Diagnosis of Serous Tubal Intraepithelial Carcinoma Based on Morphologic and Immunohistochemical Features
Bibliographic record
Abstract
There is compelling evidence that serous tubal intraepithelial carcinoma (STIC) is a precursor of high-grade serous ovarian carcinoma. Large-scale studies are now required to determine its biological significance and clinical implication. Before conducting these studies, a reproducible classification for STIC is needed, and that is the goal of this study. This study involved 6 gynecologic pathologists from 4 academic institutions and 3 independent rounds of review. In round 1, sixty-seven lesions ranging from normal, atypical, to STICs were classified by 5 pathologists on the basis of predetermined morphologic criteria. Interobserver agreement for the diagnosis of STIC versus not STIC was fair [κ = 0.39; 95% confidence interval (CI) 0.26, 0.52], and intraobserver reproducibility ranged from fair to moderate on the basis of percentage agreement and κ. Round 2 involved testing revised criteria that incorporated morphology and immunohistochemistry (IHC) for p53 protein expression and Ki-67 labeling in 10 sets by 3 of the pathologists. The result was an improvement in interobserver agreement for the classification of STIC (κ = 0.62; 95% CI 0.18, 1.00). An algorithm was then created combining morphology and IHC for p53 and Ki-67, and reproducibility was assessed as part of round 3. In 37 lesions reviewed by 6 pathologists, substantial agreement for STIC versus no STIC was observed (κ = 0.73; 95% CI 0.58, 0.86). In conclusion, we have developed reproducible criteria for the diagnosis of STIC that incorporate morphologic and IHC markers for p53 and Ki-67. The algorithm we propose is expected to help standardize the classification of STIC for future studies.
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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 | 0.001 |
| 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".