Cytohistologic immunohistochemical correlation of epithelial tubo‐ovarian neoplasms: Can cell blocks substitute for tissue?
Bibliographic record
Abstract
BACKGROUND: Cytologic specimens often represent the initial diagnostic material for tubo-ovarian neoplasms resulting from therapeutic paracentesis for patients presenting with high-volume ascites. However, subtyping and immunohistochemical (IHC) characterization, which have implications in preoperative management and downstream ancillary testing, are not routinely performed in many institutions. This study aims to perform cytohistologic correlation of commonly used IHC stains to establish their reliability in peritoneal fluids/washing specimens. METHODS: A retrospective search of the laboratory information systems was performed to identify peritoneal fluid/washing specimens involved by borderline or malignant epithelial tubo-ovarian neoplasms and concurrent/subsequent surgical resection specimens. Cell blocks and tissue were stained for PAX8, WT-1, p53, p16, Napsin-A, estrogen receptor, and progesterone receptor, and staining between cytological and surgical specimens was compared. RESULTS: A total of 56 case pairs were included, with the following final diagnoses on histological examination: 37 high-grade serous carcinomas, eight clear cell carcinomas, one endometrioid adenocarcinoma, two low-grade serous carcinomas, and eight serous borderline tumors. There was perfect cytohistologic correlation for PAX8 (Lin's concordance correlation coefficient [LINCCC] = 1.00) and WT-1 (LINCCC = 1.00), substantial/good correlation for p53 (LINCCC = 0.96), p16 (LINCCC = 0.93), napsin-A (LINCCC = 0.91) and ER (LINCCC = 0.77), and moderate correlation for PR (LINCCC = 0.54). CONCLUSIONS: Immunohistochemical correlation between peritoneal fluid and surgical resection specimens for tubo-ovarian neoplasms is high. Common subtypes of tubo-ovarian carcinomas can be reliably distinguished on fluids using IHC.
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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 | 0.005 | 0.012 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".