Radiation-Induced Sarcomas of the Breast: A Review of a 20-Year Single-Center Experience
Bibliographic record
Abstract
Background Radiation-induced sarcomas (RISs) are histologically proven sarcomas within or around a previously irradiated site, per Cahan's criteria. RIS incidence is higher in breast cancer compared to other solid cancers and the prognosis remains poor given limited treatment options. This study aimed to review 20-year experience with RISs at a large tertiary care center. Methodology Using our institutional cancer registry database, we included patients meeting Cahan's criteria diagnosed between 2000 and 2020. Patient demographics, oncologic treatment, and oncologic outcomes data were collected. Descriptive statistics were used to describe demographic data. Oncologic outcomes were assessed using the Kaplan-Meier method. Results A total of 19 patients were identified. The median age at RIS diagnosis was 72 years (range = 39-82 months), and the median latency period for the development of RIS was 112 months (range = 53-300 months). All patients underwent surgery, three patients received systemic therapy, and six patients received re-irradiation as salvage treatment. The median follow-up time was 31 months (range = 6-172 months) from the diagnosis of RIS. Overall, five patients had local recurrence, and one patient developed distant metastases. The median time to progression was seven months (range = 4-14 months). The progression-free survival (95% confidence interval (CI)) at two years was 56.1% (37.4-84.4%). At follow-up two years after the diagnosis of sarcoma, the overall survival (95% CI) was 88.9% (75.5-100%). Conclusions While breast RIS remains rare, when managed in a large tertiary care center, overall survival outcomes appear favorable. A significant proportion of patients recur locally after maximal treatment and require salvage therapy to improve outcomes. These patients should be managed in high-volume centers where multidisciplinary expertise is available.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".