Stratification of Intermediate-Risk Endometrial Cancer Patients into Groups at High Risk or Low Risk for Recurrence Based on Tumor Gene Expression Profiles
Bibliographic record
Abstract
PURPOSE: Endometrial cancers classified as "intermediate risk" based on clinical and/or pathologic features are associated with a 15% to 20% risk of recurrence. Here, we test whether global gene expression profiling can distinguish intermediate-risk tumors into high-risk and low-risk subgroups. EXPERIMENTAL DESIGN: Tumor specimens were obtained from 75 intermediate-risk endometrial cancer patients, 13 who had recurred and 62 who had not recurred with a median follow-up of 24 months. Gene expression profiles were obtained using the Affymetrix U133A GeneChip oligonucleotide microarray. The genes most associated with risk of recurrence were used to create a risk score using a leave-one-out cross-validation method and the univariate Cox proportional hazards regression model. Time to recurrence curves for the high-risk and low-risk subgroups were estimated using the Kaplan-Meier method, and the difference in time to recurrence between these two subgroups was tested using the log-rank test. RESULTS: There was a significant difference in time to recurrence between high-risk and low-risk patients using risk scores as defined above (P = 0.04). The estimated hazard ratio (95% confidence interval) was 3.07 (1.00-9.43). CONCLUSIONS: Patients with intermediate-risk endometrial cancers identified as high-risk for recurrence according to a gene expression-based risk score have a significantly increased risk for recurrence compared with those classified as low risk. These findings suggest that gene expression profiling can potentially contribute to the clinical classification and management of intermediate-risk endometrial cancers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".