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

Abstract

Background

Molecular characterization plays a crucial role in glioma classification which impacts treatment strategy and patient outcome. Dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) perfusion imaging have been suggested as methods to help characterize glioma in a non-invasive fashion. This study set out to review and meta-analyze the evidence on the accuracy of DSC and/or DCE perfusion MRI in predicting IDH genotype and 1p/19q integrity status.

Methods

After systematic literature search on Medline, EMBASE, Web of Science and the Cochrane Library, a qualitative meta-synthesis and quantitative meta-analysis were conducted. Meta-analysis was carried out on aggregated AUC data for different perfusion metrics.

Results

Of 680 papers, twelve were included for the qualitative meta-synthesis, totaling 1384 patients. It was observed that CBV, ktrans, Ve and Vp values were, in general, significantly higher in IDH wildtype compared to IDH mutated glioma. Meta-analysis comprising of five papers (totaling 316 patients) showed that the AUC of CBV, ktrans, Ve and Vp were 0.85 (95%-CI 0.75–0.93), 0.81 (95%-CI 0.74–0.89), 0.84 (95%-CI 0.71–0.97) and 0.76 (95%-CI 0.61–0.90), respectively. No conclusive data on the prediction of 1p/19q integrity was available from these studies.

Conclusions

Future research should aim to predict 1p/19q integrity based on perfusion MRI data. Additionally, correlations with other clinically relevant outcomes should be further investigated, including patient stratification for treatment and overall survival.

Key points

  • Perfusion MR imaging shows a promising method to characterize glioma non-invasively.

  • Significant higher perfusion metrics are observed in IDH-wildtype glioma.

  • The effects of 1p/19q mutations on perfusion metrics are understudied and remain unelucidated.

Introduction

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 [3,4,5]. 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.

Materials and methods

Search strategy and inclusion/exclusion methodology

This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [16]. Databases searched for literature were: Medline (accessed through PubMed), EMBASE, Web of Science, and the Cochrane Library. The full search strategies for each database are made available in the Additional file 1. Cross-referencing was used to add relevant literature to the database. Searches were conducted between May 1, 2020 and January 1, 2021. Inclusion criteria were: (1) the use of either DSC or DCE perfusion MRI; (2) the inclusion of patients suffering from glioma; (3) glioma grading and classification by use of the WHO 2016 classification system [1]; and (4) the aim of the study needed to comprise the non-invasive classification of histopathological features and/or molecular characteristics (WHO grade, IDH genotype and/or 1p/19q codeletion status). Besides, papers needed to report results as quantitative measures (e.g., sensitivity, specificity, mean accuracy and/or mean area under the receiver operator curve (AUC)). Papers were excluded if they were based on animals or non-human samples or a pediatric population. Letters, preprints, case reports, congress proceedings, and narrative reviews were excluded as well.

All papers were independently assessed by two researchers in three steps. First, screening on title and abstract was carried out. Second, full-text analysis was employed to assess whether the papers met the inclusion- and/or exclusion criteria. Finally, information was extracted from the included papers. Researchers met periodically to discuss their findings and resolve discrepancies. Standardized tables were used to acquire the information of interest from the included articles by two researchers (LvS and DH) independently. Data extracted from each study were (a) first author and year of publication, (b) number of patients included, (c) mean age of the included participants, (d) gender of the included participants, (e) use of DSC and/or DCE, (f) which histopathological/molecular outcome was assessed, (g) perfusion based MR imaging metrics and (h) accompanying statistics (e.g., AUC value, standard deviation, 95% confidence interval (CI) and/or standard error). Performance was expressed in accuracy, AUC and/or sensitivity and specificity for each outcome. Extracted data were cross-checked afterward, and discrepancies were resolved in consensus.

