Contrast-Enhanced Mammography as a Functional Biomarker in Breast Cancer: Correlation of Enhancement Patterns with Ki-67 and Histological Grade
Bibliographic record
Abstract
Background: Contrast-Enhanced Spectral Mammography (CESM) combines anatomical and functional imaging, showing promise in breast cancer diagnosis. Despite well-established lesion detection accuracy, few studies have investigated the link between CESM enhancement patterns and tumor aggressiveness biomarkers. Methods: We retrospectively evaluated 100 patients (mean age 59.5 years) undergoing CESM with complete histopathological data. Lesions were categorized by enhancement intensity (high, medium, low) and contrast homogeneity (homogeneous vs. heterogeneous), correlated with Ki-67 index and histological grade. Results: Lesion size measured by CESM closely matched histology (mean 2.16 cm vs. 2.25 cm). Mass-like lesions corresponded mainly to invasive ductal carcinoma, while non-mass patterns aligned with lobular or in situ carcinomas. Enhancement intensity correlated moderately with Ki-67 (Spearman ρ = 0.56, p < 0.001), and contrast heterogeneity showed a weaker but significant correlation with tumor grade (ρ = 0.22, p < 0.05). Conclusions: CESM accurately assesses tumor size and provides functional insight into tumor biology. Enhancement intensity may serve as a non-invasive proliferation marker, while contrast heterogeneity offers additional prognostic data, supporting CESM’s role in personalized breast cancer management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".