[ARTICLE] Imaging in neuro-oncology – Full Text

Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.

The roles of imaging in neuro-oncology primarily consist of diagnosis, prognosis, and treatment response assessment of central nervous system (CNS) tumors. Imaging assessment is currently an important surrogate endpoint for clinical trials. With ongoing evaluation and discovery of novel treatment agents, including immunotherapy agents, the ability to accurately assess progression and discern treatment-related changes is a central goal of neuro-oncologic imaging. In this review, we will summarize several clinically available imaging techniques as well as some novel methods under development, and provide an up-to-date review of some clinical challenges in treatment of glioblastomas where imaging can have important roles.

Diffusion-weighted magnetic resonance imaging (DW-MRI) can characterize tissues based on the differences in the degree of free movement of protons. It has been shown that the cellularity or cell density of tumor is associated with apparent diffusion coefficient (ADC), a calculated metric from DW-MRI.1 This property allows one to distinguish between both tumor subtypes and tumor grades (low versus high). More recently, high b-value DW-MRI, using a b-value >3000 s/mm2, has been demonstrated to be superior to standard DW-MRI in distinguishing tumor tissue from normal brain parenchyma.2 DW-MRI data can also be further quantified to generate imaging markers using techniques such as diffusion kurtosis imaging (DKI),3 histogram curve-fitting,4 and functional diffusion map (fDM).5 Restriction spectrum imaging (RSI) is an DW-MRI technique that can isolate the diffusion properties of tumor cells from extracellular process such as edema, potentially improving specificity of tumor detection and characterization.6 Diffusion tensor imaging (DTI) measures the directionality of proton motion as fractional anisotropy (FA), which is often altered in the presence of brain tumors.7 Applications of these methods will be reviewed in the following sections.

Perfusion-weighted magnetic resonance imaging (PW-MRI) techniques assess blood flow to tissue by calculating parameters derived from the time–intensity curve. Using the normal brain as reference, these techniques can detect pathological alterations of tissue vascularity that commonly occur among brain tumors due to increased vascular permeability as well as intravascular blood volume because of tumor-induced angiogenesis. Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) quantifies first-pass bolus of paramagnetic contrast agent,8,9 and is currently the most common perfusion-weighted imaging method in clinical use. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can characterize vascular permeability within or surrounding tumors by using pharmacokinetic models to quantify the movement of contrast agents crossing the blood–brain barrier.1012 DCE-MRI has an advantage over DSC-MRI due to its greater signal-to-noise ratio and spatial resolution, although imaging acquisition time is also longer. Perfusion imaging measurements are highly dependent on imaging acquisition parameters and postprocessing techniques, including variations in postprocessing software tools.13 Clinical application of this technique therefore requires efforts in standardization, particularly in multicenter settings.

Magnetic resonance spectroscopy (MRS) measures concentrations of metabolites within tissues noninvasively.14 The single-voxel spectroscopy (SVS) method collects average MRS data within a target region of interest selected on standard MRI images. The multivoxel spectroscopy (MVS) method can obtain two- or three-dimensional maps of the region of interest to detect voxel-wise spatial changes of specific metabolites. Both SVS and MVS approaches have been evaluated in tumor diagnosis, grading, pre-therapy planning and post-therapy assessment. One major limitation of the technique is its operator dependency, requiring experienced staff to manually select regions of interest during acquisition. It is also less sensitive to lesions with volume <1.5 cm3.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) is an important imaging tool in oncology.15 Similar to systemic cancers, brain tumors often exhibit increased metabolic activity resulting in elevated 18F-FDG uptake that can be detected by PET.16 The role of FDG-PET in brain tumor imaging, however, has been quite limited due to its relative lack of specificity and high background uptake by the normal brain. This limitation is particularly important for small lesions, as currently the resolution of PET imaging is limited to 5 mm. More recently, amino acid PET tracers including 11C-methionine, 18F-fluorothymidine (FLT), 18F-fluoro-ethyl-tyrosine (FET), and 18F-dihydroxyphenylalanine (DOPA) have been developed and evaluated for brain tumor imaging. This class of radiotracers is avidly taken up by malignant brain tumors that have higher cellular proliferation compared to the normal brain.1720 The advantage of high lesion-to-background uptake ratio makes amino acid PET suitable for imaging of brain tumors, including applications such as predicting tumor grade, detecting recurrent tumor, and assessing treatment response. Novel PET radiotracer (18)F-fluoromisonidazole (18F-FMISO) has been evaluated as a marker of tissue hypoxia before and after treatment.21,22

With increasing computing speed and availability of pre-engineered algorithms, imaging data can be analyzed for voxel-level intensity variations to generate texture-type features that can be correlated with tumor biology or treatment response. This approach can be applied to any imaging modality individually or simultaneously through spatial co-registration. As a result, imaging features can be regarded as tumor phenotypes and this type of biomarker can be summarized by the term ‘radiomics’.23 Screening or combining a large number of radiomic features allows generation of models that can aid oncologic diagnosis, prognostication, and treatment response prediction. This approach has been successful in a number of systemic cancers.2428 The radiomic approach is particularly suitable for evaluating high-grade gliomas, a tumor type that is well known for its genetic heterogeneity and highly complex imaging phenotypes.

