Ovarian carcinoma histotype determination is highly reproducible, and is improved through the use of immunohistochemistry
Bibliographic record
Abstract
AIMS: To assess the variation in ovarian carcinoma type diagnosis among gynaecological pathologists from Nordic countries, and whether a rationally designed panel of immunohistochemical markers could improve diagnostic reproducibility. METHODS AND RESULTS: Eight pathologists from four countries (Sweden, Denmark, Norway, and Finland) received an educational lecture on the diagnosis of ovarian carcinoma type. All tumour-containing slides from 54 ovarian carcinoma cases were independently reviewed by the participants, who: (i) determined type purely on the basis of histology; (ii) indicated whether they would apply immunohistochemistry in their routine practice; and (iii) determined type after reviewing the staining results. The results for six markers (WT1, p53, p16, HNF-1β, ARID1A, and progesterone receptor) were determined for all 54 cases, by staining of a tissue microarray. The median concordance with central review diagnosis was 86%, and significantly improved to 90% with the incorporation of immunostaining results (P = 0.0002). The median interobserver agreement was 78%, and significantly improved to 85% with the incorporation of immunostaining results (P = 0.0002). CONCLUSIONS: Use of the immunostaining results significantly improved both diagnostic accuracy and interobserver agreement. These results indicate that ovarian carcinoma type can be reliably diagnosed by pathologists from different countries, and also demonstrate that immunohistochemistry has an important role in improving diagnostic accuracy and agreement between pathologists.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".