Qualitative meta-synthesis and quantitative meta-analysis

Eligible literature was synthesized qualitatively following the PICO-strategy as proposed by Eriksen et al. [17]. Also, quality of primary diagnostic accuracy studies was assessed using the QUADAS-2. Meta-analysis was conducted on the AUC and the 95% Confidence Interval (95%-CI) using a random effects model. From the included studies, perfusion metrics and the aforementioned statistics were extracted. If one of these variables was missing, the researchers aimed to re-calculate the value when possible [18]. In addition, corresponding authors were contacted to provide missing details, with up to two reminders send by e-mail. When not all necessary data could be acquired, studies could not be meta-analyzed. Meta-analyses were conducted on different subgroups of target conditions. Meta-analysis was performed with the use of OpenMetaAnalyst (MetaAnalyst, Tufts Medical Center) [19] and/or SPSS (version 25; IBM Corp., Armonk, NY) and results were displayed in forest plots. The Higgins test was used to test for heterogeneity between included studies. Low heterogeneity between groups is marked with an I2 < 40%, whereas considerable heterogeneity is indicated by I2 > 75% [18].

Results

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 [20,21,22,23,24,25,26,27,28,29,30,31] (Fig. 1). Five papers provided sufficient data to be included in the quantitative meta-analysis [22, 24, 26, 27, 29] (Table 1).

Fig. 1
figure 1

PRISMA flow diagram

Table 1 Overview of the included studies

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).

Table 2 Combined effect size for the different DCE/DSC parameters

Qualitative meta-synthesis

The twelve included studies [20,21,22,23,24,25,26,27,28,29,30,31] totaled 1384 patients (792 males; 592 females) suffering from glioma. Gliomas could be subdivided into WHO grade II (n = 326); WHO grade III (n = 410) and WHO grade IV (n = 599). Regarding the IDH genotype, 701 gliomas were IDH-mutated and 603 tumors expressed an IDH wildtype genotype. 1p/19q codeletion (WHO 2021 Oligodendroglioma WHO grade 2 or 3) was observed in 132 tumors; non-codeletion of 1p/19q chromosome arms was observed in 429 tumors. All included papers used histopathological/molecular assessment by a trained neuropathologist who adhered to the WHO 2016 glioma classification as the gold standard.

Eight papers used DSC perfusion MRI [20, 21, 24,25,26,27,28, 30]; three papers used both DCE and DSC perfusion MRI [22, 23, 29]; one paper used DCE perfusion MRI only [26]. Two papers used artificial intelligence methods to assess different perfusion metrics between various subtypes of gliomas [20, 25], whereas the other publications used more traditional statistics.

As assessed by DSC perfusion MRI, IDHmut glioma displayed significantly lower rCBV values as compared to IDHwt glioma [21, 26,27,28, 30]. When using a retrospectively determined rCBVmax threshold value of < 2.35, the authors described a sensitivity/specificity of 100%/61% and AUC of 0.82 (95%-CI: 0.66–0.93) when differentiating IDHmut (either WHO 2021 Astroctytoma grade 2, 3 or 4 or WHO 2021 Oligodendroglioma grade 2 or 3) and IDHwt gliomas [27]. By use of the skewness of normalized CBV (nCBV) values (normalized by use of the CBV value of the normal-appearing contralateral centrum semiovale), IDHmut, 1p/19q non-codeleted glioma (WHO 2021 Astrocytoma grade 2, 3 or 4) could be distinguished from IDHwt glioma and IDHmut, 1p/19q codeleted glioma (WHO 2021 Oligodendroglioma) with a sensitivity/specificity of 84%/59% (AUC-value of 0.690 and 95%-CI 0.573–0.807). IDHmut, 1p/19q non-codeleted gliomas (WHO 2021 Astrocytoma grade 2, 3 or 4) showed significant lower nCBV values compared to the IDHmut, 1p/19q codeleted gliomas (WHO 2021 Oligodendroglioma grade 2 or 3) and the IDH wildtype gliomas [24].

When using DCE perfusion imaging, IDHmut HGG (either WHO 2021 Astroctytoma grade 3 or 4 or WHO 2021 Oligodendroglioma grade 3) showed significantly lower ktrans values as compared to IDHwt HGG (WHO 2021 Astrocytoma grade 4) [22]. In oligodendroglial tumors (WHO 2021 Oligodendroglioma, IDHmut, 1p/19q-codeleted; Grade 2 or 3), however, Lee et al. found that ktrans, Kep and Ve showed tendencies toward higher values as compared to astrocytic tumors [23]. Ve and Vp values were found to be significantly lower in IDHmut glioma (WHO 2021 Astrocytoma and WHO 2021 Oligodendroglioma) as compared to IDHwt glioma, regardless of WHO II-IV grading [22, 29]. Based on Ve and Kep values, a sensitivity/specificity of 84%/79% and 76%/78% was observed with regard to differentiate IDH mutation status [29]. The study of Hilario et al. also suggested that ktrans, Vp and Ve could be used to differentiate between LGG and HGG non-invasively [22].

