[Ki-67 expression and significance of different molecular subtypes of breast invasive ductal carcinoma].
Bibliographic record
Abstract
OBJECTIVE: To analyze Ki-67 expression and explore its significance in different molecular subtypes of breast invasive ductal carcinoma (IDC). METHODS: The expressions of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2) and Ki-67 were detected in 126 cases of IDC by immunohistochemical staining. Then the molecular subtype of each case of IDC was determined.Statistical analysis was performed to determine the relationship between Ki-67 expression and the molecular subtypes with clinicopathological features of IDC. RESULTS: There was no statistically significant difference of Ki-67 expression in age and tumor size (P > 0.05).However, significant difference existed in histological grading and lymph node metastasis (P < 0.05). The expression level of Ki-67 was negatively correlated with ER expression (r = -0.273, P = 0.002) and PR expression (r = -0.242, P = 0.007) and positively with HER-2 expression (r = 0.245, P = 0.006) . A low expression of Ki-67 was in LumianlA subtype (17/17) and high expression in other molecular subtypes. Moreover, the rate of high expression (Ki-67 LI>50%) in each subtype progressively increased with the degree of molecular typing and Ki-67 expression in different molecular subtypes showed significant difference (P < 0.05). CONCLUSIONS: The expression level of Ki-67 is correlated with histological grading and molecular type of IDC. High expression of Ki-67 carries poor prognosis. Thus it is necessary to perform a variety of routine clinicopathological examinations, such as Ki-67, ER, PR and HER-2.
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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".