Integrating qualitative and quantitative features from contrast-enhanced mammography to predict breast lesion malignancy
Author Block: I. Allajbeu1, R. Manavaki1, T. Wegman2, J. A. Saenz3, T. Van Nijnatten2, F. J. Gilbert1; 1Cambridge/UK, 2Maastricht/NL, 3Barcelona/ES
Purpose: Quantitative analysis of enhancement on contrast-enhanced mammography (CEM) has shown promise in distinguishing benign from malignant breast lesions. We evaluated diagnostic models combining low-energy (LE) mammographic features with quantitative enhancement metrics.
Methods or Background: Data from 251 CEM examinations (2018-2021) with identifiable, histologically confirmed lesions were retrospectively analysed. Lesion characteristics, including lesion diameter (LD), type (mass, calcification, distortion, asymmetry), background parenchymal enhancement (BPE), breast density (BD), and conspicuity, were assessed on LE and recombined images by three radiologists using BI-RADS criteria. Enhancement metrics were computed from early (CEearly) and late (CElate) views, with percent residual signal difference (%RSD) used to classify enhancement patterns as progressive, plateau, or wash-out. Model construction utilised nested cross-validation (CV) with stratified sampling. In each of ten outer folds, data were stratified into 90% training and 10% testing subsets. Feature selection was optimised within the training data using five-fold inner CV. Minimum redundancy–maximum relevance identified non-redundant, informative predictors for logistic regression. Performance was assessed on the outer test sets and averaged across folds using area-under-the-curve (AUC), accuracy, sensitivity, and specificity.
Results or Findings: Of 251 lesions, 155 (61.7%) were malignant and 96 (38.3%) benign. Eleven features (age, LD, type, BI-RADS score, BD, BPE, conspicuity, CEearly, CElate, RSD, enhancement type) were considered for model construction. The most predictive subset included BI-RADS score, age, lesion conspicuity, BD, CELate, and LD. Models with these features outperformed those using BI-RADS score alone, with higher AUC (0.88 vs 0.84), accuracy (83% vs 78%), sensitivity (89% vs 72%), and comparable specificity (85%).
Conclusion: Integrating standard BI-RADS descriptors with quantitative enhancement metrics can improve lesion discrimination on CEM versus BI-RADS alone. This combined approach may enhance diagnostic confidence and reduce unnecessary biopsies in clinical practice
Limitations: Single-centre study
Funding for this study: Cambridge Biomedical Research Centre (BRC)
CUH NHS Foundation Trust
Has your study been approved by an ethics committee? Not applicable
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