FIGO Versus Silverberg Grading Systems in Ovarian Endometrioid Carcinoma
Bibliographic record
Abstract
The International Federation of Obstetrics and Gynecology (FIGO) grading system for endometrial carcinoma is currently applied to ovarian endometrioid carcinoma (OEC) in many practices. However, previous reports claim superior prognostication by using the Silverberg grading system for ovarian carcinoma. Thus, a thorough comparison between FIGO and Silverberg in OEC is still warranted. A total of 72 OECs diagnosed at our institution were independently graded using both systems. Grade (G) following Silverberg was based on combined scores for architecture, nuclear atypia, and mitotic activity. FIGO grading was based on the % of nonsquamous solid component; severe atypia warranted upgrade to the architectural FIGO grade (G1 to G2 or G2 to G3). Case grouping by grade was correlated with disease-free (DFS), disease-specific (DSS), and overall (OS) survival. Eleven (15.3%) OECs were bilateral, 26 (36.1%) had ovarian surface involvement, and 12 (16.7%) had lymphovascular space invasion. Forty-seven OECs were stage I (65%), 16 (22%) stage II, and 9 (13%) stage III. Median follow-up period was 62 months (range: 1 to 179 mo). Median DFS was 60.5 months (1 to 179 mo); median OS was 61 months (1 to 179 mo). Sixteen (22%) OECs recurred and 9 (13%) patiets died of disease. In univariate analysis, both FIGO and Silverberg correlated significantly with DFS, DSS, and OS (all with P<0.05). However, when compared in multivariate analysis, only Silverberg retained statistical correlation with survival (P<0.05). G1+G2 OEC by Silverberg had significantly better DFS, DSS, and OS compared with G3; such separation was not seen with FIGO. Survival was similar in Silverberg G1 and G2 tumors even 5 years after diagnosis, whereas FIGO G2 tumors had survival approaching G1 in the first 5 years, but declined after the 5-year mark approaching G3 tumors. Tumor laterality, lymphovascular space invasion, and stage also correlated with outcome. Stage showed prognostication superior to all other variables in multivariate analysis. As currently defined, the Silverberg grading system is a better predictor of survival than FIGO. Such differences may be explained by the G2 OEC groups, with G2 Silverberg clustering with G1 tumors, and having a more favorable behavior compared with G2 FIGO. Thus, Silverberg may be preferable in order to stratify patients in low and high-risk categories for prognosis and disease management.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".