Brain metastases, inflammation, and imaging
It is worth noting that CSCs constitute the main cell population
mediating metastasis [57]. Brain metastases are more common than
tumors originating primarily in the brain and confer grave prognosis to
patients with various types of primary cancers originating in other
sites, such as lung, breast, colorectal, and melanoma, given that
available treatments show very limited efficacy. The tumor
microenvironment is crucial to determine the establishment of brain
metastases, and within this context, neuroinflammation plays a central
role [58].
Brain metastases can produce a tissue lesion that induces a response
comprising persistent astrocyte and microglia activation with cytokine
and chemokine release, increased blood vessel permeability and
recruitment of immune cells, resulting in neuroinflammation [58-61].
Also, non-neoplastic inflamed sites in the brain may facilitate the
adhesion of circulating tumor cells from peripheral tumors to the
activated endothelium in brain vessels, which is one of the possible
mechanisms promoting metastasis initiation [62]. Inflammation at
distant sites promotes adhesion of CTCs to the activated endothelium and
then initiates the formation of metastases. Many different types of
mediators and immune cells are involved in brain metastasis, depending
among other factors on the type of primary tumor of origin, metastatic
site in the brain, and differential astrocyte and microglial activation,
resulting in high biological heterogeneity [58, 63-67].
In terms of imaging, differentiating brain tumors from brain metastases
pose a challenge by itself, as both present imaging patterns with
similar peritumoral hyperintensities and intratumoral texture on MRI.
Improvements have been obtained with the use of relative cerebral blood
flow and volume analyses, diffusion tensor imaging, neurite orientation
dispersion, density imaging to examine intracellular volume fraction,
isotropic volume fraction, and extracellular volume fraction, and
metabolite analysis with MR spectroscopy. Also, intratumoral creatine
suggests GBM, whereas its absence indicates metastasis when single-voxel
proton MR spectroscopy is used for imaging [68, 69]. A study
examining the use of the mean apparent diffusion coefficient and
absolute standard deviation derived from apparent diffusion coefficient
measurements based on cellularity levels could not find marked
differences between GBM tumors from brain metastasis [70]. However,
another study found that the apparent diffusion coefficient could
differentiate GBM from metastasis, and that homogeneity and the inverse
difference moment of GBM were significantly higher than those of
metastases in the regions of interest placements examined [71, 72].
Assessing the heterogeneity of both the tumor masses and peritumoral
edema with MRI texture analysis revealed that the heterogeneity of the
GBMs peritumoral edema was significantly higher than the edema
surrounding MET, differentiating them with a sensitivity of 80% and
specificity of 90% [73]. Combining arterial spin labeling perfusion
(ASL)- and diffusion tensor imaging (DTI)-derived metrics showed to be
promising in differentiating GBM and solitary brain metastases [74].
The use of 2D texture features extracted from images obtained with MRI
may be a useful alternative for discriminating between GBM and brain
metastases [75]. Computational-aided quantitative analysis of MRI
images may improve the accuracy in differentiating GBM from metastases,
and texture features are more significant than fractal-based features
for that purpose [76]. Increasingly, machine learning algorithms
have been applied to imaging data to improve the differentiation between
GBM and brain metastases [77-81]. Novel diagnostic support systems
based on radiomic features extracted from post-contrast 3DT1 MR images
may help improving the distinction between solitary brain metastases and
GBM with high diagnosis performance and generalizability [77].
Machine learning and deep learning-based models applied to conventional
MR images may support preoperative discrimination between GBM and
solitary brain metastasis conventional MR images [78-80], and deep
learning network models that allow automated, on-site analysis of
resected tumor specimens based on confocal laser endoscopic techniques
image datasets have been developed [81]. Other parameters such as
the cerebral blood volume gradient in the peritumoral brain zone may
enable the differentiation of GBMs from metastases [82]. Another
approach is to evaluate peritumoral areas with color map of phase
difference enhanced imaging (Color PADRE) [83].
As with tumors originating in the brain, metastases are treated with
radiotherapy and stereotactic radiosurgery, making incidence of
radiation necrosis an important issue, and the distinction between
metastasis and inflamed and necrotic tissue by MRI can be challenging.
One study examined the hypothesis that methionine levels could be
increased in metastatic tissue, whereas the inflammation marker
translocator protein (PBR-TSPO), which can be quantified with specific
PET tracers, would be increased in necrosis. Thus, the use of the
[11C]methionine and
[11C]PBR28 tracers in PET was evaluated in 5
patients previously treated brain metastases showing regrowth. The use
of [11C]methionine could accurately identify
pathologically confirmed tumor regrowth in all 7 lesions examined,
whereas [11C]PBR28 could only identify 3 of 7
lesions, indicating that the former, but not the later tracer can be
used as a reliable marker [84].