A systematic review and meta-analysis on the differentiation of glioma grade and mutational status by use of perfusion-based magnetic resonance imaging

Following the 2016 World Health Organization (WHO) classification system of tumors of the central nervous system, the high-grade glioma group can be divided in two subgroups. One subgroup comprises the anaplastic oligodendroglioma IDH mutant and 1p/19q codeleted, and the anaplastic oligodendroglioma not otherwise specified. The second subgroup comprises the IDH mutant glioblastoma, the IDH wildtype glioblastoma, and the glioblastoma not otherwise specified [1]. Knowledge on the exact mutational status of glioma is not only important for classification, it also has significant impact on prognosis [2] and treatment strategy [3‐5]. With regard to low grade gliomas, two groups of gliomas can be distinguished. The first groups consist of oligodendroglial tumors which are isocitrate dehydrogenase (IDH) mutant and 1p/19q codeleted The second groups consist of astrocytic tumors. It is comprised of (1) IDH mutated, 1p/19q non-codeleted diffuse astrocytoma, (2) the IDH wildtype astrocytoma, and (3) the diffuse astrocytoma not otherwise specified [1].

However, the recently published WHO 2021 classification system has placed even more emphasis on the molecular characteristics of glioma subtypes. The group of diffuse astrocytic and oligodendroglial gliomas can be subdivided based on the IDH mutations. IDH wildtype tumors are classified as high-grade gliomas, without exception. In order to be classified as glioblastoma (IDH wildtype; grade 4), nuclear ATRX loss has to be present. Additionally, IDH wild-type diffuse astrocytic tumors in adults without the histological features of glioblastoma, but with one or more of three genetic parameters (telomerase reverse transcriptase gene [TERT] promoter mutation, epidermal growth factor receptor [EGFR] gene amplification, or combined gain of entire chromosome 7 and loss of entire chromosome 10 [+ 7/ − 10]) are now also classified as glioblastoma. In the 2021 classification, all IDH-mutant diffuse astrocytic tumors with intact 1p/19q chromosomes are considered a single type called astrocytoma, IDH-mutant with WHO grades ranging from 2 to 4. Grading of these tumors takes into account molecular findings such as the homozygous deletion of CDKN2A/B, which is associated with a worse prognosis. IDH-mutant astrocytomas with these molecular alterations will be classified as WHO grade of 4, even if microvascular proliferation or necrosis is absent [6]. Additionally, IDH mutant oligodendroglial gliomas with codeleted 1p/19q chromosomes are considered oligodendrogliomas. While the establishment of the sophisticated molecular markers to classify gliomas is an important advance in glioma diagnosis, all of the literature which is covered within this review is based on the 2016 WHO classification of central nervous system tumors [6, 7].

To this end, artificial intelligence applied to conventional MRI sequences (i.e., pre- and post-contrast T1-weighted, T2-weighted and T2-weighted FLAIR images) to predict mutational status has provided promising results in recent years (for a review, see [8]). In addition, various signs have been identified which can help the radiologist to predict the molecular status of glioma in the daily clinical setting. For example, the T2-FLAIR mismatch sign has been found to be a reliable non-invasive marker for identification of IDH mutant astrocytomas [9].

Bearing in mind the pathophysiological differences between various glioma subtypes and the related changes in the gliomas vasculature, perfusion-based imaging could increase the diagnostic accuracy of non-invasive characterization of glioma subtypes. For example, oligodendroglial tumors are characterized by a branching pattern of vascularization, whereas astrocytic glioma shows a distinctively different vascularization [10]. Therefore, perfusion based MR imaging (either dynamic susceptibility contrast (DSC) or dynamic contrast enhancement (DCE) perfusion MR imaging) has been the subject of research to non-invasively identify molecular characteristics [11, 12].

DSC-perfusion MR imaging relies on the susceptibility induced signal loss on T2*-weighted sequences, resulting from a bolus of gadolinium-based contrast agent passing through the capillaries. The most commonly used DSC perfusion parameter is Cerebral Blood Volume (CBV). CBV can be estimated by use of the area under the curve (AUC) of the signal intensity-time curve [13, 14]. However, more recent studies compute CBV maps by integrating the transverse relaxivity changes which occur dynamically over a first-pass injection followed by leakage correction due to the leaky blood–brain barrier in most tumors (for a recent overview and recommendations, see [15]). DCE-perfusion MR imaging relies on the evaluation of T1 shortening induced by a gadolinium-based contrast agent bolus leaking from the blood vessels into the tissue. Pharmacokinetic modeling can be used to derive various values including, Ve and Vp. ktrans represents the capillary permeability; Ve represents the fractional volume of the gadolinium-based contrast agent in the extravascular-extracellular space; Vp represents the fractional volume of the of the gadolinium-based contrast agent in the plasma space [13].

