Role of Radiotherapy-Induced Malignancies in Patients with Both Breast and Lung Cancer Diagnoses
Bibliographic record
Abstract
Background:Breast and lung cancer are two of the most commonly diagnosed cancers in North America. While patients are living longer with advances in treatment and supportive care, some patients are being diagnosed with a second malignancy. The primary objective in this study was to assess the correlation between the development of an ipsilateral lung cancer or breast cancer, and prior radiation therapy. In addition, we sought to report the survival outcomes of patients in these clinical scenarios. Methods: We conducted a single institution (the Ottawa Hospital Cancer Centre) retrospective review of patients with the diagnoses of both breast and lung cancer treated between 1995 and 2013. Patients were included if they received radiation for a breast primary, and subsequently developed an ipsilateral lung primary, or vice-versa. Data included patient demographics, lifestyle factors, tumor location and subtype, cancer stages, treatment modalities, and survival outcomes. Results: Of 252 patients included in the study, 217 patients developed a breast primary first, with 35 patients developing a lung primary first. Median disease-free survival from the second primary diagnosis was 36 months in breast primary first patients, and 59 months in the lung primary first cohort. There was no significant correlation between the laterality of radiation treatment and side of second primary based on Fisher’s exact test. Conclusions: Our data reveal no association between side of radiation treatment and subsequent cancer development. The benefits of radiotherapy outweigh the risk of radiation-induced primaries. Longer term studies with matched patient cohorts are required to further assess treatment and lifestyle factors that may contribute towards the development of second malignancies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".