of adjuvant breast radiotherapy (RT), and risks compound sharply after 25 to 30 years. We hypothesized that SLC risk is mainly an issue for early-stage breast cancer, and that lives could be saved using different RT techniques. PATIENTS AND METHODS: The SEER database was used to extract breast patient age, stage survival, and radiotherapy utilization over time and per stage and to assess the factors associated with increased SLC risk with a multivariable competing risk Cox model. The number of SLC was calculated using the BEIR model modified with patient survival, age, and use of RT from the SEER database. Stage distribution and number of new breast cancer cases were obtained from the NAACCR. Mean lung dose for various irradiation techniques was obtained from measurement or literature. RESULTS: Out of the 765,697 non-metastatic breast cancers in the SEER database from 1988 to 2012, 49.8% received RT. RT significantly increased the SLC risk for longer follow-up (HR=1.58), early stage including DCIS, stage I and IIA (HR = 1.11), and younger age (HR=1.061) (all p<0.001). More advanced stages did not have significantly increased risk. In 2019, 104,743 early-stage breast patients received radiotherapy, and an estimated 3,413 will develop SLC (3.25%) leading to an excess of 2,900 deaths (2.77%). VMAT would reduce this mortality by 9.9%, hypofractionation 26 Gy in five fractions by 38.8%, a prone technique by 70.3%, 3D-CRT APBI by 43.3%, HDR brachytherapy by 71.1%, LDR by 80.7%, and robotic 4π APBI by 85.2%. CONCLUSIONS: SLC after breast RT remains a clinically significant issue for early-stage breast cancers. This mortality could be significantly reduced using a prone technique or APBI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".