Studies using artificial intelligence showed promising results with regard to prediction of IDH mutation status. Choi et al. showed that a convolutional long short-term memory model with an attention mechanism had an accuracy, sensitivity, and specificity of 92.8%, 92.6%, and 93.1%, respectively, in the validation set (AUC: 0.98; 95%-CI 0.969–0.991) with regard to IDH genotype prediction by use of DSC perfusion MRI. In the test set, an accuracy, sensitivity, and specificity of 91.7%, 92.1%, and 91.5% were observed, respectively. The AUC value of the IDH genotype prediction demonstrated to be 0.95 with a 95% CI ranging between 0.898 and 0.982. Subsequent analysis of the signal intensity curves of DSC imaging elucidated high attention on the combination of the end of the pre-contrast baseline, the up/downslopes of signal drops, and/or post-bolus plateaus for the curves used to predict IDH genotype [20]. Another study showed that when using a random forest algorithm, shape, distribution and rCBV-extracted features elucidated significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features, while texture and intensity feature were more relevant for the distinguishing of III and IV. Based on this random forest algorithm, gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases [25].

Meta-analysis

Meta-analysis of the data (n = 237 patients) showed that CBV values have an accuracy of correctly predicting IDH genotype with an AUC of 0.832 and a standard error of 0.03 (95%-CI 0.75–0.91). When using DCE parameters (n = 122), an AUC of 0.81, 0.84 and 0.78 is observed for ktrans, Ve and Vp, respectively. Standard errors (and 95%-CI) for ktrans, Ve and Vp were found to be 0.02 (95%-CI 0.73–0.90), 0.03 (95%-CI 0.77–0.92) and 0.03 (95%-CI 0.68–0.87), respectively (Table 2). The corresponding Forest-Plots of the different perfusion metrics are provided in Fig. 2. I2 analysis showed that included DCE-MRI studies were homogeneous (I2 < 1%). In the individual analyses of ktrans, Ve and Vp, studies were found to be non-significantly heterogeneous (p = 0.834; p = 0.548; p = 0.519, respectively). The meta-analysis of DSC-MRI studies showed to have moderate heterogeneity (I2 = 35%; p = 0.215). The role of perfusion MRI metrics in predicting the 1p/19q-codeletion status could not be meta-analyzed using the acquired data.

Fig. 2
figure 2

Forest-plot of the area under the curve (AUC) of the receiver operator curve (ROC) of the different perfusion metrics in predicting IDH mutation status. IDH, isocitrate dehydrogenase, ktrans, volume transfer coefficient; rCBV, relative cerebral blood volume; Ve, fractional volume of the extravascular extracellular space; Vp, fractional blood plasma volume; 95%-CI, 95%-confidence interval

Discussion

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 [33,34,35]. These genotypic differences result in high expression of certain angiogenic factors, including vascular endothelial growth factor, transforming growth factor β2, and pleiotrophin [35,36,37,38]. 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].

Strength and limitations

By adhering to the 2016 WHO glioma classification to be included, some valuable papers needed to be excluded, though also resulted in rather homogeneous dataset to be meta-analyzed. One of the strengths of this reviews concerns the relative homogeneous imaging protocols which were meta-analyzed. For example, all DSC-imaging protocols were imaged after administering a pre-bolus injection of a gadolinium-based contrast agent. Also, for the included studies which investigated the diagnostic accuracy of DCE-imaging, mean perfusion values (i.e., Ve, Vp and ktrans). However, different studies used different values of perfusion parameters (mean rCBV vs. mean rCBV max values), which partially limits the generalizability of results [15]. The homogeneity of the meta-analyzed patients and histopathological outcomes (i.e., IDH genotype and 1p/19q codeletion status) strengthen the here described findings. Another limitation of the here applied methodology concerns the fact that this systematic review was executed without registration in an international database.

