Optimal b-values for diffusion kurtosis imaging in invasive ductal carcinoma versus ductal carcinoma in situ breast lesions.

Diffusion kurtosis imaging (DKI) is a diffusion-weighted MRI technique that probes the non-Gaussian diffusion of water molecules within biological tissues. The purpose of this study was to investigate the DKI model optimal b-values combinations in invasive ductal carcinoma (IDC) versus ductal carcinoma in situ (DCIS) breast lesions. The study included 114 malignant breast lesions (64 IDC and 50 DCIS). Patients underwent a breast MRI examination which included a diffusion-weighted sequence (b = 0-3000 s/mm2). For each lesion, the b-values were combined among each other (109 combinations) and each mean kurtosis (MK) parameter was obtained. Differences between the lesion groups and b-values combinations were assessed. Also, the diagnostic performance of the combinations was determined through receiver operating characteristic (ROC) curve analysis, and compared. Root mean square error (RMSE) was also obtained. All the b-values combinations showed significant differences between the lesion groups (p < 0.05). The combination 0, 50, 200, 750, 1000, 2000 s/mm2 showed the best performance (AUC = 0.930, sensitivity = 95.3%, specificity = 82.0%, accuracy = 89.5%), with a RMSE of 17.65. The b-values combinations with the worst performance were composed of only high or ultra-high b-values, or with b = 1000 s/mm2 as the maximum b-value. Better results were obtained when zero b-value was included in the DKI model fitting with at least one b-value below 1000 s/mm2 and one b-value above 1000 s/mm2 (conserving b = 1000 s/mm2). Six was the optimal number of b-values, nonetheless other combinations with less b-values may be considered, but with a consequent diagnostic performance loss.

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 (f1 , f2 , f3 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/mm2 ) with a 3 T scanner. ASSESSMENT: For each group of benign, malignant, invasive, and in situ breast lesions, the ADC was obtained. Also, θ and k parameters (scale and shape of the statistic distribution, respectively), f1 , f2 , and f3 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, f1 , f2 , f3 fractions) showed significant differences between benign and malignant lesions, and between in situ and invasive lesions (f1 , f2 , f3 fractions) (P ≤ 0.001). The best diagnostic performances were obtained with ADC and f1 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 f1 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. Level of Evidence 2: Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:230-238.