Utility of Morphologic Risk Stratification Modeling and Immunohistochemical Surrogates for Key Molecular Alterations in Uterine Leiomyosarcoma
Bibliographic record
Abstract
Accurate diagnosis and prognostic stratification of uterine leiomyosarcoma (LMS) is becoming more important with more nuanced clinical management. Two recent studies by Momeni-Boroujeni and Chapel reported a 7-marker surrogate immunohistochemistry (IHC) diagnostic panel and a morphologic risk stratification schema, respectively. Our objective was to test these approaches in a local cohort. Thirty-four consecutive LMS cases diagnosed at Foothills Medical Center, Calgary, Alberta, Canada (2016-2022) underwent detailed histopathologic review and surrogate IHC panel (TP53, RB1, ATRX, PTEN, DAXX, MTAP, and MDM2). Associations of molecular alterations, morphologic features and survival were studied. Abnormal staining was detected for RB1 (65%), TP53 (62%), ATRX (44%), PTEN (32%), MTAP (15%), DAXX (9%), and MDM2 (6%). Seventy-nine percent of cases showed abnormality in ≥2 molecular markers, confirming a LMS diagnosis. However, 21% of cases showed only one or no abnormality and these cases were associated with a lower nuclear grade and mitotic count, which may cause diagnostic difficulties. While molecular alterations did not predict survival, morphologic risk stratification distinguished low-risk, intermediate-risk, and high-risk groups with significant differences in disease-specific survival (log-rank P = 0.030). While these findings validate the sensitivity of an IHC-based diagnostic panel in confirming the vast majority of LMS diagnoses, a subset, which more likely shows ambiguous diagnostic features, probably requires genomic testing. The previously proposed morphologic criteria seem to provide a robust prognostic stratification.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".