Classification of Extraovarian Implants in Patients With Ovarian Serous Borderline Tumors (Tumors of Low Malignant Potential) Based on Clinical Outcome
Bibliographic record
Abstract
The classification of extraovarian disease into invasive and noninvasive implants predicts patient outcome in patients with high-stage ovarian serous borderline tumors (tumors of low malignant potential). However, the morphologic criteria used to classify implants vary between studies. To date, there has been no large-scale study with follow-up data comparing the prognostic significance of competing criteria. Peritoneal and/or lymph node implants from 181 patients with high-stage serous borderline tumors were evaluated independently by 3 pathologists for the following 8 morphologic features: micropapillary architecture; glandular architecture; nests of epithelial cells with surrounding retraction artifact set in densely fibrotic stroma; low-power destructive tissue invasion; single eosinophilic epithelial cells within desmoplastic stroma; mitotic activity; nuclear pleomorphism; and nucleoli. Follow-up of 156 (86%) patients ranged from 11 to 264 months (mean, 89 mo; median, 94 mo). Implants with low-power destructive invasion into underlying tissue were the best predictor of adverse patient outcome with 69% overall and 59% disease-free survival (P<0.01). In the evaluation of individual morphologic features, the low-power destructive tissue invasion criterion also had excellent reproducibility between observers (κ=0.84). Extraovarian implants with micropapillary architecture or solid nests with clefts were often associated with tissue invasion but did not add significant prognostic value beyond destructive tissue invasion alone. Implants without attached normal tissue were not associated with adverse outcome and appear to be noninvasive. Because the presence of invasion in an extraovarian implant is associated with an overall survival analogous to that of low-grade serous carcinoma, the designation low-grade serous carcinoma is recommended. Even though the low-power destructive tissue invasion criterion has excellent interobserver reproducibility, it is further recommended that the presence of an invasive implant be confirmed by at least 2 pathologists (preferably at least 1 of whom is an experienced gynecologic pathologist) in order to establish the diagnosis of-low grade serous carcinoma.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".