title: The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE).
abstract: The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKTinto components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MKAcorrelated with cell eccentricity (r=0.95, p<10-7) and MKIwith the cell density variance (r=0.83, p<10-3). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r=0.80, p<10-3) and microscopic scale (μFA, r=0.93, p<10-6). A multiple regression analysis showed that the conventional MKTparameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MKAwas associated only to cell eccentricity, and MKIonly to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean±s.d.) MKA=1.11±0.33 vs MKI=0.44±0.20 (p<10-3), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MKI=0.57±0.30 vs MKA=0.26±0.11 (p<0.05). In conclusion, DIVIDE allows non-invasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale.
authors.affiliation: Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden. Electronic address: filip.szczepankiewicz@med.lu.se.
authors.forename: Filip
authors.initials: F
authors.lastname: Szczepankiewicz
authors.affiliation: Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden; Skåne University Hospital, Department of Imaging and Function, Lund, Sweden.
authors.forename: Danielle
authors.initials: D
authors.lastname: van Westen
authors.affiliation: Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund, Sweden.
authors.forename: Elisabet
authors.initials: E
authors.lastname: Englund
authors.affiliation: Harvard Medical School, Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA.
authors.forename: Carl-Fredrik
authors.initials: CF
authors.lastname: Westin
authors.affiliation: Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden; Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden.
authors.forename: Freddy
authors.initials: F
authors.lastname: Ståhlberg
authors.affiliation: Skåne University Hospital, Department of Imaging and Function, Lund, Sweden.
authors.forename: Jimmy
authors.initials: J
authors.lastname: Lätt
authors.affiliation: Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden.
authors.forename: Pia C
authors.initials: PC
authors.lastname: Sundgren
authors.affiliation: Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden; Lund University, Lund University Bioimaging Center, Lund, Sweden.
authors.forename: Markus
authors.initials: M
authors.lastname: Nilsson
mesh_terms: D000328:Adult; D000368:Aged; D016880:Anisotropy; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D005910:Glioma / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D008297:Male; D008577:Meningeal Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D008579:Meningioma / Q000000981:diagnostic imaging / Q000473:pathology; D008853:Microscopy / Q000379:methods; D008875:Middle Aged
keywords: Diffusion tensor distribution; Diffusional kurtosis; Diffusional variance; Microscopic anisotropy; Quantitative microscopy; Tumor heterogeneity