Keywords
diffusion tensor imaging - high-grade glioma - solitary brain metastases - mean diffusivity
and fractional anisotropy
Introduction
Brain tumors represent a diverse and complex group of neoplastic disorders that can
significantly impact both patient morbidity and mortality.[1] Among the various types of brain tumors, differentiating between them is of paramount
importance due to their distinct clinical management and prognostic implications.[2] High-grade gliomas (HGGs), characterized by their aggressive growth and infiltrative
nature within the brain parenchyma, present a unique diagnostic challenge.[3] Conversely, solitary brain metastases (SBMs), originating from primary tumors outside
the central nervous system, demand precise identification to guide treatment decisions
and determine prognosis.[4]
Conventional structural magnetic resonance imaging (MRI) techniques have provided
crucial insights into the anatomical characteristics of brain tumors. However, accurately
distinguishing between HGG and SBM remains challenging due to overlapping radiological
features such as contrast enhancement and peritumoral edema. The limited specificity
of conventional MRI has spurred the exploration of advanced imaging modalities that
can provide deeper insights into the underlying tissue microstructure and pathophysiology
of these tumors, including diffusion-weighted imaging, diffusion tensor imaging (DTI),
dynamic susceptibility imaging, dynamic contrast enhancement, and MR spectroscopy.[5]
DTI has emerged as a powerful neuroimaging technique that offers unique information
about tissue microarchitecture by probing the diffusion of water molecules within
biological tissues. The diffusion properties of water are influenced by barriers such
as cell membranes, extracellular matrix, and fiber tracts. As a result, DTI can provide
valuable information about tissue integrity, cellular density, and fiber orientation.
In brain tumor research, DTI has shown promise in elucidating the underlying differences
in tissue microstructure between different tumor types.[6]
The ability of DTI to quantify metrics such as mean diffusivity (MD) and fractional
anisotropy (FA) has raised significant interest in its potential to discriminate between
HGG and SBM. MD reflects the overall magnitude of water diffusion and can be influenced
by factors such as cellularity and tissue disruption. FA, on the other hand, provides
information about the directionality and coherence of water diffusion, thereby offering
insights into tissue microarchitecture and organization. These metrics have demonstrated
sensitivity to changes in tissue composition and disruption caused by pathological
processes.[7]
[8] Using these diffusion metrics, varying success has been achieved in distinguishing
between “infiltrative edema” of HGG and “vasogenic edema” of metastases. There also
seem to be no significant differences in the intratumoral metrics of these two entities.[9]
[10]
[11]
[12]
Peritumoral vasogenic edema exhibits notably elevated MD compared with infiltrative
edema, primarily attributed to the significant increase in water accumulation in contrast
to the cellular infiltration seen in glioblastoma. On the other hand, differences
in peritumoral FA between the two pathologies are not consistently observed. This
discrepancy might arise from the varying proportions of normal white and gray matter,
intracellular water, and tumor cell infiltration present within infiltrative edema.[13]
In addition to MD and FA, DTI provides several other scalar metrics like axial diffusivity
(AD), radial diffusivity (RD), as well as planar (CP), spherical (CS), and linear
(CL) anisotropy coefficient.[8] These metrics have been studied in glioma grading,[14]
[15] but have not been utilized to differentiate HGG and SBM.
This study aims to contribute to the growing body of research investigating the utility
of DTI-derived metrics in differentiating between HGG and SBM. By evaluating these
values within both intratumoral and peritumoral regions, we aim to assess whether
there are any significant differences between the two tumor types. The potential implications
of our findings extend to enhancing clinical decision-making, prognostic assessment,
and treatment planning for patients with brain tumors. Furthermore, a deeper understanding
of the distinct DTI profiles associated with HGG and SBM may shed light on the underlying
biological mechanisms driving their behavior and progression.
Materials and Methods
The study was commenced after getting clearance from the Institutional Ethical Committee.
We prospectively enrolled 41 patients over 2 years, presenting with a solitary enhancing
lesion on postcontrast T1 images. Patients with a previous history of cranial surgery,
chemotherapy, or radiotherapy, general contraindications for MRI, contrast allergy,
and multiple parenchymal enhancing lesions were excluded from the study.
All the patients underwent total or subtotal resection, and the diagnosis was confirmed
on histopathology.
