Interobserver Agreement for Mismatch Repair Protein Immunohistochemistry in Endometrial and Nonserous, Nonmucinous Ovarian Carcinomas
Bibliographic record
Abstract
Immunohistochemistry (IHC) for mismatch repair (MMR) proteins is an established test to identify Lynch syndrome (LS) in patients with colorectal cancer and is being increasingly used to identify LS in women with endometrial and/or nonserous ovarian cancer (OC). We assessed interobserver agreement in the interpretation of MMR-IHC on endometrial and ovarian carcinomas. The study consisted of 73 consecutive endometrial cancers (n=48) and nonserous, nonmucinous epithelial OCs (n=25). Six pathologists from 2 cancer centers, one with and the other without, previous experience in interpreting MMR-IHC, evaluated MLH1, MSH2, MSH6, and PMS2 stains. Before the study, an experienced pathologist led a review of 9 teaching cases. A decision tool was developed as a guide in MMR-IHC interpretation. Staining was interpreted as intact, deficient, or equivocal for each protein. Interobserver agreement for the patient MMR status was categorized as "almost perfect" with κ=0.919 (95% CI, 0.863-0.976). All observers were in agreement in 66 (92%) tumors. Four of the less experienced pathologists had at least 1 discrepant interpretation. There were 6 discordant cases: 3 MMR-deficient cases and 2 MMR-intact cases by majority opinion were called equivocal by at least 1 observer, and 1 MMR-deficient case by majority opinion was interpreted as MMR intact by 1 pathologist. Only the latter case (1/73 patients, 1.4%) had an unequivocal disagreement that could affect patient management. Issues associated with discordant interpretation included heterogeneous staining, intratumoral lymphocytes, regional reduced internal control tissue staining, and scattered absent/weak staining adjacent to tumor cells with strong nuclear staining.
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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 | 0.063 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".