Breast lesion classification accuracy using intravoxel incoherent motion diffusion modelling is improved by incorporating all parameters and informative Bayesian priors

Strategies for intravoxel incoherent motion (IVIM) diffusion imaging acquisition and analysis are often framed in terms of curve-matching, whereas for breast lesions, classification accuracy against histopathologic assessment is a true metric of functional imaging performance. In this study, we show that IVIM diffusion modelling is best able to discriminate breast lesions (23 benign, 29 malignant) when using all parameters, and when derived from Bayesian methods employing either Gaussian shrinkage or local homogeneity priors, with ROC AUC values increasing from 0.83 (D, conventional least-squares) to 0.92 (D+f+D*, shrinkage prior).