Imaging technique and data analysis: MRI was performed using a 3-Tesla scanner (GE Architect) using a Head 48 channel
coil, the T1 (TR/TE, 7700/1000 ms), T2 (TR/TE, 5742/1000 ms), and fluid-attenuated
inversion recovery (TR/TE/TI, 7500/120/1000 ms) sequences with matrix 256 × 256, field
of view (FOV) 24 × 24 (frequency and phase FOV) and slice thickness of 1.8 mm were
obtained. Post-intravenous contrast T1 image study was done using gadolinium-based
contrast (gadobuterol) (0.5 mL/kg [0.1 mmol/kg] body weight with the maximum dose
of 10 mL at a flow rate of ∼2 mL/s).
DTI data were obtained using a single-shot echo planar imaging sequence (TR/TE 3941/1000 ms)
with parallel imaging that includes two strong encoding gradients as described by
Stejskal-Tanner.[16] Diffusion gradients were applied along 50 directions, using a b-value of 0 and 1,000 second mm2. The frequency FOV was 26, the phase FOV was 1 and a data matrix of 128 × 128 was
used. Thirty-eight slices were obtained, a thickness of 3 mm, with no gap, and a total
scan duration of approximately 3 to 4 minutes.
These gradients are balanced concerning stationary protons, induce a diffusion-dependent
phase dispersion, and result in signal loss in areas with higher diffusion. The diffusion
sensitivity of the pulse sequence is expressed as the b-value in units s/m2 and is related to the duration and strength of the diffusion encoding gradient. Measurements
including two b-values (for example, b = 0 second mm2 and b= 1,000 second mm2) allow for plotting the logarithmic signal intensity versus the b-value for a region-of-interest (ROI) or a volume element (voxel).
The data was transferred to the GE workstation for postprocessing, and the images
were analyzed using software with automated generation of diffusion maps. After using
a correction algorithm to compensate for head motion and image distortion due to the
eddy currents, the FA, MD, AD, RD, CL, CS, and CP diffusion tensor maps were generated.
In the context of the DTI model, FA indicates the degree of asymmetry in diffusion
within a voxel, with values theoretically ranging between 0 (completely isotropic
diffusion) and 1 (completely anisotropic diffusion). MD indicates how much water molecules
are spreading out within a voxel, giving an overall measure of molecular dispersion.
AD and RD refer to the average rates of diffusion of water molecules in specific directions.
AD measures the average diffusion parallel to the primary direction indicated by the
largest eigenvalue of the diffusion tensor, while RD quantifies the average diffusion
perpendicular to this primary direction.[17]
CL, CP, and CS provide additional information about the characteristics of the diffusion
ellipsoid's shape. CL indicates how elongated or linear the ellipsoid is, with a focus
on diffusion occurring predominantly along the axis corresponding to the largest eigenvalue.
CP, on the other hand, quantifies how the ellipsoid is flattened or planar, emphasizing
diffusion primarily within the plane formed by the two eigenvectors associated with
the two largest eigenvalues. Lastly, CS measures the degree of spherical shape or
isotropic diffusion within the ellipsoid.[17]
Before the evaluation of the diffusion maps, conventional MRI sequences were assessed
for lesion localization, characterization, and enhancement pattern. For quantitative
evaluation, we manually placed four circular ROIs (ROIs: 5 mm2) within the enhancing component of the lesion and in the region of peritumoral edema
(within 1 cm from the enhancing lesion) in an orthogonal orientation on postcontrast
T1 images and transferred to the DTI postprocessed maps. The ROIs were selected manually
by an experienced radiologist with more than 4 years of experience in DTI analysis
(S.R.K.), aiming to maintain consistency. Each ROI was placed in the most enhanced
region to ensure consistency and focus on the areas of maximal tumor cellularity and
enhancement.
Means of all the diffusion metrics across four ROIs of intratumoral and peritumoral
regions were obtained from DTI postprocessed images and not normalized to cerebral
white matter.
Statistical Analysis
The Statistical Package for the Social Sciences, version 23, was used to conduct statistical
analysis (SPSS-23, IBM, Chicago, Illinois, United States).
The Mann–Whitney test was used to compare the diffusion metrics values between the
two groups. Statistical significance was defined as a p-value of < 0.05.
The results of the Mann–Whitney U test are tabulated in [Table 1].
