Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation

Autoři: Qing Wang aff001;  Gloria J. Guzmán Pérez-Carrillo aff002;  Maria Rosana Ponisio aff001;  Pamela LaMontagne aff001;  Sonika Dahiya aff003;  Daniel S. Marcus aff001;  Mikhail Milchenko aff001;  Joshua Shimony aff001;  Jingxia Liu aff004;  Gengsheng Chen aff001;  Amber Salter aff005;  Parinaz Massoumzadeh aff001;  Michelle M. Miller-Thomas aff001;  Keith M. Rich aff006;  Jonathan McConathy aff007;  Tammie L. S. Benzinger aff001;  Yong Wang aff001
Působiště autorů: Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, United States of America aff001;  Department of Medical Imaging, Neuroradiology Section, University of Arizona, Tucson, Arizona, United States of America aff002;  Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, United States of America aff003;  Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, United States of America aff004;  Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, United States of America aff005;  Department of Neurosurgery, Washington University in St. Louis, St. Louis, Missouri, United States of America aff006;  Department of Radiology, Division of Molecular Imaging and Therapeutics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America aff007;  Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri, United States of America aff008
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225093



Primary brain tumors are composed of tumor cells, neural/glial tissues, edema, and vasculature tissue. Conventional MRI has a limited ability to evaluate heterogeneous tumor pathologies. We developed a novel diffusion MRI-based method—Heterogeneity Diffusion Imaging (HDI)—to simultaneously detect and characterize multiple tumor pathologies and capillary blood perfusion using a single diffusion MRI scan.


Seven adult patients with primary brain tumors underwent standard-of-care MRI protocols and HDI protocol before planned surgical resection and/or stereotactic biopsy. Twelve tumor sampling sites were identified using a neuronavigational system and recorded for imaging data quantification. Metrics from both protocols were compared between World Health Organization (WHO) II and III tumor groups. Cerebral blood volume (CBV) derived from dynamic susceptibility contrast (DSC) perfusion imaging was also compared with the HDI-derived perfusion fraction.


The conventional apparent diffusion coefficient did not identify differences between WHO II and III tumor groups. HDI-derived slow hindered diffusion fraction was significantly elevated in the WHO III group as compared with the WHO II group. There was a non-significantly increasing trend of HDI-derived tumor cellularity fraction in the WHO III group, and both HDI-derived perfusion fraction and DSC-derived CBV were found to be significantly higher in the WHO III group. Both HDI-derived perfusion fraction and slow hindered diffusion fraction strongly correlated with DSC-derived CBV. Neither HDI-derived cellularity fraction nor HDI-derived fast hindered diffusion fraction correlated with DSC-derived CBV.


Conventional apparent diffusion coefficient, which measures averaged pathology properties of brain tumors, has compromised accuracy and specificity. HDI holds great promise to accurately separate and quantify the tumor cell fraction, the tumor cell packing density, edema, and capillary blood perfusion, thereby leading to an improved microenvironment characterization of primary brain tumors. Larger studies will further establish HDI’s clinical value and use for facilitating biopsy planning, treatment evaluation, and noninvasive tumor grading.

Klíčová slova:

Adenocarcinomas – Biopsy – Cancer detection and diagnosis – Diffusion magnetic resonance imaging – Fluid dynamics – Magnetic resonance imaging – Malignant tumors – Neuroimaging


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2019 Číslo 11