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, 14 (11), e0225093
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Heterogeneity Diffusion Imaging of Gliomas: Initial Experience and Validation

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Heterogeneity Diffusion Imaging of Gliomas: Initial Experience and Validation

Qing Wang et al. PLoS One.

Abstract

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Conflict of interest statement

Drs. Yong Wang and Qing Wang are the founders of InnoVoxel LLC, a startup company aiming to validate and clinically translate the diffusion basis spectrum imaging technique. A USA patent related to this technology has been filed (PCT/US2017/049440). Outside of this work, Dr. Tammie Benzinger discloses her relationships with Biogen, Roche, Jaansen, Eli Lilly and Avid Radiopharmaceuticals. Dr. Yong Wang discloses his relationship with Medtronic. The other authors report no conflicts of interest concerning the materials or methods used in this study or the findings specified in this paper. The financial disclosure does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Schematic figure of the isotropic spectrum signals from Heterogeneity Diffusion Imaging.
Isotropic diffusivity is used to define each pathological component within a brain tumor. The isotropic diffusivity cutoffs for each of the pathological components were selected from previous diffusion magnetic resonance imaging studies of brain tumors. Specifically, the isotropic diffusion components with diffusivity that ranged between 0.3 and 0.8 μm2/ms were associated with the dense packing of tumor cells. The components with diffusivity that ranged between 0.8 and 2.5 μm2/ms were associated with extracellular water edema. The components with diffusivity that ranged between 5 and 40 μm2/ms were associated with capillary blood perfusion within tumors.
Fig 2
Fig 2. Neuronavigation-guided anatomic images of biopsy tissue sampling sites.
Neuronavigation-guided T1-weighted images of the biopsy tissue sampling site at (A) axial, (B) coronal, and (C) sagittal views. (D) The tissue sampling site is labeled on the T2-weighted fluid attentuation inversion recovery image. Purple arrows indicate the passive biopsy needle.
Fig 3
Fig 3. Imaging from a woman in her 70s diagnosed with World Health Organization grade II recurrent oligodendroglioma.
(A) The T1-weighted post-contrast image shows a lesion with decreased signal intensity. (B) The fluid-attenuated inversion recovery image and (C) the diffusion magnetic resonance imaging-derived apparent diffusion coefficient show a lesion with an increased signal. (D) The dynamic susceptibility contrast-derived cerebral blood volume map and the Heterogeneity Diffusion Imaging-derived (E) cellularity fraction, (F) slow hindered diffusion fraction, (G) fast hindered diffusion fraction, and (H) perfusion fraction maps were generated on manually defined tumor regions and overlaid on the fluid-attenuated inversion recovery image. No elevated cerebral blood volume and Heterogeneity Diffusion Imaging-derived slow hindered diffusion fraction and perfusion fraction are shown in the tumor region. The elevated Heterogeneity Diffusion Imaging-derived cellularity fraction and fast hindered diffusion fraction are shown in the tumor region.
Fig 4
Fig 4. Imaging from a man in his 50s diagnosed with World Health Organization grade III oligodendroglioma.
(A) The T1-weighted post-contrast image shows a lesion with decreased signal intensity. (B) The fluid-attenuated inversion recovery image and (C) the diffusion magnetic resonance imaging-derived apparent diffusion coefficient show a lesion with an increased signal. (D) The dynamic susceptibility contrast-derived cerebral blood volume map and the Heterogeneity Diffusion Imaging-derived (E) cellularity fraction, (F) slow hindered diffusion fraction, (G) fast hindered diffusion fraction, and (H) perfusion fraction maps were generated on manually defined tumor regions of interest and overlaid on the fluid-attenuated inversion recovery image. The elevated cerebral blood volume and Heterogeneity Diffusion Imaging-derived cellularity fraction, slow hindered diffusion fraction, fast hindered diffusion fraction, and perfusion fraction are shown in the tumor region.
Fig 5
Fig 5. Boxplots of imaging metrics.
There is no group significant difference in (A) apparent diffusion coefficient or (B) Heterogeneity Diffusion Imaging (HDI)-derived cellularity fraction between the World Health Organization (WHO) II and III groups. (C) The HDI-derived slow hindered diffusion fraction is significantly higher in the WHO III group as compared with the WHO II group. (D) There is no group significant difference in HDI-derived fast hindered diffusion fraction between the WHO II and III groups. (E) The cerebral blood volume is significantly higher in the WHO III group as compared with the WHO II group. (F) The HDI-derived perfusion fraction is significantly higher in the WHO III group as compared with the WHO II group. Boxes indicate 25th to 75th percentiles, and thin lines indicate 5th and 95th percentiles. *, P < .05.
Fig 6
Fig 6. The associations between dynamic susceptibility contrast-derived cerebral blood volume and Heterogeneity Diffusion Imaging-derived indices.
Scatter plots showing the significant correlations between (A) dynamic susceptibility contrast perfusion imaging-generated cerebral blood volume (CBV) and Heterogeneity Diffusion Imaging (HDI)-derived perfusion fraction and (B) CBV and HDI-derived slow hindered diffusion fraction in all subjects at the tissue sampling regions. No significant correlations were found between (C) CBV and HDI-derived cellularity fraction or (D) CBV and HDI-derived fast hindered diffusion fraction.

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