Association Between IHC and MSI Testing to Identify Mismatch Repair–Deficient Patients with Ovarian Cancer
Bibliographic record
Abstract
OBJECTIVE: In epithelial ovarian cancer, concordance between results of microsatellite instability (MSI) and immunohistochemical (IHC) testing has not been demonstrated. This study evaluated the association of MSI-high (MSI-H) status with loss of expression (LoE) of mismatch repair (MMR) proteins on IHC and assessed for potential factors affecting the strength of the association. METHODS: Tumor specimens from three population-based studies of epithelial ovarian cancer were stained for MMR proteins through manual or automated methods, and results were interpreted by one of two pathologists. Tumor and germline DNA was extracted and MSI testing performed. Multivariable logistic regression models were fitted to predict loss of IHC expression based on MSI status after adjusting for staining method and reading pathologist. RESULTS: Of 834 cases, 564 (67.6%) were concordant; 41 were classified as MSI-H with LoE and 523 as microsatellite stable (MSS) with no LoE. Of the 270 discordant cases, 83 were MSI-H with no LoE and 187 were MSS with LoE. Both IHC staining method and reading pathologist were strongly associated with discordant results. CONCLUSIONS: Lack of concordance in the current study may be related to inconsistencies in IHC testing methods and interpretation. Results support the need for validation studies before routine screening of ovarian tumors is implemented in clinical practice for the purpose of identifying Lynch syndrome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".