Risk of thyroid carcinoma in a female population after radiotherapy for breast carcinoma
Bibliographic record
Abstract
BACKGROUND: There is increasing concern regarding the risk of developing a second primary tumor in adjacent organs as a result of scattered radiation among patients who have undergone radiotherapy (RT) for breast carcinoma. Previous studies have focused mainly on the possible increase in the incidence of contralateral breast carcinoma. To the authors' knowledge, the risk of thyroid carcinoma among these women has not been explored to date. METHODS: In this population-based, retrospective cohort study, the authors identified 194,798 women who were diagnosed with invasive breast carcinoma (exclusive of those with distant metastasis) between 1973 and 1993, and ascertained subsequent cases of thyroid carcinoma utilizing data from the Surveillance, Epidemiology, and End Results (SEER) program of the U.S. National Cancer Institute. Poisson regression was used to calculate the age-standardized incidence ratio (SIR) of thyroid carcinoma and to model the influence of RT on the relative risk (RR) between the RT cohort (48,495 women) and the non-RT cohort (146,303 women). RESULTS: A total of 28 women in the RT cohort and 112 women in the non-RT cohort subsequently developed thyroid carcinoma. The distribution of thyroid carcinoma histologies in both the RT cohort and the non-RT cohort was similar to that in the female general population. Overall, there was no significant increase in the risk of thyroid carcinoma in either the RT cohort or the non-RT cohort compared with the general population; the SIR was 1.1 (95% confidence interval [95% CI], 0.8-1.6) for the RT cohort and 1.2 (95% CI, 1.0-1.4) for the non-RT cohort. When the RT cohort was compared with the non-RT cohort, the RR of thyroid carcinoma was 1.0 (95%CI, 0.7-1.5). CONCLUSIONS: The risk of radiation-associated thyroid carcinoma after initial RT for breast carcinoma was so low as to be undetectable in the current large population-based study. Continued monitoring of these women will be required to document that these findings are maintained with even longer follow-up periods. However, with 10,895 women having been followed for > 10 years at the time of last follow-up in the current study, these findings should be reassuring to women considering RT for their breast carcinoma. Therefore, women who have received RT for breast carcinoma require no special surveillance for their thyroid gland. Furthermore, previous breast radiation need not be a factor in determining the optimal management of thyroid nodules arising in women who received RT for breast carcinoma.
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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".