Role of diffusion tensor imaging and diffusion metrics in primary, secondary brain tumours and tumour mimics

Abstract

Background and Aim: Diffusion Tensor Imaging studies provide a valuable information regarding the status of white matter tracts adjacent to the intracranial brain lesions. The quantitative nature of Diffusion Tensor Imaging will play a role in assessing the outcome of clinical trials, as an additional surrogate marker in monitoring the therapeutic response. The aim of our study were to determine the Mean Diffusivity and Fractional Anisotropy coefficients in the affected white matter tracts and thereby to compare the Fractional Anisotropy and Mean Diffusivity of the affected white matter tracts in primary, secondary brain tumours and tumour like lesions.Materials and Methods: From December 2018 to September 2019, 49 patients were included in the study who were referred to the department for conventional Magnetic Resonance Imaging. The histopathological diagnosis was obtained and confirmed in patients who have undergone surgical resection.Results: Diffusion Tensor Imaging studies provide a valuable information regarding the status of white matter tracts adjacent to the intra cranial brain lesions. Fractional Anisotropy depends on the orientation and density of white matter fibres and Mean Diffusivity depends on the degree of perilesional edema. Gliomas present with white matter fibre destruction and less vasogenic edema when compared to tumour like lesions, and a higher perilesional Apparent Diffusion Coefficient favours a non-neoplastic etiology. Thus, the Diffusion Tensor Imaging sequences may help in differentiating the brain lesions of different aetiologies.Conclusion: The quantitative nature of Diffusion Tensor Imaging will play a role in assessing the outcome of clinical trials, as an additional surrogate marker in monitoring the therapeutic response. Careful studies to validate Diffusion Tensor Imaging and its metrics will allow it to become more applicable clinically and can affect therapeutic decision-making, choosing appropriate treatment and eventually patient outcome.