Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI

• Authors: Bing Ji, Emory University; Silun Wang, Emory University; Zhou Liu, Emory University; Brent Weinberg, Emory University; Xiaofeng Yang, Emory University; Tian Liu, Emory University; Liya Wang, Emory University; Hui Mao, Emory University
• Journal Title: NeuroImage: Clinical
• Volume: Volume 23
• Publisher: Elsevier: Creative Commons | 2019-01-01, Pages 101864-101864
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1016/j.nicl.2019.101864
• Subjects: Health Sciences, Oncology; Biology, Neuroscience; Health Sciences, Radiology
• Keywords: Brain tumor; Cerebral blood volume; Dynamic susceptibility contrast; Feature extraction; Magnetic resonance imaging; Signal time course

Abstract

Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions.