Implementation of endometrial cancer molecular subtyping into a hybrid community-academic practice
Bibliographic record
Abstract
OBJECTIVES: We sought to confirm utility of our institution's modified Proactive Molecular Risk Classifier for Endometrial Cancer protocol in our daily practice, which includes mismatch repair (MMR), p53, and L1 cell adhesion molecule (L1CAM) immunohistochemistry with in-house next-generation sequencing for POLE, TP53, and CTNNB1. METHODS: We conducted a retrospective review of all patients in our institution who underwent primary endometrial carcinoma resection from the year prior to protocol implementation (PRE; October 1, 2020, to September 30, 2021) through first year of implementation (POST; October 1, 2021, to September 30, 2022) to compare the distribution of molecular and traditional staging factors using GOG-249 criteria to assign clinical risk. RESULTS: In total, 136 of 260 PRE patients were classified as clinically low risk (LR), of whom 31 were MMR deficient. Of the 157 LR POST patients with endometrioid-type carcinoma, 45 were MMR deficient, 5 were POLE mutant, 5 were TP53 mutant, 56 were of no specific molecular profile (NSMP), and 46 did not receive full protocol testing. Of all 79 POST NSMP endometrioid-type cases, 18 were CTNNB1 mutated and 8 showed L1CAM expression. CONCLUSIONS: Our protocol identified 22 (14%) of 157 LR tumors that harbored incipient intermediate- to high-risk molecular aberrations in TP53, CTNNB1, or L1CAM. Moving forward, results of ongoing trials assessing adjuvant therapy decisions based on molecular classification are necessary to confirm protocol utility and identify appropriate modifications.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".