Differentiation of Fibroadenomas and Pure Mucinous Carcinomas on Dynamic Contrast-Enhanced MRI of the Breast Using Volume Segmentation for Kinetic Analysis: A Feasibility Study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to retrospectively evaluate the diagnostic performance of volume-based kinetic analysis in dynamic contrast-enhanced MRI (DCE-MRI) of the breast for the differentiation of fibroadenomas (FAs) with high T2 signal intensity from pure mucinous carcinomas (PMCs). MATERIALS AND METHODS: A review of records from 2007 to 2013 that were stored in the pathology department database at our institution identified nine patients with PMCs (defined as tumor cells with a mucinous component ≥ 90%) who underwent preoperative breast MRI. The PMCs were compared with 15 biopsy-proven FAs from 13 patients. Characteristics noted on DCE-MRI were evaluated using computer-assisted diagnosis software. For each mass, the proportion of progressive enhancement in the lesion at the delayed phase was quantified. Both groups of masses were compared using a Wilcoxon signed rank test. A ROC curve was used to define an appropriate cutoff point. RESULTS: The median rate of progressive enhancement was 100% (range, 99-100%) for FAs and 97% (range, 87-99%) for PMCs (p = 0.0326). The AUC of the kinetic curve for progressive enhancement was 0.7519 (95% CI, 0.5258-0.9407). A more appropriate cutoff value to maximize sensitivity and specificity was 98.5%. With this cutoff, sensitivity was 66.7% (95% CI, 11.1-100%) and specificity was 80% (95% CI, 39.6-99.8%) for the diagnosis of PMCs. CONCLUSION: Volume-based kinetic analysis may aid in differentiating FAs from PMCs on DCE-MRI studies of the breast.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".