Evaluation of Clinical Criteria for the Identification of Lynch Syndrome Among Unselected Endometrial Cancer Patients
Bibliographic record
Abstract
Lynch syndrome (LS) is caused by germline mutations in DNA mismatch repair genes (MLH1, MSH2, MSH6, PMS2). Women with this syndrome are at high risk for developing endometrial cancer and several other types of cancer. The lifetime risk for affected women of developing endometrial cancer is 40%. Between 1% and 5% of endometrial cancers can be attributed to LS. Women with the syndrome are at high risk of developing a second cancer, and their first-degree relatives with and without cancer are also at risk for having the syndrome. Diagnosing LS leads to heightened surveillance and risk-reducing strategies that facilitate prevention of endometrial cancer and/or early detection and also allows for cancer prevention strategies in first-degree relatives. The Society of Gynecologic Oncology (SGO) published a clinical practice statement in 2007 establishing clinical criteria to identify individuals at elevated risk for LS with the goals of early identification and cancer prevention. The criteria included features such as young age of cancer diagnosis and family history of LS–associated tumors. The SGO criteria also stated that patients with a 5% to 10% probability of having a germline mutation in a DNA mismatch repair gene (which is the criterion standard for diagnosing LS) should undergo further evaluation and testing. The SGO 2007 criteria have not previously been validated in a population-based setting. The performance of SGO 5% to 10% clinical criteria was compared with that of universal tissue testing (expression of DNA mismatch repair proteins and MLH1 methylation in tumor tissue) in 412 unselected endometrial cancers in order to classify these tumors as sporadic or probable LS. The costs of using universal tissue testing to identify probable LS endometrial cancer patients may be prohibitive. A simplified cost-effectiveness analysis was performed to compare the direct costs of utilizing SGO 5% to 10% clinical criteria to universal tissue testing. The percentage of probable LS detected by tissue testing was 10.5%. The specificity of SGO 5% to 10% criteria to identify probable LS in these cases was 77.3% (95% confidence interval [CI], 72.7%–81.8%); sensitivity was 32.6% (95% CI, 19.2%–48.5%). With the exception of tumors located in the lower uterine segment, multivariate analysis of clinical features, family history, and pathologic variables did not identify significant differences between sporadic and probable LS groups. The simplified cost-effectiveness analysis showed that the cost per probable LS patient identified using a universal tumor testing strategy was comparable to the cost when the SGO 5% to 10% criteria are employed. These data show that the SGO screening criteria are not optimal for detecting probable LS patients in an unselected endometrial cancer population. Although the criteria successfully identify probable LS cases among women with endometrial cancer who are young or have significant family history of signature tumors, this screening strategy misses a larger proportion of probable LS patients who are older and have less significant family history.
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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 | 0.011 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".