Increased Risk of Locoregional Recurrence for Women With T1-2N0 Triple-Negative Breast Cancer Treated With Modified Radical Mastectomy Without Adjuvant Radiation Therapy Compared With Breast-Conserving Therapy
Bibliographic record
Abstract
PURPOSE: To evaluate the risk of locoregional recurrence (LRR) associated with locoregional treatment of women with primary breast cancer tumors negative for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (triple-negative breast cancer [TNBC]). PATIENTS AND METHODS: Patients diagnosed with TNBC were identified from a cancer registry in a single institution (n=768). LRR-free survival was estimated using Kaplan-Meier analysis. The Cox proportional hazards regression model was used to determine risk of LRR on the basis of locoregional management: breast-conserving therapy (BCT; ie, lumpectomy and adjuvant radiation therapy [RT]) and modified radical mastectomy (MRM) in the TNBC population and T1-2N0 subgroup. RESULTS: At a median follow-up of 7.2 years, 77 patients (10%) with TNBC developed LRR. Five-year LRR-free survival was 94%, 85%, and 87% in the BCT, MRM, and MRM + RT groups, respectively (P < .001). In multivariate analysis, MRM (compared with BCT), lymphovascular invasion and lymph node positivity were associated with increased LRR. Conversely, adjuvant chemotherapy was associated with decreased risk of LRR. For patients with T1-2N0 tumors, 5-year LRR-free survival was 96% and 90% in the BCT and MRM groups, respectively (P = .027), and MRM was the only independent prognostic factor associated with increased LRR compared with BCT (hazard ratio, 2.53; 95% CI, 1.12 to 5.75; P = .0264). CONCLUSION: Women with T1-2N0 TNBC treated with MRM without RT have a significant increased risk of LRR compared with those treated with BCT. Prospective studies are warranted to investigate the benefit of adjuvant RT after MRM in TNBC.
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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 | 0.003 |
| 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) | 0.001 | 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".