MRI Evaluation of Uterine Masses for Risk of Leiomyosarcoma: A Consensus Statement
Bibliographic record
Abstract
Laparoscopic myomectomy, a common gynecologic operation in premenopausal women, has become heavily regulated since 2014 following the dissemination of unsuspected uterine leiomyosarcoma (LMS) throughout the pelvis of a physician treated for symptomatic leiomyoma. Research since that time suggests a higher prevalence than previously suspected of uterine LMS in resected masses presumed to represent leiomyoma, as high as one in 770 women (0.13%). Though rare, the dissemination of an aggressive malignant neoplasm due to noncontained electromechanical morcellation in laparoscopic myomectomy is a devastating outcome. Gynecologic surgeons' desire for an evidence-based, noninvasive evaluation for LMS is driven by a clear need to avoid such harms while maintaining the availability of minimally invasive surgery for symptomatic leiomyoma. Laparoscopic gynecologists could rely upon the distinction of higher-risk uterine masses preoperatively to plan oncologic surgery (ie, potential hysterectomy) for patients with elevated risk for LMS and, conversely, to safely offer women with no or minimal indicators of elevated risk the fertility-preserving laparoscopic myomectomy. MRI evaluation for LMS may potentially serve this purpose in symptomatic women with leiomyomas. This evidence review and consensus statement defines imaging and disease-related terms to allow more uniform and reliable interpretation and identifies the highest priorities for future research on LMS evaluation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".