Synopsis
New non-Gaussian measurement techniques like
neurite orientation dispersion and density imaging (NODDI) that allow quantification of specific tissue
microstructure features can provide meaningful biophysical indices to overcome the
low specificity of DTI. In this study we applied three compartment model based NODDI and DTI to histopathology and explored the correlation with tumor cellularity between non-enhancing and contrast enhancing lesions. Unlike in normal brain where Vin represents the neurite density, it was positively correlated with tumor grade and
tumor score in tissue samples from the tumor region, indicating the association of Vin with tumor cellularity. Although NODDI is not directly built on tumor,
it brings parameters that were sensitive to tumor cellularity, which may
complement the conventional DTI model and adds specificity. Thus NODDI, when combined with DTI, could add value
in understanding the heterogeneity of tissue microstructure in brain tumors.Introduction
Diffusion tensor imaging (DTI) is a widely used
technique in the diagnosis and monitoring of response to therapy in patients
with brain tumors
1-5. It has high sensitivity to underlying tissue
structure but low specificity
6. Recent work has shown the application of neurite
orientation dispersion and density imaging (NODDI) to various neurological disorders
including brain tumors
7-11 to overcome the low specificity of DTI.
Purpose
In this study, we have validated the association
of NODDI and DTI with histopathology features in different tumor grades, as it will be highly valuable to the clinical
community to confirm the diagnostic potential of these techniques7.
Methods
The association
of diffusion parameters and tumor cellularity is explored within non-enhancing
(NE) and contrast enhancing (CE) regions.
Patient Population:
A total of 92 tissue samples from 34 patients with brain tumors (Figure 1),
each with 1-4 tissue samples, were analyzed. Of these samples, 16 were from CE
lesion (CEL), 68 were from NE lesion (NEL; T2 lesion - CEL), and 8 were at the
border.
Histopathology parameters included tumor score (0 =
neuropil without tumor, 1=infiltrating margin, 2=infiltrating tumor cells,
3=high percentage of tumor cells), angiogenesis (0=absent, 1=circumferential or simple, 2=complex),
proliferation (% of all cells positive for MIB-1) and tumor grade.
Data Acquisition:
Two-shell diffusion data including 24 gradient directions with b=1000 s/mm2
and 55 gradient directions with b=2000 s/mm2 were acquired with a
standard SE-EPI sequence at a nominal voxel size of 2×2×2 mm3. Standard T1-weighted pre- and post-contrast
and T2 FLAIR anatomical images were also acquired for region of interest (ROI)
analysis.
Data Processing:
The diffusion data were eddy-current corrected and DTI models were fit to the
combined shell data with weighted least squares tensor fitting using FSL12. NODDI parameters were
estimated as suggested in the original NODDI model13.
Rigid alignment was performed between T2 FLAIR images and b0 images and the
transformation was applied to the results for analysis of ROIs. The latter included
the T2 hyper-intensity lesion (T2L) on T2 FLAIR images, the contrast-enhancing
lesion (CEL) on T1 post-gad images and normal appearing white matter (NAWM) as
segmented from T1-weighted pre-gad images. Image guided tissue targets were
marked on the BrainLab surgical navigation system and the center coordinates of
acquired tissue samples were used to define spherical ROIs of 5 mm diameter. Median
intensity values of apparent diffusion coefficient (ADC), fractional anisotropy
(FA), and radial diffusivity (RD) from DTI and intra neurite volume fraction (Vin),
extra cellular volume fraction (Vec) and orientation dispersion
index (ODI) from NODDI were analyzed within each ROI using automated software
developed in our research group14.
Statistical
analysis: Significant differences between tumor scores and tissue types
were assessed using a Mann Whitney test. Categorical outcome variables were
tested for association with each imaging parameter using ordinal logistic
regression and continuous outcome variables were tested using Pearson
correlation. P-values ≤ 0.05 were considered statistically significant in this
exploratory analysis.
Results and Discussion
Median values of DTI and NODDI parameters within each ROI for
different grades are shown in Figure 2. In all tumor grades, Vin was
reduced compared to NAWM, indicating lower neurite density. Within grade 4
tumors, a higher Vin was observed in both the CEL and CE tissue
samples compared to the NEL and NE tissue samples, consistent with the results
found by Wen et al12.
While increasing and decreasing trends were observed for
Vin and ADC with tumor score respectively (Figure 3), only a statistically
significant difference in Vin (p<0.02) was found between CE
tissue samples with a tumor score of 2 vs 3.
Across all grades, Vin values showed a positive
correlation with tumor score (p<0.002) [Table 1], indicating that Vin could
be a measure of tumor cellularity. Vec was negatively associated
with tumor score (p<0.002) as was ADC (p<0.04) similar to previous
studies15. Although FA and ODI did not
correlate with tumor score, ODI was elevated in CE compared to NE tissue
(Figure 2d). This could suggest less coherently organized structures16 with increased cellularity in CE tissue. Only FA showed a positive correlation
with angiogenesis (p<0.05). All of the imaging parameters were associated
with tumor grade.
Conclusion
Both ADC and NODDI parameters V
in and V
ec
were significantly correlated with brain tumor histopathology. Although NODDI
is not directly built on tumor, it brings parameters that were sensitive to
tumor cellularity, which may complement the conventional DTI model and adds
specificity. In particular, V
in and ADC have distinct variations
within CE and NE regions that when combined can offer additional insight into
the heterogeneity of tissue microstructure among brain tumors.
Acknowledgements
This work was supported by the SPORE and P01 grants: P50-CA97257 and P01CA118816.References
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