Neural network vector quantization improves the diagnostic quality of computer-aided diagnosis in dynamic breast MRI

Abstract

We quantitatively evaluate a novel neural network pattern recognition approach for characterization of diagnostically challenging breast lesions in contrast-enhanced dynamic breast MRI. Eighty-two women with 84 indeterminate mammographic lesions (BIRADS III-IV, 38/46 benign/malignant lesions confirmed by histopathology and follow-up, median lesion diameter 12mm) were examined by dynamic contrast-enhanced breast MRI. The temporal signal dynamics results in an intensity time-series for each voxel represented by a 6-dimensional feature vector. These vectors were clustered by minimal-free-energy Vector Quantization (VQ), which identifies groups of pixels with similar enhancement kinetics as prototypical time-series, so-called codebook vectors. For comparison, conventional analysis based on lesion-specific averaged signal-intensity time-courses was performed according to a standardized semi-quantitative evaluation score. For quantitative assessment of diagnostic accuracy, areas under ROC curves (AUC) were computed for both VQ and standard classification methods. VQ increased the diagnostic accuracy for classification between benign and malignant lesions, as confirmed by quantitative ROC analysis: VQ results (AUC=0.760) clearly outperformed the conventional evaluation of lesion-specific averaged time-series (AUC=0.693). Thus, the diagnostic benefit of neural network VQ for MR mammography analysis is quantitatively documented by ROC evaluation in a large data base of diagnostically challenging small focal breast lesions. VQ outperforms the conventional method w.r.t. diagnostic accuracy.