Nipple aspirate fluid and ductoscopy to detect breast cancer
Bibliographic record
Abstract
We prospectively performed cytologic assessment and image analysis (IA) on matched nipple aspirate fluid (NAF) and mammary ductoscopy (MD) specimens to determine (1) the accuracy of these methods in cancer detection and (2) whether the two collection methods provide complementary information.NAF and MD specimens were collected from 84 breasts from 75 women (nine bilateral samples) who underwent breast surgery. Cytologic evaluation was performed on all samples. IA was performed on slides with sufficient epithelial cells.Cytologic evaluation proved more accurate in patients without pathologic spontaneous nipple discharge (PND) than those with PND, mainly because of the potential false positive diagnosis in the latter. While the sensitivity of NAF and MD cytology was low (10% and 14%, respectively), both were 100% specific in cancer detection in the non-PND cohort. Combining NAF and MD cytology information improved sensitivity (24%) without sacrificing specificity. Similar to cytology, IA was more accurate in patients without PND having high specificity (100% for aneuploid IA), but relatively low sensitivity (36%). Combining NAF and MD cytology with aneuploid IA improved the sensitivity (45%) while maintaining high specificity (100%). The best predictive model was positive NAF cytology and/or MD cytology combined with IA aneuploidy, which resulted in 55% sensitivity and 100% specificity in breast cancer detection.Cytologic evaluation and IA of NAF and MD specimens are complementary. The presence of atypical cells arising from an intraductal papilloma in ductoscopic specimens is a potential source of false positive diagnosis in patients with nipple discharge.
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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) | 0.000 | 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".