Current Morphologic Criteria Perform Poorly in Identifying Hereditary Leiomyomatosis and Renal Cell Carcinoma Syndrome-associated Uterine Leiomyomas
Bibliographic record
Abstract
The contemporary oncologic pathology report conveys diagnostic, prognostic, predictive, and hereditary predisposition information. Each component may be premised on a morphologic feature or a biomarker. Clinical validity and reproducibility are paramount as is standardization of reporting and clinical response to ensure individualization of patient care. Regarding hereditary predisposition, morphology-based genetic referral systems in some instances have eclipsed genealogy-based systems, for example, cell type in ovarian cancer and BRCA screening. In other instances such as Lynch syndrome, morphology-based schemas supplement clinical schemas and there is an emerging standard of care for reflex biomarker testing. Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome predisposes patients to uterine and cutaneous leiomyomas (LMs) and renal cell carcinomas (RCCs). Several authors have emphasized the role pathologists may play in identifying this syndrome by recognizing the morphologic characteristics of syndromic uterine LMs and RCCs. Recently immunohistochemical overexpression of S-(2-succinyl) cysteine (2SC) has been demonstrated as a robust biomarker of mutation status in tumors from HLRCC patients. In this blinded control-cohort study we demonstrate that the proposed morphologic criteria used to identify uterine LMs in HLRCC syndrome are largely irreproducible among pathologists and lack sufficient robustness to serve as a trigger to triage cases for 2SC immunohistochemistry or patients for further family/personal history inquiry. Although refinement of morphologic criteria can be considered, in view of the availability of a clinically robust biomarker, consideration should be given to reflex testing of uterine LMs with an appropriate age cut off or in the setting of a suspicious family history.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".