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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.

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Many diffusion models have been proposed in order to obtain more information from breast tumor tissues through Magnetic Resonance Imaging (MRI) (1). The Gamma distribution (GD) may model MRI signal decay based on a statistical approach. This model considers the Theta parameter, which indicates the statistical dispersion of the distribution, and the k parameter, which is responsible for the probability distribution shape. If Theta shows higher values, then there will be a more spread out distribution and if k shows lower values the distribution shape will be more affected, which would be expected in malignant tumors due to tissue heterogeneity (1). The purpose of this study was to evaluate if GD model is capable of distinguishing between different breast tumors. Materials and Methods: In this study 85 breast tumor lesions were analyzed, including 17 benign lesions (Fibroadenoma, FA) and 68 malignant lesions (43 Invasive Ductal Carcinomas, IDC; 19 Invasive Lobular Carcinomas, ILC; and 6 Ductal Carcinoma in situ, CDIS). Informed consent was obtained for all patients. Data were acquired using a 3T MRI scanner with a dedicated breast coil and a DWI sequence with 3 orthogonal diffusion gradient directions and 8 b values between 0 and 3000s/mm 2 . Theta and k parameters were acquired from fitting data to the GD model, and mean values were obtained to compare between benign and malignant lesions, and between histological types. Non-parametric statistics were used (α=0.05). Results and Discussion: Significantly lower Theta and higher k values were observed in benign lesions ((0.65±0.43)×10 -3 mm 2 /s, 4.29±1.90, respectively) when compared to malignant lesions ((0.97±0.50)×10 -3 mm 2 /s, 1.23±0.52, respectively). It was also possible to differentiate FA from IDC lesions with both Theta and k probably due to IDC heterogeneity, which restricts diffusion. Unlike other diffusion model parameters, these were able to differentiate FA and ILC, and FA and CDIS lesions, suggesting that the GD model could bring advantages over other diffusion models in characterizing breast tumors. This study was partly funded by Fundação para a Ciência e Tecnologia (FCT) under the grant PEst-OE/SAU/UI0645/2014. REFERENCES[1] Yablonskiy DA, Sukstanskii AL. Theoretical models of the diffusion weighted MR signal. NMR Biomed. 2010;23:661-81.

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