Cell Proliferation Measured by Ki67 Staining and Correlation to Clinicopathological Parameters in Operable Breast Carcinomas from Vietnamese and Swedish Patients
Bibliographic record
Abstract
Background: Cell proliferation measured by Ki67 has recently been shown to be a prognostic and predictive factor in breast cancer. The aim of this study was to compare cell proliferation determined by Ki67 expression with different clinicopathologic parameters among Vietnamese and Swedish women with breast cancer. Materials and Methods:The study was based on series of breast cancer from Vietnamese patients treated in the National Cancer Hospital in Hanoi, Vietnam and from Swedish patients treated in the Karolinska Hospital, Stockholm, Sweden. Cell proliferation was measured by Ki67 staining in an automated procedure and was expressed as percentage of stained tumor cell nuclei. Results:The distribution and mean of Ki67 indices from Vietnamese patients were similar to those estimated from Swedish patients, 27.7% (±17.1%) vs. 26.9% (±23.1%). There were no differences between the two series of patients with respect to proliferation index and age, tumor size and lymph node status. The mean Ki67 indices were higher in high grade tumors in both series. In addition, Swedish patients had significantly higher Ki67 indices in tumors associated with other poor prognostic factors as compared to Vietnamese, 52.8% vs. 31.9% in ER(-) tumors, 39.6% vs. 30.7% in PgR(-) tumors and 40.1% vs. 28.3% in HER2 amplified tumors, respectively. Conclusions: The cell proliferation index in breast cancers was similar in the Vietnamese and Swedish series. High proliferation was associated with poor prognostic factors such as high grade, hormone receptor negativity and HER2amplification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".