Histological grading of ovarian mucinous carcinoma – an outcome‐based analysis of traditional and novel systems
Bibliographic record
Abstract
AIMS: Grading of primary ovarian mucinous carcinoma (OMC) is inconsistent among practices. The International Collaboration on Cancer Reporting recommends grading OMC using the International Federation of Gynecology and Obstetrics (FIGO) system for endometrial endometrioid carcinoma, when needed. The growth pattern (expansile versus infiltrative), a known prognostic variable in OMC, is not considered in any grading system. We herein analysed the prognostic value of various grading methods in a well-annotated cohort of OMC. METHODS AND RESULTS: Institutional OMCs underwent review and grading by the Silverberg and FIGO schemes and a novel system, growth-based grading (GBG), defined as G1 (expansile growth or infiltrative invasion in ≤10%) and G2 (infiltrative growth >10% of tumour). Of 46 OMCs included, 80% were FIGO stage I, 11% stage II and 9% stage III. On follow-up (mean = 52 months, range = 1-190), five patients (11%) had adverse events (three recurrences and four deaths). On univariate analysis, stage (P = 0.01, Cox proportional analysis), Silverberg grade (P = 0.01), GBG grade (P = 0.001) and percentage of infiltrative growth (P < 0.001), but not FIGO grade, correlated with disease-free survival. Log-rank analysis showed increased survival in patients with Silverberg grade 1 versus 2 (P < 0.001) and those with GBG G1 versus G2 (P < 0.001). None of the parameters evaluated was significant on multivariate analysis (restricted due to the low number of adverse events). CONCLUSIONS: Silverberg and the new GBG system appear to be prognostically significant in OMC. Pattern-based grading allows for a binary stratification into low- and high-grade categories, which may be more appropriate for patient risk stratification. Despite current practices and recommendations to utilise FIGO grading in OMC, our study shows no prognostic significance of this system and we advise against its use.
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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 | 0.000 | 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".