Accurate Distinction of Ovarian Clear Cell From Endometrioid Carcinoma Requires Integration of Phenotype, Immunohistochemical Predictions, and Genotype
Bibliographic record
Abstract
Ovarian clear cell carcinoma (OCCC) and ovarian endometrioid carcinoma (OEC) are both associated with endometriosis but differ in histologic phenotype, biomarker profile, and survival. Our objectives were to refine immunohistochemical (IHC) panels that help distinguish the histotypes and reassess the prevalence of mismatch repair deficiency (MMRd) in immunohistochemically confirmed OCCC. We selected 8 candidate IHC markers to develop first-line and second-line panels in a training set of 344 OCCC/OEC cases. Interobserver reproducibility of histotype diagnosis was assessed in an independent testing cohort of 100 OCC/OEC initially without and subsequently with IHC. The prevalence of MMRd was evaluated using the testing cohort and an expansion set of 844 ovarian carcinomas. The 2 prototypical combinations (OCCC: Napsin A+/HNF1B diffusely+/PR-; OEC: Napsin A-/HNF1B nondiffuse/PR+) occurred in 75% of cases and were 100% specific. A second-line panel (ELAPOR1, AMACR, CDX2) predicted the remaining cases with 83% accuracy. Integration of IHC improved interobserver reproducibility (κ=0.778 vs. 0.882, P<0.0001). The prevalence of MMRd was highest in OEC (11.5%, 44/383), lower in OCCC (1.7%, 5/297), and high-grade serous carcinomas (0.7%, 5/699), and absent in mucinous (0/126) and low-grade serous carcinomas (0/50). All 5 MMRd OCCC were probable Lynch syndrome cases with prototypical IHC profile but ambiguous morphologic features: 3/5 with microcystic architecture and 2/5 with intratumoral stromal inflammation. Integration of first-line and second-line IHC panels increases diagnostic precision and enhances prognostication and triaging for predisposing/predictive molecular biomarker testing. Our data support universal Lynch syndrome screening in all patients with OEC when the diagnosis of other histotypes has been vigorously excluded.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".