Adjuvant radiotherapy alone, an effective treatment option for early-stage low- risk breast cancer in women over 50: results from a population based cohort study using a Canadian provincial database
Bibliographic record
Abstract
PURPOSE: Breast conserving surgery (BCS) is the primary treatment for early-stage breast cancer(EBC). Typically, adjuvant endocrine therapy (ET) and radiation therapy (RT) are standard treatments offered for EBC. However, non-compliance and toxicity remain as issues with HT and many patients choose adjuvant RT alone. The benefit of adjuvant RT alone in women with low-risk EBC remains unclear. It is hypothesized that adjuvant RT-alone can improve outcomes in low-risk EBC patients, similar to ET alone or RT + ET combination. METHODS: This population-based study identified women aged 50-80 with T1, N0, Estrogen receptor positive (ER + ve), human epidermal growth receptor-2 negative(Her-2/neu-ve) EBC treated with BCS, followed by adjuvant treatments (RT-alone, ET-alone, or RT + ET combination) from 2010 to 2015. Primary outcomes were recurrence free survival (RFS), overall survival (OS), and breast cancer specific survival (BCSS). RESULTS: :55.0,91.6). Adjuvant treatments were: BCS only 216 (8 %), RT alone 803 (29 %), ET alone 274 (10 %), and RT + ET combination 1517 (54 %). 398 patients (22.2 %) completed 5-years of ET. Compared to BCS alone, there was no statistically significant difference between treatment groups for RFS and BCSS. There were significant difference among the treatment groups for OS compared to BCS alone: Hazard ratio (HR) 0.66 (95 % confidence interval (CI): 0.45 - 0.97) for RT alone, 0.55 (95 % CI: 0.35 - 0.87) for ET alone, and 0.48 (95 % CI: 0.33 - 0.70) for RT + ET combination. Determinants of OS were age, tumor grade, comorbidities, and adjuvant therapy. CONCLUSIONS: Our population-based cohort study showed that there was no statistically significant difference in RFS and BCSS among various adjuvant treatments versus BCS alone. However, RT alone, ET alone and RT + ET combination resulted in a statistically significant improvement in OS compared to BCS alone. Our findings support RT alone can be a viable alternative to ET + RT combination for women over 50 with low-risk EBC. Ongoing studies like EUROPA, REaCT trial and EPOPE will provide more insight into the role of RT alone as a definite treatment option.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".