Table 1
Results of Mann–Whitney U test
|
DTI metric
|
Group
|
50th percentile
|
p-Value (significant p-Value < 0.05)
|
|
Intratumoral FA
|
SBM
HGG
|
0.171
0.166
|
0.277
|
|
Intratumoral MD
|
SBM
HGG
|
1.301
1.092
|
0.5824
|
|
Intratumoral AD
|
SBM
HGG
|
0.920
0.981
|
0.4831
|
|
Intratumoral RD
|
SBM
HGG
|
0.983
1.012
|
0.9233
|
|
Intratumoral CP
|
SBM
HGG
|
0.011
0.037
|
0.7415
|
|
Intratumoral CS
|
SBM
HGG
|
0.996
0.992
|
0.8152
|
|
Intratumoral CL
|
SBM
HGG
|
0.002
0.001
|
0.9015
|
|
Peritumoral FA
|
SBM
HGG
|
0.112
0.216
|
0.0217 (significant)
|
|
Peritumoral MD
|
SBM
HGG
|
1.508
1.246
|
0.2264
|
|
Peritumoral AD
|
SBM
HGG
|
1.68
1.47
|
0.0857
|
|
Peritumoral RD
|
SBM
HGG
|
1.68
1.46
|
0.2714
|
|
Peritumoral CP
|
SBM
HGG
|
0.02
0.009
|
0.1824
|
|
Peritumoral CS
|
SBM
HGG
|
1.018
0.997
|
0.8690
|
|
Peritumoral CL
|
SBM
HGG
|
0.028
0.018
|
0.0392 (significant)
|
Abbreviations: AD, axial diffusivity; CL, linear coefficient; CP, planar coefficient;
CS, spherical coefficient; DTI, diffusion tensor imaging; FA, fractional anisotropy;
HGG, high-grade glioma; MD, mean diffusivity; RD, radial diffusivity; SBM, solitary
brain metastasis.
Results
The study population consisted of 27 HGGs (grade III and IV) (19 men, 8 women, age
range 40–75 years, mean age 55 ± 11.2 [standard deviation]) and 14 SBMs (6 men, 8
women, age range 35–80 years, mean age 58 ± 14.1 [standard deviation]). Of the 14
patients with SBM, 9 patients had primary lung malignancy, 3 had colon adenocarcinoma,
and 2 had breast carcinoma.
There is a significant difference in peritumoral FA (p = 0.0217) and peritumoral CL (p = 0.039) between the two groups when analyzed using the Mann–Whitney U test. The median value was higher in the HGG group compared with SBM in the case
of peritumoral FA, while it was higher in SBM compared with HGG in the case of peritumoral
CL. At the receiver operating characteristic curve, the area under the curve (AUC)
of peritumoral FA used to differentiate HGG and SBM was 0.2791. Similarly, the AUC
of peritumoral CL was 0.6984. Box plots describing peritumoral FA and CL between SBMs
(group 1) and HGGs (group 2) are provided in [Fig. 1].
Fig. 1 (A, B) In the graphical representation, the interquartile range between the 25th and 75th
percentiles is depicted by boxes. The medians are denoted by the central lines within
these boxes. The vertical bars extending from the boxes, referred to as whiskers,
illustrate the data range excluding outliers. Group 1: solitary brain metastasis and
group 2: high-grade glioma. Print resolution: 600 dpi. Size (Px): W: 1138 H: 414.
Print size: 4.82 * 1.75 cm.
There were no significant statistical differences in the other five diffusion metrics
between the two groups. Representative cases of HGG and SBM are depicted in [Figs. 2] and [3].
Fig. 2 (A–D) A 45-year-old female presented with headache and seizures. (A) Postcontrast axial T1 image shows an enhancing lesion with peritumoral edema. (B) Axial fluid-attenuated inversion recovery (FLAIR) image shows the peritumoral edema
associated with the lesion. Representative regions of interest (ROIs) are placed in
the enhancing area and peritumoral area in orthogonal orientation. (C and D) Axial fractional anisotropy and mean diffusivity map of the lesion with ROIs. Histopathological
diagnosis: Anaplastic astrocytoma World Health Organization (WHO) grade III. Print
resolution: 600 dpi. Size (Px): W: 3007 H: 921. Print size: 5.01 * 1.54 inches.
Fig. 3 (A–C) A 55-year-old male presented with right-sided weakness. (A) Postcontrast axial T1 image shows an enhancing lesion with central nonenhancing
areas and peritumoral edema. Representative regions of interest (ROIs) are placed
in the enhancing area and peritumoral area in orthogonal orientation. (B) Axial fluid-attenuated inversion recovery (FLAIR) image shows the peritumoral edema
associated with the lesion. (C and D) Axial fractional anisotropy and mean diffusivity map of the lesion with ROIs. Histopathological
diagnosis: Metastasis from primary lung malignancy. Print resolution: 600 dpi. Size
(Px): W: 3190 H: 967. Print size: 5.32 * 1.61 inches.
