The rise of a novel classification system for endometrial carcinoma; integration of molecular subclasses
Bibliographic record
Abstract
Endometrial cancer is a clinically heterogeneous disease and it is becoming increasingly clear that this heterogeneity may be a function of the diversity of the underlying molecular alterations. Recent large-scale genomic studies have revealed that endometrial cancer can be divided into at least four distinct molecular subtypes, with well-described underlying genomic aberrations. These subtypes can be reliably delineated and carry significant prognostic as well as predictive information; embracing and incorporating them into clinical practice is thus attractive. The road towards the integration of molecular features into current classification systems is not without obstacles. Collaborative studies engaging research teams from across the world are working to define pragmatic assays, improve risk stratification systems by combining molecular features and traditional clinicopathological parameters, and determine how molecular classification can be optimally utilized to direct patient care. Pathologists and clinicians caring for women with endometrial cancer need to engage with and understand the possibilities and limitations of this new approach, because integration of molecular classification of endometrial cancers is anticipated to become an essential part of gynaecological pathology practice. This review will describe the challenges in current systems of endometrial carcinoma classification, the evolution of new molecular technologies that define prognostically distinct molecular subtypes, and potential applications of molecular classification as a step towards precision medicine and refining care for individuals with the most common gynaecological cancer in the developed world. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".