Performance characteristics of screening strategies to identify Lynch syndrome in women with ovarian cancer
Bibliographic record
Abstract
BACKGROUND: For women with ovarian cancer (OC), the optimal screening strategy to identify Lynch syndrome (LS) has not been determined. In the current study, the authors compared the performance characteristics of various strategies combining mismatch repair (MMR) immunohistochemistry (IHC), microsatellite instability testing (MSI), and family history for the detection of LS. METHODS: Women with nonserous and/or nonmucinous ovarian cancer were recruited prospectively from 3 cancer centers in Ontario, Canada. All underwent germline testing for LS and completed a family history assessment. Tumors were assessed using MMR IHC and MSI. The sensitivity, specificity, and positive and negative predictive values of screening strategies were compared with the gold standard of a germline result. RESULTS: Of 215 women, germline data were available for 189 (88%); 13 women (7%) had pathogenic germline variants with 7 women with mutS homolog 6 (MSH6); 3 women with mutL homolog 1 (MLH1); 2 women with PMS1 homolog 2, mismatch repair system component (PMS2); and 1 woman with mutS homolog 2 (MSH2). A total of 28 women had MMR-deficient tumors (13%); of these, 11 had pathogenic variants (39%). Sequential IHC (with MLH1 promoter methylation analysis on MLH1-deficient tumors) followed by MSI for nonmethylated and/or MMR-intact patients was the most sensitive (92.3%; 95% confidence interval, 64%-99.8%) and specific (97.7%; 95% confidence interval, 94.2%-99.4%) approach, missing 1 case of LS. IHC with MLH1 promoter methylation analysis missed 2 patients of LS. Family history was found to have the lowest sensitivity at 55%. CONCLUSIONS: Sequential IHC (with MLH1 promoter methylation analysis) followed by MSI was found to be most sensitive. However, IHC with MLH1 promoter methylation analysis also performed well and is likely more cost-effective and efficient in the clinical setting. The pretest probability of LS is high in patients with MMR deficiency and warrants universal screening for LS.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".