Although various studies with different methodologies and outcomes have been published since the release of the WHO 2016 classification system of glioma, a comprehensive overview of the accuracy of perfusion based MR imaging to predict the molecular characteristics of glioma is still lacking. In addition, a systematic overview of the literature on this topic could help to shape future research and daily clinical practice to focus on the most promising technique (either DSC- or DCE-perfusion MRI). The aim of the current paper was therefore to provide an overview of the relevant literature with regard to the use of DSC and DCE perfusion imaging used to differentiate glioma grade and mutational status.

A total of 552 studies were identified after systematic searching. Duplicates were removed and 379 papers were systematically screened on title and abstract resulting in the inclusion of 34 papers for full-text analysis. Reasons for exclusion of the 345 papers are provided in Fig. 1. After full-text analysis, the investigators met to discuss the identified non-consensus papers to resolve disagreements and to reach consensus. Of the 34 papers, 12 could be included in the qualitative meta-synthesis. Twenty-two papers were therefore excluded (details provided in Fig. 1). No discrepancies between the judgement of the two researchers remained after discussion, resulting in the final inclusion of 12 papers for the qualitative meta-synthesis [20‐31] (Fig. 1). Five papers provided sufficient data to be included in the quantitative meta-analysis [22, 24, 26, 27, 29] (Table 1).

Using the QUADAS2 (QUality Assessment tool for Diagnostic Accuracy Studies), the most current version of the QUADAS tool of the QUADAS task force, the risk of bias was considered low in all included studies (Table 2).

This systematic review and meta-analysis shows that perfusion MRI can be used to effectively predict IDH genotype non-invasively following the WHO 2016/2021 glioma classification. Different DSC or DCE perfusion parameters were found to have an equal performance regarding the non-invasive prediction of IDH genotype. Prediction of the 1p/19q-codeletion status could not be meta-analyzed using the acquired data.

The role of perfusion MRI in non-invasive glioma classification can be significant and can be explained by the different glioma vasculature fingerprints which provide a specialized microenvironment for glioma cells [32]. Within HGG, blood vessels are abnormal and display a distinct gene expression signature which differs from the genotype of blood vessels in normal brain tissue [33‐35]. These genotypic differences result in high expression of certain angiogenic factors, including vascular endothelial growth factor, transforming growth factor β2, and pleiotrophin [35‐38]. In LGG, on the other hand, potential molecular alterations regarding angiogenesis have been investigated less extensively. In 2018, Zhang et al. demonstrated that WHO grade II glioma expressed an intermediate stage of vascular abnormality, less severe than that of glioblastoma vessels but distinct from normal vessels. Enhanced expression of laminin subunit alpha 4 and angiopoietin 2 in WHO grade II glioma was confirmed by staining of human tumor tissue microarrays. More specifically, IDHwt gliomas displayed a specific angiogenic gene expression signature (i.e., upregulation of Angiopoietin 2 and serpin family H) which resulted in enhanced endothelial cell migration and matrix remodeling. In the same study, transcription factor analysis indicated increased transforming growth factor beta and hypoxia signaling in IDHwt gliomas [39]. Based on these studies, we can conclude that gliomas with different IDH genotype have distinct molecular vascularization. In addition, the blood vessels in LGG displayed alterations in gene expression which partially overlapped with changes previously identified in HGG vessels [39]. As IDHwt glioma vessels have a distinct vascular gene expression pattern associated with vascular remodeling, these microstructural changes can be used to explain why IDHwt glioma show significantly higher perfusion metrics compared to IDHmut glioma. These insights in genotype and phenotype justify the use of perfusion MRI to predict IDH genotype. The role of 1p/19q codeletion status on angiogenesis and vascular growth, however, remains partially elusive. Previous research demonstrated that 1p/19q codeletion was associated with higher CBV values compared with glioma with intact alleles [40]. Another paper reported specific genotypic differences in oligodendroglioma by use of DSC perfusion MRI with significantly higher rCBVmax values in LGG with 1p/19q codeletion [41]. It is believed that 1p/19q codeleted LGG show an increased metabolism and angiogenesis and have an extensive internal vascular network. This is supported by the study of Kapoor et al. in which a significantly higher rCBVmax was observed in 1p/19q codeleted LGG (WHO 2021 Oligodendroglioma WHO grade 2). Additionally, an increased vascular endothelial growth factor expression, CD31, and CD105, was observed as compared with glioma with intact alleles [42].

The clinical usability of MRI perfusion imaging to predict IDH genotype remains partially elusive as the differences were based on aggregated results. Although several papers provided specific threshold values [27, 28], no clinically useful threshold values are available. The current review did not include studies arterial spin labeling (ASL) as a perfusion MR imaging method as only sparse literature with regard to ASL was found in exploratory literature searches. A recent paper by Wang et al. (2019) reported that only a mild correlation was found between the IDH1 genotypes and ASL derived glioma perfusion parameters. There was no significant association between 1p/19q codeletion and perfusion in grade II and III gliomas [43].

This review and meta-analysis showed that accuracy of DSC parameters was not different from the accuracy of DCE parameters to non-invasive predict the IDH genotype in glioma patients. The use of perfusion MRI with regard to predicting 1p/19q codeletion status could not be determined using these data.