Pathologist Concordance for Ovarian Carcinoma Subtype Classification and Identification of Relevant Histologic Features Using Microscope and Whole Slide Imaging
Bibliographic record
Abstract
CONTEXT.—: Despite several studies focusing on the validation of whole slide imaging (WSI) across organ systems or subspecialties, the use of WSI for specific primary diagnosis tasks has been underexamined. OBJECTIVE.—: To assess pathologist performance for the histologic subtyping of individual sections of ovarian carcinomas using a light microscope and WSI. DESIGN.—: A panel of 3 experienced gynecologic pathologists provided reference subtype diagnoses for 212 histologic sections from 109 ovarian carcinomas based on optical microscopy review. Two additional attending pathologists provided diagnoses and also identified the presence of a set of 8 histologic features important for ovarian tumor subtyping. Two experienced gynecologic pathologists and 2 fellows reviewed the corresponding WSI images for subtype classification and feature identification. RESULTS.—: Across pathologists specialized in gynecologic pathology, concordance with the reference diagnosis for the 5 major ovarian carcinoma subtypes was significantly higher for a pathologist reading on a microscope than each of 2 pathologists reading on WSI. Differences were primarily due to more frequent classification of mucinous carcinomas as endometrioid with WSI. Pathologists had generally low agreement in identifying histologic features important to ovarian tumor subtype classification with either an optical microscopy or WSI. This result suggests the need for refined histologic criteria for identifying such features. Interobserver agreement was particularly low for identifying intracytoplasmic mucin with WSI. Inconsistencies in evaluating nuclear atypia and mitoses with WSI were also observed. CONCLUSIONS.—: Further research is needed to specify the reasons for these diagnostic challenges and to inform users and manufacturers of WSI technology.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".