Description of a Novel System for Grading of Endometrial Carcinoma and Comparison With Existing Grading Systems
Bibliographic record
Abstract
The most widely used system for grading of endometrial carcinoma is the International Federation of Gynecology and Obstetrics (FIGO) grading system. This grading system requires evaluation of histologic features that are difficult to assess reproducibly. Two hundred and two cases of endometrial carcinoma, treated by hysterectomy, were retrieved from the archives of Vancouver General Hospital (1983-1998). For each tumor, the architectural pattern, nuclear grade, and mitotic index were assessed. The tumor architectural pattern, nuclear grade, and mitotic index were significant predictors of patient outcome (P < 0.0001 for each, by univariate analysis). There were no prognostic differences between patients having predominantly solid versus papillary tumors, or tumors with mild versus moderate nuclear atypia. The tumors were then classified into high and low grade based on assessment of these three features. The presence of at least two criteria of these three: 1) predominantly papillary or solid growth pattern, 2) mitotic index > or =6/10 high power fields, or 3) severe nuclear atypia, resulted in a tumor being considered high grade. Low-grade tumors satisfied at most one of those criteria. The proposed grading system was found to be an independent predictor of patient outcome when patient survival was adjusted for FIGO stage, patient age, and tumor cell type. It also had more prognostic power than other grading systems tested when it was applied to all tumors, regardless of their cell type; however, the FIGO grading system was superior for prognostication when only carcinomas of endometrioid type were considered. With the FIGO grading system, no significant difference in survival was observed between patients with grade 1 and grade 2 tumors. Combining FIGO grades 1 and 2 results in a binary system (grades 1 and 2 vs. grade 3) that was the most prognostically significant grading system tested, with the additional advantages of being highly reproducible and familiar to practicing pathologists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".