Cytologic Histotyping of Gynecologic Malignancies in Peritoneal Fluids Is Reliable When Compared to Its Corresponding Surgical Specimen
Bibliographic record
Abstract
BACKGROUND: Cytologic examination of peritoneal fluid (PTFL) often represents the first and only pathologic specimen available to guide management of gynecologic malignancies (GMs). This study examines the cytohistologic correlation between histotyping of GM in PTFL and its corresponding surgical specimen (SS). METHODS: The study retrospectively identified positive PTFL with a GM between 2017 and 2022. Cytologic specimens (CSs) that were obtained after or concurrently with its diagnostic SS (biopsy/resection) were excluded. Root cause analysis of discordant cases was performed by reviewing morphology, specimen characteristics, and immunophenotype of CS. RESULTS: GM affected 55.8% (502/899) of malignant PTFL, of which 15.7% (79)/22.3% (112) was the only/initial diagnostic sample, respectively. Compared to SS, when a subtype was rendered on CS (91.9%), the concordance rate is 91.2% (almost perfect agreement, K = 0.842). Factors contributing to incorrect/inadequate subtyping include specimen limitations (low volume and/or cellularity), cytopathologist preference, and insufficient immunophenotyping. In seven patients (1.4%), the CS was able to render a more definitive diagnosis than its preceding nondiagnostic SS due to the paucity of lesional cells. CONCLUSION: When compared to SS, histotyping of GM in PTFL is reliable and, at times, can be more definitive than its surgical counterpart. In some cases, subtyping is limited by extrinsic factors (i.e., specimen limitations). In other cases, the responsible cytopathologist prefers not to subtype despite supportive morphologic and immunohistochemical features. This highlights an opportunity for improvement in the diagnosis/subtyping of GM in PTFL, which may be the initial or only diagnostic specimen prior to patient treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| 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 | 0.000 |
| 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".