Diffusion-weighted MR image analysis based on gamma distribution model for differentiating benign and malignant brain tumors

Abstract

Background: Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors. Methods: A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm(2) with a 1.5 T scanner. The gamma distribution model is expected to demonstrate the probability of water molecule distribution based on the apparent diffusion coefficient. For all tumors, the apparent diffusion coefficient, shape parameter (kappa), and scale parameter (theta) were calculated for each b-value. In the staging step, the fractions (f(1), f(2), f(3)) expected to reflect the intracellular, and extracellular diffusion and perfusion were investigated. Diffusion <1 x 10(-4) mm(2)/s (f(1)), 1 x 10(-4) mm(2)/s < Diffusion > 3 x 10(-4) mm(2)/s (f(2)), and Diffusion >3 x 10(-4) mm(2)/s (f(3)); in the grading step, fractions were determined to check heavily restricted diffusion. Diffusion lower than 0.3 x 10(-4) mm(2)/s (f(11)). Diffusion lower than 0.5 x 10(-4) mm(2)/s (f(12)). Diffusion lower than 0.8 x 10(-4) mm(2)/s (f(13)). Results: The findings were analyzed using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Gamma model parameters (kappa, f(1), f(2), f(3)) showed a satisfactory difference in differentiating meningioma from glioma. For b value = 2000 s/mm(2), f(1) had a better diagnostic performance than kappa and apparent diffusion coefficient (sensitivity, 88; specificity, 68; P < .001). The best diagnostic performance was related to f(3) in b = 2000 s/mm(2) (area under the curve = 0.891, sensitivity = 83, specificity = 80, P < .001). In the grading step, f(12) (area under the curve = 0.870, sensitivity = 92, specificity = 72, P < .001) had the best diagnostic performance in differentiating high-grade from low-grade gliomas with b = 2000 s/mm(2). Conclusion: The findings of our study highlight the potential of using a gamma distribution model with diffusion-weighted imaging based on multiple b-values for grading and staging brain tumors. Its potential integration into routine clinical practice could advance neurooncology and improve patient outcomes through more accurate diagnosis and treatment planning.