Abstract 4855: Poor performance of published clinical screening criteria for the population-based identification of endometrial cancer patients with Lynch Syndrome.
Bibliographic record
Abstract
Abstract Objective: Clinically based risk assessment tools, targeting those with young age of cancer onset and family history of specific cancers, have been used to identify individuals with Lynch Syndrome (LS). Women with LS are equally likely to develop endometrial carcinoma (EC) as they are colorectal carcinoma (CRC). To identify women presenting with EC at risk for LS, the Society of Gynecologic Oncology (SGO) published recommendations in 2007 regarding which patients would benefit from further genetic evaluation for LS. These criteria are comparable to the Revised Bethesda Guidelines. The primary objective of this study was to evaluate SGO criteria's ability to predict women at risk for LS in the EC population and to ascertain if alternative criteria exist that can better identify high-risk patients. Methods: 408 sequential EC cases were evaluated for immunohistochemical expression of four DNA mismatch repair (MMR) proteins. Tumors with loss of MSH2, MSH6 or PMS2 were designated as probable Lynch Syndrome (PLS). Tumors with loss of MLH1 and absence of MLH1 promoter methylation were also designated PLS. Clinical and pathologic data were collected from the electronic medical record. Results: Of the 408 EC cases, 43 (10.5%) of the patients were defined as probable Lynch Syndrome (PLS). 97/408 (23.7%) of EC cases met SGO criteria, but only 14 of these 97 cases (14.4%) were PLS. Of the 43 PLS cases, 29/43 (67.4%) did not meet SGO criteria. Comparison of clinical and pathologic characteristics, including age of cancer diagnosis and family history of EC and/or CRC, revealed no statistically significant differences between sporadic and PLS tumors with the exception of tumors arising from the lower uterine segment. Tumors with this sight of origin are more likely to be associated with PLS. The sensitivity and specificity of SGO criteria was 32.6% and 77.2%, respectively. Conclusions: Existing clinical guidelines for detecting endometrial carcinoma patients at elevated risk for having Lynch Syndrome perform poorly in an unselected patient population. With the exception of tumors arising from the lower uterine segment, there are no statistically significant clinical or pathological differences between sporadic tumors and PLS tumors. Lower uterine segment EC occurs in only 3% of all EC cases and is not a useful screening tool. SGO clinical criteria correctly identified 32.6% of women with PLS, which results in missed CRC screening opportunities for 67% of women with increased risk profiles. These results suggest that the SGO guidelines are inadequate for identifying LS. Given the lack of effective clinical or pathological screening tools, MMR IHC and MLH1 methylation testing of all endometrial cancer patients may be the best way to identify women at risk for having Lynch Syndrome. Citation Format: Amanda S. Bruegl, Bojana Djordjevic, Bryan M. Fellman, Diana L. Urbauer, Raja Luthra, Karen H. Lu. Poor performance of published clinical screening criteria for the population-based identification of endometrial cancer patients with Lynch Syndrome. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4855. doi:10.1158/1538-7445.AM2013-4855
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".