Histologic, Molecular, and Cytogenetic Features of Ovarian Cancers: Implications for Diagnosis and Treatment
Bibliographic record
Abstract
Ovarian epithelial carcinoma (OEC), the most common ovarian malignancy, is a heterogeneous disease with several histologic subtypes that show characteristic cytogenetic features, molecular signatures, oncologic signaling pathways, and clinical-biologic behavior. Recent advances in histopathology and cytogenetics have provided insights into pathophysiologic features and natural history of OECs. Several studies have shown that high- or low-grade serous, endometrioid, and clear cell carcinomas are characterized by mutations involving the TP53, K-ras/BRAF, CTNNB1, and PIK3CA genes, respectively. High-grade serous carcinomas, the most common subtype, often manifest with early transcoelomic spread of disease beyond the ovaries, whereas low-grade serous and mucinous carcinomas commonly manifest with early-stage disease, with a resultant excellent prognosis. On the basis of pathogenetic mechanisms, recent findings suggest a dualistic model of ovarian carcinogenesis consisting of types I and II. Type I (low-grade serous, mucinous, and endometrioid) cancers commonly arise from well-described, genetically stable precursor lesions (usually borderline tumors); manifest as large adnexal masses with early-stage disease; and have a relatively indolent clinical course, with an overall good prognosis. In contrast, type II carcinomas (high-grade serous, endometrioid, mixed, and undifferentiated variants) originate de novo from the adnexal epithelia, often demonstrate chromosomal instability, and have aggressive biologic behavior. Better knowledge of hereditary ovarian cancer syndromes and associated cytogenetic abnormalities has led to increased interest in novel biomarkers and molecular therapeutics. Genetic changes, pathologic features, imaging findings, and natural histories of a variety of histologic subtypes of OEC are discussed in this article.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".