Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion-Weighted MRI: A Preliminary Study

Background The gamma distribution (GD) model is based on the statistical distribution of the apparent diffusion coefficient (ADC) parameter. The GD model is expected to reflect the probability of the distribution of water molecule mobility in different regions of tissue, but also the intra- and extracellular diffusion and perfusion components (f(1), f(2), f(3) fractions). Purpose To assess the GD model in the characterization and diagnostic performance of breast lesions. Study Type Prospective. Population In all, 48 females with 24 benign and 33 malignant breast lesions. Field Strength/Sequence A diffusion-weighted sequence (b = 0-3000 s/mm(2)) with a 3 T scanner. Assessment For each group of benign, malignant, invasive, and in situ breast lesions, the ADC was obtained. Also, theta and k parameters (scale and shape of the statistic distribution, respectively), f(1), f(2), and f(3) fractions were obtained from fitting the GD model to diffusion data. Statistical Tests Lesion types were compared regarding diffusion parameters using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Results The majority of GD parameters (k, f(1), f(2), f(3) fractions) showed significant differences between benign and malignant lesions, and between in situ and invasive lesions (f(1), f(2), f(3) fractions) (P <= 0.001). The best diagnostic performances were obtained with ADC and f(1) fraction in benign vs. malignant lesions (area under curve [AUC] = 0.923 and 0.913, sensitivity = 93.9% and 81.8%, specificity = 79.2% and 91.7%, accuracy = 87.7% and 86.0%, respectively). In invasive lesions vs. in situ lesions, the best diagnostic performance was obtained with f(1) fraction, which outperformed ADC results (AUC = 0.978 and 0.941, and sensitivity = 91.3% for both parameters, specificity = 100.0% and 90.0%, accuracy = 93.9% and 90.9%, respectively). Data Conclusion This work shows that the GD model provides information in addition to the ADC parameter, suggesting its potential in the diagnosis of breast lesions.