Conclusion

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.

Availability of data and materials

Data analyzed in this study will be made available upon reasonable request by contacting the corresponding author.

Abbreviations

95%-CI:

95%-Confidence interval

AUC:

Area under the curve

DCE MRI:

Dynamic contrast enhancement magnetic resonance perfusion imaging

DSC MRI:

Dynamic susceptibility contrast magnetic resonance perfusion imaging

HGG:

High-grade glioma

IDH:

Isocitrate dehydrogenase

IDHmut:

Mutation of the isocitrate dehydrogenase gene(s)

IDHwt:

Wild-type isocitrate dehydrogenase gene(s)

Kep:

Rate constant of gadolinium efflux between the extravascular extracellular space and blood plasma

ktrans:

Volume transfer constant

LGG:

Low grade glioma

MRI:

Magnetic resonance imaging

nCBV:

Normalized cerebral blood volume

rCBV:

Relative cerebral blood volume

rCBVmax-p:

Maximum relative cerebral blood volume in the peri-enhancing region

rCBVmax-t:

Maximum relative cerebral blood volume in the tumor-enhancing region

Ve:

Fractional volume of the extravascular extracellular space

Vp:

Fractional blood plasma volume

WHO:

World Health Organization

References

  1. Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820

    Article  PubMed  Google Scholar 

  2. Lapointe S, Perry A, Butowski NA (2018) Primary brain tumours in adults. Lancet 392(10145):432–446

    Article  PubMed  Google Scholar 

  3. Narang J, Jain R, Scarpace L et al (2011) Tumor vascular leakiness and blood volume estimates in oligodendrogliomas using perfusion CT: an analysis of perfusion parameters helping further characterize genetic subtypes as well as differentiate from astroglial tumors. J Neurooncol 102(2):287–293

    Article  PubMed  Google Scholar 

  4. Saito T, Yamasaki F, Kajiwara Y et al (2012) Role of perfusion-weighted imaging at 3 T in the histopathological differentiation between astrocytic and oligodendroglial tumors. Eur J Radiol 81(8):1863–1869

    Article  PubMed  Google Scholar 

  5. Lecavalier-Barsoum M, Quon H, Abdulkarim (2014) Adjuvant treatment of anaplastic oligodendrogliomas and oligoastrocytomas. Cochrane Database Syst Rev (5)

  6. Louis DN, Perry A, Wesseling P et al (2021) The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol 23(8):1231–1251

    Article  CAS  PubMed  Google Scholar 

  7. Weller M, van den Bent M, Preusser M et al (2021) EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol 18(3):170–186

    Article  PubMed  Google Scholar 

  8. van Kempen E, Post M, Mannil M et al (2021) Accuracy of machine learning algorithms for the classification of molecular features of gliomas on MRI: a systematic literature review and meta-analysis. Cancers 13:2606

    Article  PubMed  PubMed Central  Google Scholar 

  9. Jain R, Johnson DR, Patel SH et al (2020) “Real world” use of a highly reliable imaging sign: “T2-FLAIR mismatch” for identification of IDH mutant astrocytomas. Neuro Oncol 22(7):936–943

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhou J, Li N, Yang G, Zhu Y (2011) Vascular patterns of brain tumors. Int J Surg Pathol 19(6):709–717

    Article  PubMed  Google Scholar 

  11. Brendle C, Hempel J-M, Schittenhelm J et al (2018) Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol 28(3):421–428

    Article  PubMed  Google Scholar 

  12. Ma H, Wang Z, Xu K et al (2017) Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery. Exp Ther Med 13(6):2691–2698

    Article  PubMed  PubMed Central  Google Scholar 

  13. Essig M, Shiroishi MS, Nguyen TB et al (2013) Perfusion MRI: the five most frequently asked technical questions. AJR Am J Roentgenol 200(1):24–34

    Article  PubMed  PubMed Central  Google Scholar 

  14. Petrella JR, Provenzale JM (2000) MR perfusion imaging of the brain: techniques and applications. AJR Am J Roentgenol 175(1):207–219

