Correlation Between Immunohistochemical Biomarkers Expression and Prognosis of Ovarian Carcinomas in Tunisian Patients
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is the leading cause of death from gynaecological malignancies. Newer biological prognostic factors and predictors of response to therapy are needed. Our study was designed to evaluate the expression of p53, Bcl-2, Estrogen receptor (ER) and Progesterone receptor (PR) in ovarian carcinoma and to compare it with other prognostic parameters such as age, FIGO stage, size of residual tumor, histological type and grade. METHODS: This is a retrospective study conducted in the department of pathology at Sfax University Hospital. Confirmed 57 cases of ovarian carcinoma were reviewed in the period between January 1995 and December 2006. We used immunohistochemistry to evaluate the expression of p53, Bcl-2, ER and PR receptors and Chi-Square and Student test to correlate immunohistochemical findings with some prognostic parameters of ovarian carcinoma. RESULTS: ). The expression of PR was associated with an early stage (FIGO I and II), a non serous histologic type and a low tumour grade (p = 0,045; 0,010 and 0,036 respectively). No correlation was found between Bcl-2 and ER and prognostic parameters. Survival analysis revealed that Bcl-2 status, FIGO stage, presence of ascites, peritoneal cytology, and residual disease were significant predictive factors of survival. CONCLUSION: p53 expression correlates with a worse prognosis in epithelial ovarian cancer, whereas Bcl-2 expression is related to a better outcome. For hormonal status, expression of PR is found to be an independent indicator of favourable prognosis. These results should be supported by more and larger studies.
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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 | 0.001 |
| 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".