Can Magnetic Resonance Imaging Predict Pathologic Findings for Endometrioid Endometrial Cancer?
Bibliographic record
Abstract
This pilot study aimed to assess the feasibility of precisely measuring tumor diameter and myometrial invasion in patients with endometrioid endometrial cancer (EEC) using preoperative contrast-enhanced magnetic resonance imaging (MRI). Adult patients with confirmed diagnosis of complex hyperplasia with atypia or EEC were included. Three radiologists separately measured tumor diameter and myometrial invasion. Basic descriptive statistics were used to describe patient characteristics and to compare radiology- and pathology-measured tumor diameter and myometrial invasion. Using the pathology results for tumor diameter as the gold standard for comparison, at least 1 radiologist was able to predict largest tumor diameter within 5 mm for 41.7% of patients. Similarly, based on pathology results for myometrial invasion, at least 1 radiologist was able to predict myometrial invasion within 5% for 50% of patients. All radiologists were able to predict superficial (<50%) or deep (≥50%) myometrial invasion for 75% of patients, with greater sensitivity, specificity, and accuracy for deep myometrial invasion. Given variation among radiologic measurements, it is difficult to recommend preoperative MRI as a basis for measuring tumor diameter and myometrial invasion. Even so, the ability to predict superficial versus deep myometrial invasion may benefit patients with EEC for whom surgery is not a viable option or for those seeking fertility-sparing treatment options.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".