Effect of radiotherapy boost and hypofractionation on outcomes in ductal carcinoma in situ
Bibliographic record
Abstract
BACKGROUND: Boost radiotherapy (RT) improves outcomes for patients with invasive breast cancer, but whether this is applicable to patients with pure ductal carcinoma in situ (DCIS) is unclear. This study examined outcomes from whole breast RT, with or without a boost, and the impact of different dose-fractionation schedules in a population-based cohort of women with pure DCIS treated with breast-conserving surgery (BCS). METHODS: Data was analyzed for 957 subjects diagnosed between 1985 and 1999. RT use was analyzed over time. Ten-year Kaplan-Meier local control (LC), breast cancer specific survival (BCSS), and overall survival (OS) were compared using the log-rank test. Cox regression modeling of LC was performed. RESULTS: Median follow-up was 9.3 years. Of the patient cohort 475 (50%) had no RT (NoRT) after BCS, 338 (35%) had RT without a partial breast boost (RTNoB), and 144 (15%) had RT with boost (RT + B). Subjects with risk factors of local recurrence were more likely to receive RT. Subjects receiving adjuvant RT had a trend toward improved LC (15-year LC: NoRT 87%; RTNoB 94%; RT + B 91%; P = .065). Multivariable analysis showed that RT with or without a boost was significantly associated with improved LC (HR, 0.29 and 0.38, respectively, compared with NoRT, P = .025), with no difference associated with a boost or different dose-fractionation schedules. CONCLUSIONS: Adjuvant RT improves local control in patients with DCIS treated with BCS. Hypofractionation is as effective as standard fractionation schedules. Boost RT was not associated with improved LC compared with whole breast RT alone. Cancer 2011. © 2010 American Cancer Society.
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 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".