Surgical Treatment After Neoadjuvant Systemic Therapy in Young Women With Breast Cancer
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate eligibility for breast conserving surgery (BCS) pre- and post-neoadjuvant systemic therapy (NST), and trends in the surgical treatment of young breast cancer patients. BACKGROUND: Young women with breast cancer are more likely to present with larger tumors and aggressive phenotypes, and may benefit from NST. Little is known about how response to NST influences surgical decisions in young women. METHODS: The Young Women's Breast Cancer Study, a multicenter prospective cohort of women diagnosed with breast cancer at age ≤40, enrolled 1302 patients from 2006 to 2016. Disease characteristics, surgical recommendations, and reasons for choosing mastectomy among BCS-eligible patients were obtained through the medical record. Trends in use of NST, rate of clinical and pathologic complete response, and surgery were also assessed. RESULTS: Of 1117 women with unilateral stage I-III breast cancer, 315 (28%) received NST. Pre-NST, 26% were BCS eligible, 17% were borderline eligible, and 55% were ineligible. After NST, BCS eligibility increased from 26% to 42% (P < 0.0001). Among BCS-eligible patients after NST (n = 133), 41% chose mastectomy with reasons being patient preference (53%), BRCA or TP53 mutation (35%), and family history (5%). From 2006 to 2016, the rates of NST (P = 0.0012), clinical complete response (P < 0.0001), and bilateral mastectomy (P < 0.0001) increased, but the rate of BCS did not increase (P = 0.34). CONCLUSION: While the proportion of young women eligible for BCS increased after NST, many patients chose mastectomy, suggesting that surgical decisions are often driven by factors beyond extent of disease and treatment response.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".