Performance characteristics of screening strategies for Lynch syndrome in unselected women with newly diagnosed endometrial cancer who have undergone universal germline mutation testing
Bibliographic record
Abstract
BACKGROUND: Immunohistochemistry (IHC) for mismatch repair protein expression, microsatellite instability (MSI) testing, tumor morphology, and family history were compared to determine which screening strategy is superior in identifying Lynch syndrome (LS) in unselected women with newly diagnosed endometrial cancer (EC) who have undergone universal germline mutation testing. METHODS: A prospective cohort study was performed that recruited women with newly diagnosed EC. Participants completed a family history assessment with molecular characterization of EC with IHC and MSI testing and EC assessment for LS-associated morphologic features and underwent universal germline mutation testing for mutations in the mismatch repair pathway. The sensitivity, specificity, and positive and negative predictive values were compared between the screening strategies. RESULTS: A total of 118 (65%) of 182 consecutive women with EC participated. Of these, 34 women (29%) had tumors that were IHC deficient and 27 women (23%; N = 117) had tumors that were positive for MSI. Twenty women (17%) met IHC criteria and 16 women (15.2%, N = 105) met family history criteria based on Ontario Ministry of Health Criteria for the genetic assessment for LS. Seven women (5.9%) had a germline mutation: 4 had MLH1 (mutL homolog 1), 2 had MSH6 (mutS homolog 6), and 1 had MSH2 (mutS homolog 2). IHC in women aged <60 years had the best performance characteristics, with a sensitivity of 100%, a specificity of 86.1%, a positive predictive value of 58.3%, and a negative predictive value of 100%. Family history and tumor morphology both had the lowest sensitivity at 71.4%. Overall tumor morphology had the poorest performance, with a specificity of 42.1%. CONCLUSIONS: The mutation rate of 5.9% was higher than expected in this unselected cohort of women with EC. The superior screening strategy to identify women presenting with EC is universal IHC screening in women aged <60 years.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".