    Article  CAS  PubMed  Google Scholar 

  15. Boxerman JL, Quarles CC, Hu LS et al (2020) Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol 22(9):1262–1275

    Article  PubMed  PubMed Central  Google Scholar 

  16. Moher D, Liberati A, Tetzlaff J, Altman DG (2010) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 8(5):336–341

    Article  PubMed  Google Scholar 

  17. Eriksen MB, Frandsen TF (2018) The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. J Med Libr Assoc 106(4):420–431

    Article  PubMed  PubMed Central  Google Scholar 

  18. Higgins JPT, G.S.e (2011) Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011]. The Cochrane Collaboration

  19. Wallace BC, Lajeunesse MJ, Dietz G et al (2017) OpenMEE: intuitive, open-source software for meta-analysis in ecology and evolutionary biology. Methods Ecol Evol 8(8):941–947

    Article  Google Scholar 

  20. Choi KS, Choi SH, Jeong B (2019) Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network. Neuro Oncol 21(9):1197–1209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hempel J-M, Schittenhelm J, Klose U et al (2019) In vivo molecular profiling of human glioma cross-sectional observational study using dynamic susceptibility contrast magnetic resonance perfusion imaging. Clin Neuroradiol 29(3):479–491

    Article  PubMed  Google Scholar 

  22. Hilario A, Hernandez-Lain A, Sepulveda JM et al (2019) Perfusion MRI grading diffuse gliomas: impact of permeability parameters on molecular biomarkers and survival. Neurocirugia 30(1):11–18

    Article  Google Scholar 

  23. Lee JY, Ahn KJ, Lee YS et al (2018) Differentiation of grade II and III oligodendrogliomas from grade II and III astrocytomas: a histogram analysis of perfusion parameters derived from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI. Acta Radiol 59(6):723–731

    Article  PubMed  Google Scholar 

  24. Lee MK, Park JE, Jo Y et al (2020) Advanced imaging parameters improve the prediction of diffuse lower-grade gliomas subtype, IDH mutant with no 1p19q codeletion: added value to the T2/FLAIR mismatch sign. Eur Radiol 30(2):844–854

    Article  CAS  PubMed  Google Scholar 

  25. Sudre CH, Panovska-Griffiths J, Sanverdi E et al (2020) Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status. BMC Med Inform Decis Mak 20(1):1–14

    Article  Google Scholar 

  26. Wu H, Tong H, Du X et al (2020) Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas. Eur Radiol 30(6):3254–3265

    Article  CAS  PubMed  Google Scholar 

  27. Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D (2017) Noninvasive assessment of IDH mutational status in World Health Organization grade II and III astrocytomas using DWI and DSC-PWI combined with conventional MR imaging. AJNR Am J Neuroradiol 38(6):1138–1144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Xing Z, Zhang H, She D et al (2019) IDH genotypes differentiation in glioblastomas using DWI and DSC-PWI in the enhancing and peri-enhancing region. Acta Radiol 60(12):1663–1672

    Article  PubMed  Google Scholar 

  29. Zhang H, Lyu G, He W et al (2020) DSC and DCE histogram analyses of glioma biomarkers, including IDH, MGMT, and TERT, on differentiation and survival. Acad Radiol 27(12):e263–e271

    Article  PubMed  Google Scholar 

  30. Brendle C, Klose U, Hempel J-M et al (2020) Association of dynamic susceptibility magnetic resonance imaging at initial tumor diagnosis with the prognosis of different molecular glioma subtypes. Neurol Sci 41(12):3625–3632

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wang X, Cao M, Chen H, Ge J, Suo S, Zhou Y (2020) Simplified perfusion fraction from diffusion-weighted imaging in preoperative prediction of IDH1 mutation in WHO grade II–III gliomas: comparison with dynamic contrast-enhanced and intravoxel incoherent motion MRI. Radiol Oncol 54(3):301–310. https://doi.org/10.2478/raon-2020-0037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Takano S (2012) Glioblastoma angiogenesis: VEGF resistance solutions and new strategies based on molecular mechanisms of tumor vessel formation. Brain Tumor Pathol 29(2):73–86

