Cytomorphologic and molecular analyses of fallopian tube fimbrial brushings for diagnosis of serous tubal intraepithelial carcinoma
Bibliographic record
Abstract
BACKGROUND: The paradigm shift localizing the origin of ovarian high-grade serous carcinoma (HGSC) to the fallopian tube underscores the rationale for meticulous microscopic examination of salpingectomy specimens. The precursor, termed "serous tubal intraepithelial carcinoma," is often a focal lesion, which poses difficulties for histologic diagnosis. METHODS: The authors describe a method to examine exfoliated epithelial cells from fallopian tube fimbria by gentle brushing, thereby enabling thorough sampling of the mucosal surface. Fimbrial brushings were collected from 20 fresh salpingectomy specimens from 15 patients, including 5 who had pathologically confirmed ovarian HGSC. Samples taken only from tubes that were grossly negative for tumor were processed for Papanicolaou staining, p53 immunocytochemistry, and tumor protein 53 (TP53) mutation analysis. RESULTS: Cells with malignant cytomorphologic features were identified only in tubal brushings from patients with ovarian HGSC. In all cases, atypical/malignant cells on cytology corresponded to lesions with similar morphology and immunostaining pattern in permanent sections, demonstrating the sensitivity of the technique while providing reassurance that specimen integrity was not disrupted by the procedure. Targeted next-generation sequencing confirmed the presence of TP53 mutations in fimbrial brushings from HGSC, but not in benign samples, and demonstrated concordance with the immunostaining pattern. Identical mutations were observed in matched lesions microdissected from formalin-fixed tissue sections. CONCLUSIONS: The described technique enables cytologic evaluation of the fallopian tube fimbria for a diagnosis of serous tubal intraepithelial carcinoma, serving as a complement to histology while offering distinct advantages with respect to the procurement of cellular material for ancillary testing and research.
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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 | 0.000 |
| 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".