Morphologic and Molecular Characteristics of Mixed Epithelial Ovarian Cancers
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) consists of 5 major histotypes: high-grade serous carcinoma (HGSC), endometrioid carcinoma (EC), clear cell carcinoma (CCC), mucinous carcinoma (MC), and low-grade serous carcinoma (LGSC). Each can have a broad spectrum of morphologic appearances, and 1 histotype can closely mimic histopathologic features more typical of another. Historically, there has been a relatively high frequency of mixed, defined by 2 or more distinct histotypes present on the basis of routine histopathologic assessment, histotype carcinoma diagnoses (3% to 11%); however, recent immunohistochemical (IHC) studies identifying histotype-specific markers and allowing more refined histotype diagnoses suggest a much lower incidence. We reviewed hematoxylin and eosin-stained slides from 871 cases of EOC and found the frequency of mixed carcinomas to be 1.7% when modern diagnostic criteria are applied. Through international collaboration, we established a cohort totaling 22 mixed EOCs, consisting of 9 EC/CCC, 4 EC/LGSC, 3 HGSC/CCC, 2 CCC/MC, and 4 other combinations. We interrogated the molecular differences between the different components of each case using IHC, gene expression, and hotspot sequencing analyses. IHC data alone suggested that 9 of the 22 cases were not mixed tumors, as they presented a uniform immuno-phenotype throughout, and these cases most probably represent morphologic mimicry and variation within tumors of a single histotype. Synthesis of molecular data further reduces the incidence of mixed carcinomas. On the basis of these results, true mixed carcinomas with both morphologic and molecular support for the presence of >1 histotype within a given tumor represent <1% of EOCs.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".