Long-Term Outcomes of Invasive Ipsilateral Breast Tumor Recurrences After Lumpectomy in NSABP B-17 and B-24 Randomized Clinical Trials for DCIS
Bibliographic record
Abstract
BACKGROUND: Ipsilateral breast tumor recurrence (IBTR) is the most common failure event after lumpectomy for ductal carcinoma in situ (DCIS). We evaluated invasive IBTR (I-IBTR) and its influence on survival among participants in two National Surgical Adjuvant Breast and Bowel Project (NSABP) randomized trials for DCIS. METHODS: In the NSABP B-17 trial (accrual period: October 1, 1985, to December 31, 1990), patients with localized DCIS were randomly assigned to the lumpectomy only (LO, n = 403) group or to the lumpectomy followed by radiotherapy (LRT, n = 410) group. In the NSABP B-24 double-blinded, placebo-controlled trial (accrual period: May 9, 1991, to April 13, 1994), all accrued patients were randomly assigned to LRT+ placebo, (n=900) or LRT + tamoxifen (LRT + TAM, n = 899). Endpoints included I-IBTR, DCIS-IBTR, contralateral breast cancers (CBC), overall and breast cancer-specific survival, and survival after I-IBTR. Median follow-up was 207 months for the B-17 trial (N = 813 patients) and 163 months for the B-24 trial (N = 1799 patients). RESULTS: Of 490 IBTR events, 263 (53.7%) were invasive. Radiation reduced I-IBTR by 52% in the LRT group compared with LO (B-17, hazard ratio [HR] of risk of I-IBTR = 0.48, 95% confidence interval [CI] = 0.33 to 0.69, P < .001). LRT + TAM reduced I-IBTR by 32% compared with LRT + placebo (B-24, HR of risk of I-IBTR = 0.68, 95% CI = 0.49 to 0.95, P = .025). The 15-year cumulative incidence of I-IBTR was 19.4% for LO, 8.9% for LRT (B-17), 10.0% for LRT + placebo (B-24), and 8.5% for LRT + TAM. The 15-year cumulative incidence of all contralateral breast cancers was 10.3% for LO, 10.2% for LRT (B-17), 10.8% for LRT + placebo (B-24), and 7.3% for LRT + TAM. I-IBTR was associated with increased mortality risk (HR of death = 1.75, 95% CI = 1.45 to 2.96, P < .001), whereas recurrence of DCIS was not. Twenty-two of 39 deaths after I-IBTR were attributed to breast cancer. Among all patients (with or without I-IBTR), the 15-year cumulative incidence of breast cancer death was 3.1% for LO, 4.7% for LRT (B-17), 2.7% for LRT + placebo (B-24), and 2.3% for LRT + TAM. CONCLUSIONS: Although I-IBTR increased the risk for breast cancer-related death, radiation therapy and tamoxifen reduced I-IBTR, and long-term prognosis remained excellent after breast-conserving surgery for DCIS.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".