Discussion
The ability of the DTI-generated parameters from the lesion and perilesional edema
to differentiate between several groups of brain tumors has been statistically evaluated
in this study. This was predicated on the fact that perilesional edema in brain lesions
might vary in composition. The majority of benign lesions exhibit vasogenic perilesional
edema; however, gliomas also exhibit infiltrating cells. In this investigation, variations
in the DTI parameters were assessed.
HGGs have higher perilesional FA because glioblastomas have more peritumoral infiltration
than metastases and produce a more tumor-specific extracellular matrix, which results
in increased anisotropy, as found in our study.[14] Extracellular water is a key indicator of MD because metastatic lesions express
high levels of vascular endothelial growth factor, which promotes vascular permeability.
A larger perilesional MD in metastases than in HGGs can be attributed to the latter
cause; however, it was found to be statistically insignificant.[10]
In our study, peritumoral CL tensor was higher in SBM when compared with HGG. This
finding was contrary to previous studies, where peritumoral CL was higher in HGG.[18]
[19] Varying compositions of edema, tumor infiltration, normal gray/white matter, and
the adjacent tracts in the immediate peritumoral region complicate the identification
of the precise factors responsible for the variations in these metrics.[5]
Wang et al[20] studied pure isotropic diffusion (p), pure anisotropic diffusion (q), and the total
magnitude of the diffusion tensor (L) and FA parameters in glioblastoma multiforme
(GBM) and brain metastases, revealing significantly reduced q and FA of the gross
tumor portion, and q in the peritumoral margin in the GBM group. They also concluded
that none of these metrics can distinguish between these two groups by itself.
Several studies have concluded that the enhancing region of GBM had higher FA, CP,
and CL, and lower CS as compared with metastases.[19]
[21] Regression coefficients of RD and AD were significantly higher in the tumor-infiltrated
edema, as compared with pure vasogenic edema.[22] As MD and RD are inversely related to the degree of tumor cellularity, lower intratumoral
MD and RD have been found in HGG when compared with SBM.[23]
A meta-analysis conducted by Jiang et al revealed higher FA and lower MD in the peritumoral
areas of HGGs when compared with metastasis.[10] Another meta-analysis by Suh et al revealed a pooled sensitivity of 77.0% (95% confidence
interval [CI], 62.3–87.1%) and a pooled specificity of 80.3% (95% CI, 73.5–85.7%)
for differentiating these two entities based on DTI, with peritumoral MD showing the
highest sensitivity and specificity.[9]
Newer diffusion imaging techniques such as neurite orientation dispersion and density
imaging (NODDI), mean apparent propagator MRI, and diffusion kurtosis imaging (DKI)
have been studied, with NODDI demonstrating the highest performance.[23]
There are several drawbacks in this study. The first is the relatively small sample
size in this study, thereby limiting the extrapolation of our conclusions. The second
limitation is the subjective assessment and manual placement of ROI. Since we used
a predefined size for ROI, placed them within 1 cm of the enhancing region, utilized
multiple ROIs, and used the mean value, this limitation was negated to a certain extent.
However, the possibility of exclusion of infiltrative cells in the peritumoral edema
from measurement cannot be ruled out.[24] This limitation can be negated to a certain extent by using semiautomated methods
to obtain a greater sampling area,[25] and by using an interrater reliability analysis.[12] Though histopathological diagnosis was available for all the cases, the peritumoral
area was not assessed during biopsy/surgery, though several studies have correlated
DTI and pathological findings.[26]
[27] Utilizing average parameter values in frequently heterogeneous areas may not consistently
provide the most accurate representation of anisotropy within the region.[19] Further analysis of SBM based on their primary origin could not be assessed due
to limited data.
Conclusion
Our study highlights the potential utility of DTI-derived metrics in distinguishing
between HGGs and SBMs. Specifically, peritumoral FA and linear anisotropy coefficient
(CL) demonstrated significant differences between the two tumor types. The observed
higher peritumoral FA in HGG and elevated peritumoral CL in SBM suggest distinct microstructural
characteristics in these regions. While these findings contribute to the growing body
of research, the study acknowledges limitations such as a relatively small sample
size and subjective ROI placement. Nonetheless, the insights gained from this investigation
could have implications for refining clinical decision-making, prognostic assessment,
and treatment planning for patients with brain tumors. Future research endeavors,
encompassing larger cohorts and advanced diffusion imaging techniques, may further
elucidate and validate these findings, paving the way for more precise characterization
and management of these diverse brain neoplastic disorders.
