Ductoscopic cytology and image analysis to detect breast carcinoma
Bibliographic record
Abstract
BACKGROUND: The goal of the prospective study was to determine whether 1) image analysis (IA; including DNA index [DI], S-phase fraction, and the presence or absence of aneuploidy or hypertetraploidy [HT]) of fiberoptic ductoscopy (FD) breast specimens was feasible, 2) IA findings from FD specimens predicted histopathologic evidence of disease, and 3) a combination of IA, cytology, and clinical factors provided complementary information in the diagnosis of breast carcinoma. METHODS: IA and cytologic evaluation were performed on 106 consecutively collected ductoscopic specimens from 88 subjects. RESULTS: IA was successful in 73 (71%) FD specimens. HT (P = 0.03) was related to intraductal visual observations. The proportion of atypical papillomas with aneuploidy was greater (P = 0.05) than in any other class. The HT index was higher in atypical papilloma (P = 0.02) and in breast carcinoma (P = 0.05) than with other diagnoses. The proportion of papilloma cases with HT was greater (P = 0.04) than benign papillomas. Malignant cytology was associated with a higher DI (P = 0.02) and a higher HT index (P = 0.001) than nonmalignant (benign or atypical) cytology. HT (P = 0.002) was less common with cytology containing few epithelial cells, and the HT index was higher with malignant versus nonmalignant FD cytology (P = 0.001). The percentage of cells containing HT was greater in FD specimens that were suspicious for malignancy (P = 0.006) than in those with benign cytology or mild atypia. Considering all samples and combining IA, cytologic, and visual findings in a stepwise linear discriminant analysis optimized the sensitivity (61%) and specificity (90%) of breast carcinoma prediction. Excluding spontaneous nipple discharge (SND) samples and adding epithelial cell quantity in samples improved the model (sensitivity, 85%; specificity, 80%). CONCLUSIONS: IA was feasible in FD specimens whenever adequate epithelial cells were present. IA and cytologic findings were associated. Adding IA results to cytology and visual findings and excluding samples with SND improved diagnostic sensitivity.
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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".