HER2/neu and Ki-67 expression predict non-invasive recurrence following breast-conserving therapy for ductal carcinoma in situ
Bibliographic record
Abstract
BACKGROUND: Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer that may progress to invasive cancer. Identification of factors that predict recurrence and distinguish DCIS from invasive recurrence would facilitate treatment recommendations. We examined the prognostic value of nine molecular markers on the risks of local recurrence (DCIS and invasive) among women treated with breast-conserving therapy. METHODS: A total of 213 women who were treated with breast-conserving therapy between 1982 and 2000 were included; 141 received breast-conserving surgery alone and 72 cases received radiotherapy. We performed immunohistochemical staining on the DCIS specimen for nine markers: oestrogen receptor, progesterone receptor, Ki-67, p53, p21, cyclinD1, HER2/neu, calgranulin and psoriasin. We performed univariable and multivariable survival analyses to identify markers associated with the recurrence. RESULTS: The rate of recurrence at 10 years was 36% for patients treated with breast-conserving surgery alone and 18% for women who received breast-conserving surgery and radiotherapy. HER2/neu+/Ki-67+ expression was associated with an increased risk of DCIS recurrence, independent of grade and age (HR=3.22; 95% CI: 1.47-7.03; P=0.003). None of the nine markers were predictive of invasive recurrence. CONCLUSION: Women with a HER2/neu/neu+/Ki67+ DCIS have a higher risk of developing DCIS local recurrence after breast-conserving surgery.
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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 | 0.002 |
| 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".