Diagnosis of Ovarian Carcinoma Histotype Based on Limited Sampling
Bibliographic record
Abstract
Growing insights into the biological features and molecular underpinnings of ovarian cancer has prompted a shift toward histotype-specific treatments and clinical trials. As a result, the preoperative diagnosis of ovarian carcinomas based on small tissue sampling is rapidly gaining importance. The data on the accuracy of ovarian carcinoma histotype-specific diagnosis based on small tissue samples, however, remains very limited in the literature. Herein, we describe a prospective series of 30 ovarian tumors diagnosed using cytology, frozen section, core needle biopsy, and immunohistochemistry (p53, p16, WT1, HNF-1β, ARID1A, TFF3, vimentin, and PR). The accuracy of histotype diagnosis using each of these modalities was 52%, 81%, 85%, and 84% respectively, using the final pathology report as the reference standard. The accuracy of histotype diagnosis using the Calculator for Ovarian Subtype Prediction (COSP), which evaluates immunohistochemical stains independent of histopathologic features, was 85%. Diagnostic accuracy varied across histotype and was lowest for endometrioid carcinoma across all diagnostic modalities (54%). High-grade serous carcinomas were the most overdiagnosed on core needle biopsy (accounting for 45% of misdiagnoses) and clear cell carcinomas the most overdiagnosed on frozen section (accounting for 36% of misdiagnoses). On core needle biopsy, 2/30 (7%) cases had a higher grade lesion missed due to sampling limitations. In this study, we identify several challenges in the diagnosis of ovarian tumors based on limited tissue sampling. Recognition of these scenarios can help improve diagnostic accuracy as we move forward with histotype-specific therapeutic strategies.
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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 | 0.004 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".