    Article  CAS  PubMed  Google Scholar 

  33. Madden SL, Cook BP, Nacht M et al (2004) Vascular gene expression in nonneoplastic and malignant brain. Am J Pathol 165(2):601–608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pen A, Moreno MJ, Martin J, Stanimirovic DB (2007) Molecular markers of extracellular matrix remodeling in glioblastoma vessels: microarray study of laser-captured glioblastoma vessels. Glia 55(6):559–572

    Article  PubMed  Google Scholar 

  35. Dieterich LC, Mellberg S, Langenkamp E et al (2012) Transcriptional profiling of human glioblastoma vessels indicates a key role of VEGF-A and TGF beta 2 in vascular abnormalization. J Pathol 228(3):378–390

    Article  CAS  PubMed  Google Scholar 

  36. Zhang L, Dimberg A (2016) Pleiotrophin is a driver of vascular abnormalization in glioblastoma. Mol Cell Oncol 3(6):e1141087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhang L, Laaniste L, Jiang Y, Alafuzoff I, Uhrbom L, Dimberg A (2016) Pleiotrophin enhances PDGFB-induced gliomagenesis through increased proliferation of neural progenitor cells. Oncotarget 7(49):80382–80390

    Article  PubMed  PubMed Central  Google Scholar 

  38. Zhang L, Kundu S, Feenstra T et al (2015) Pleiotrophin promotes vascular abnormalization in gliomas and correlates with poor survival in patients with astrocytomas. Sci Signal 8(406):ra125

    Article  CAS  PubMed  Google Scholar 

  39. Zhang L, He L, Lugano R et al (2018) IDH mutation status is associated with distinct vascular gene expression signatures in lower-grade gliomas. Neuro Oncol 20(11):1505–1516

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chawla S, Krejza J, Vossough A et al (2013) Differentiation between oligodendroglioma genotypes using dynamic susceptibility contrast perfusion-weighted imaging and proton MR spectroscopy. AJNR Am J Neuroradiol 34(8):1542–1549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Whitmore RG, Krejza J, Kapoor GS et al (2007) Prediction of oligodendroglial tumor subtype and grade using perfusion weighted magnetic resonance imaging. J Neurosurg 107(3):600–609

    Article  CAS  PubMed  Google Scholar 

  42. Kapoor GS, Gocke TA, Chawla S et al (2009) Magnetic resonance perfusion-weighted imaging defines angiogenic subtypes of oligodendroglioma according to 1p19q and EGFR status. J Neurooncol 92(3):373–386

    Article  PubMed  Google Scholar 

  43. Wang N, Xie S, Liu H, Chen G, Zhang W (2019) Arterial spin labeling for glioma grade discrimination: correlations with IDH1 genotype and 1p/19q status. Transl Oncol 12(5):749–756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Welker K, Boxerman J, Kalnin A, Kaufmann T, Shiroishi M, Wintermark M (2015) ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. AJNR Am J Neuroradiol 36(6):E41-51

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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L.vS. and V.K. systematically searched and reviewed the available literature under direct supervision of D.H. D.H. helped to solve disagreements whenever needed. L.vS. and V.K., under direct supervision of D.H., carried out the meta-analysis. L.vS. and V.K. wrote the first draft of the manuscript, which was reviewed and corrected by D.H., F.M. and M.S. D.H. wrote subsequent versions in close collaboration with L.vS. and V.K. These versions were corrected by F.M. and M.S. All authors read and approved the final manuscript.

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Correspondence to Dylan Henssen.

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van Santwijk, L., Kouwenberg, V., Meijer, F. et al. A systematic review and meta-analysis on the differentiation of glioma grade and mutational status by use of perfusion-based magnetic resonance imaging. Insights Imaging 13, 102 (2022). https://doi.org/10.1186/s13244-022-01230-7

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  • DOI: https://doi.org/10.1186/s13244-022-01230-7

Keywords

  • Dynamic contrast enhancement magnetic resonance perfusion imaging
  • Dynamic susceptibility contrast magnetic resonance perfusion imaging
  • Glioma
  • Molecular classification