Imaging plays a key role in the diagnosis of brain tumors and has become one routine management step during preoperative evaluation to aid determination of tumor grade and prognosis. It can also provide important spatial information on tumor tissue characteristics for some tumor subtypes that can influence surgical and radiation treatment planning. In addition, imaging has shown increasing ability to detect tumor genetic profile that can further provide valuable prognostic and predictive information for optimal treatment planning. Finally, imaging findings are often combined with clinical data such as age, gender, and presenting symptoms and signs to increase the accuracy of diagnosis for various tumor types, as well as identifying non-tumor mimics.

One common clinical dilemma during preoperative diagnosis of brain tumors is to distinguish between high-grade glioma and lymphoma. Standard management of CNS lymphoma is nonsurgical and biopsy is the preferred approach if lymphoma is suspected preoperatively, whereas maximal surgical resection provides the best prognosis for high-grade glioma. On conventional imaging sequences, these tumor types commonly exhibit contrast enhancement and peritumoral edema, which make it challenging to differentiate. Lymphomas typically exhibit low ADC values due to high cellularity.29,30 However, this histological feature can be seen in high-grade gliomas and metastases.

Quantitatively, the FA and ADC values of primary cerebral lymphoma are significantly lower than those of glioblastoma.31,32 There is also evidence that DSC-MRI and DCE-MRI parameters of the enhancing regions of the tumor can discriminate between lymphomas and glioblastomas as well as between lymphomas and metastasis,32,33 although a direct comparison of DCE-MRI and DW-MRI shows that ADC measurement is superior to DCE-MRI in differentiating the two tumor types.34 Detection of intratumoral microhemorrhage using the susceptibility-sensitive MRI technique also allows differentiation of glioblastoma and primary CNS lymphomas.35 Texture features generated from post-contrast images of lymphoma and glioblastoma also allow diagnostic differentiation.36

Analysis of nonenhancing signal abnormalities surrounding brain lesions can provide independent diagnostic information. ADC values measured within fluid-attenuated inversion recovery (FLAIR) abnormalities surrounding the enhancing regions can differentiate high-grade gliomas from solitary metastases.37,38 The difference could be due to the presence of tumor infiltration by glioma, resulting in higher cellularity than tumor-induced edema.39 This is also supported by MRS and DSC-MRI measurements of the peritumoral region showing higher choline to N-acetylaspartic acid (NAA) ratio and greater vascularity among high-grade gliomas compared to brain metastases.32,40,41 Combined evaluation of both the enhancing and nonenhancing regions can potentially enhance diagnostic accuracy.32,42 Beyond the margins of signal abnormalities outlined by conventional MRI, including T1- and T2-weighted imaging, MRS can identify regions of brain containing tumor and improve surgical resection and patient outcome.43,44

Molecular data of gliomas have demonstrated prognostic significance and have been incorporated into the 2016 World Health Organization (WHO) criteria.45 The imaging characteristics of brain tumors can be directly related to a specific set of tumor genomics, providing opportunities to noninvasively predict tumor genotype preoperatively. Radiomic models have been developed based on conventional MRI, DTI, and DSC-MRI for predicting gene expression profiles of newly diagnosed glioblastomas.46 Specific genetic alterations of tumors can also be predicted by analysis of MRI data and predictive models have been generated for O6-methylguanine-DNA methyltransferase (MGMT) methylation status,47,48 epidermal growth factor (EGFR) amplification status,25,49 and EGFR receptor variant III status.50 Isocitrate dehydrogenase 1/2 (IDH) mutations are commonly present in low-grade gliomas as well as secondary glioblastomas. These mutant tumors accumulate 2-hydroxyglutarate (2HG), an onco-metabolite that can be detected by MRS (Figure 1).51 Measurement of 2HG concentration allows diagnosis of IDH mutant tumor preoperatively and also opportunities to monitor tumor activity during treatment.52,53 Static and dynamic FET-PET measurements have also been correlated with IDH and 1p/19q status.54 More recently, multimodal MRI imaging can be evaluated by machine learning algorithms to generate predictive models for IDH status in gliomas.5557

Continue —-> Imaging in neuro-oncology – Hari Nandu, Patrick Y. Wen, Raymond Y. Huang, 2018

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