Comparison of clinical schemas and morphologic features in predicting Lynch syndrome in mutation‐positive patients with endometrial cancer encountered in the context of familial gastrointestinal cancer registries
Bibliographic record
Abstract
BACKGROUND: Endometrial cancer (EC) is the most common extraintestinal malignancy in Lynch syndrome (LS) and often is the sentinel malignancy, yet there is no consensus regarding LS-EC detection algorithms. In this study, the authors determined the efficacy of family/personal history and tumor morphology in predicting LS in a cohort of patients with EC who had mutation-proven LS. METHODS: Amsterdam II (AmII) criteria, revised Bethesda guidelines (rBG), and Society of Gynecologic Oncologists (SGO) clinical screening criteria were applied to the pedigrees of 76 patients with mutation-proven LS who had pathology-proven EC. When tumors were tested for microsatellite instability (MSI) phenotype status or mismatch-repair protein-immunohistochemical (MMR-IHC) expression, those results also were reviewed, and LS-associated histopathologic features were documented in 38 available patients. RESULTS: Of 76 patients, 36%, 58%, 71%, and 93% would have been selected for further testing for LS by pedigree screening at the time of EC diagnosis with rBG, AmII, SGO 20%-to-25%, and SGO 5%-to-10% criteria, respectively. Ninety percent (18 of 20 tumors) of tested ECs had high MSI, and 96% (22 of 23 tumors) had abnormal MMR-IHC expression. At least 1 LS-EC morphologic feature was present in 16 of 38 tumors (42%). CONCLUSIONS: Clinical screening criteria had variable efficacy for the identification of LS-associated EC, and SGO 5%-to-10% criteria performed best. Characteristic pathologic features were present in a minority of patients. Although a high proportion of LS-ECs had the MSI phenotype and were MMR deficient, the specificity of these tests and of clinical screening for LS in unselected patients with EC has been poorly described. Prospective studies to determine the optimal combination of these screening modalities are required.
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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 | 0.001 |
| 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 | 0.001 |
| 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".