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

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).

To assess the GD model in the characterization and diagnostic performance of breast lesions.

Prospective.

In all, 48 females with 24 benign and 33 malignant breast lesions.

A diffusion-weighted sequence (b = 0-3000 s/mm² ) with a 3 T scanner.

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.

Lesion types were compared regarding diffusion parameters using nonparametric statistics and receiver operating characteristic curve diagnostic performance.

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).

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.