Glioma and Glioblastoma Multiforme: Comparison
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Please note this is a comparison between Version 2 by Rita Xu and Version 1 by Mohammadreza Alizadeh.

Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios.

  • glioma
  • glioblastoma multiform (GBM)
  • radiomics

1. Introduction

Glioma is the name for all cancers that are thought to come from glial cells. They are also the most common type of brain and spinal cord cancer. About 57% of all gliomas are glioblastoma (GBM), the most common and life-threatening brain tumor. Additionally, 48% of all primary aggressive central nervous system (CNS) tumors are also GBM [1,2,3][1][2][3]. In 2021, the World Health Organization (WHO) classified glioma and glioblastoma based on key molecular biomarkers; this describes neoplastic entities and makes it much less critical for tumor classification to depend on morphologic features. Under the 2021 update, adult diffuse gliomas are divided into three main disease groups based on molecular markers: IDH-mutant, 1p/19q codeleted oligodendroglioma; IDH-mutant, non-codeleted astrocytoma; and IDH-wildtype glioblastoma [4,5][4][5].
Surgery with maximum safe reaction is the standard treatment utilized to treat GBM. The aim of resection is a gross total resection without risking the patient’s functional status. However, complete resection is often not practical, given the infiltrative behavior of gliomas. So, surgery is usually followed by chemotherapy and/or radiation treatment. Today, most people with glioblastoma receive a mix of treatments before and after surgery [6,7][6][7]. These include radiotherapy (RT) alone or with chemotherapy. After surgery, standard treatment gives 60 Gy in 2 Gy doses over six weeks, along with temozolomide (TMZ). Currently, there are three Food and Drug Administration (FDA)-approved medications to treat GBM: TMZ, bevacizumab, and BCNU (carmustine). TMZ is currently the most common FDA-approved chemotherapy drug for treating glioblastoma [8]. An increase in median survival rate has been observed with these combination therapeutics. Other treatment protocols are now mainly in the research phase, including tumor-treating fields (TTFields), vaccine-based immunotherapies, and oncolytic viral T-cell immunotherapy [8].
IDH-mutant astrocytoma and 1p/19q codeleted oligodendroglioma are usually treated by maximum surgical resection. The subsequent treatment depends on the grade of the neoplasm on histopathology. IDH-mutant astrocytoma grade 2 and 1p/19q codeleted oligodendroglioma grade 2 without residual tumor on fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) will be followed by surveillance MRI. IDH-mutant astrocytoma grade 2 with a residual tumor on FLAIR MRI, 1p/19q codeleted oligodendroglioma grade 2 with residue on FLAIR MRI, grades 3 and 4 IDH-mutant astrocytoma, and grade 3 1p/19q codeleted oligodendroglioma will be treated by chemoradiation [9].
Radionecrosis causes additional lesions resembling tumor progression or recurrence during imaging follow-up. Correctly differentiating these lesions is crucial since their therapeutic paradigms differ [10,11][10][11].
MRI and positron emission tomography (PET) approaches have shown the capability of differentiating these scenarios [12,13,14][12][13][14]. There is still no consensus on their effectiveness due to the lack of investigations and heterogeneity in scanning protocols. In addition, these approaches have faced severe restrictions in correctly differentiating these approaches [15,16][15][16].
All types of glioma status post-treatment need to be followed by MRI for diagnosis of recurrence and or progression. The interpretation of MRI findings in glioma after treatment is challenging, given the fact that there is an overlap between post-treatment signal alteration and recurrence/progression.
The post-treatment glioma MRI can show the following conditions:
A.
Pseudoprogression: A transient enlargement of tumoral abnormal signal intensity and enhancement after chemoradiation (usually within six months after treatment) caused by inflammation, edema, damage to the endothelium, blood–brain barrier (BBB) disruption, and oligodendroglial injury after treatment. It is more common within 3 months after completion of therapy, but it can occur years after treatment. Moreover, it is more common in O6-methylguanine-DNA methyltransferase (MGMT)-methylated tumors, particularly when treated with TMZ [15]. Patients are usually stable clinically. Pseudoprogression is generally associated with a longer survival rate [17,18,19][17][18][19]. Pseudoprogression (psp) arises from a pronounced local tissue reaction involving inflammation, edema, and abnormal vessel permeability, leading to new or increased contrast-enhancing lesions. While less severe cases may subside without additional treatment, more severe cases can progress to true treatment-related necrosis over time [20]. The differentiation between tumor progression and pseudoprogression presents a significant challenge, and advanced imaging techniques such as advanced MRI and PET imaging show promise in improving the accuracy of this differentiation [21]. Follow-up MRI scans, conducted 4 to 8 weeks after the initial scan, are commonly utilized to aid in distinguishing between the two conditions [20].
B.
Radiation necrosis: Radiation can cause radiation-induced neurotoxicity in the brain parenchyma, secretion of tumor necrosis factor-alpha (TNF-α), endothelial damage, damage to the BBB, glial and subsequence worsening of edema, and the enhancement/evolution of new areas of abnormal enhancement mimicking recurrence/true progression. Radiation necrosis (RN) usually happens 3–12 months after RT in approximately 3–24% of adult brain tumors but can be seen up to several years and even decades after RT [15]. Histological examination reveals necrosis, edema, gliosis, endothelial thickening, hyalinization, fibrinoid deposition, thrombosis, and vessel occlusion. These pathological criteria distinguish RN from other glioma-related conditions [22]. TNF-α is the primary cytokine released following radiation. Other cytokines that cause endothelial cell death, astrocyte activation, and BBB permeability are upregulated by TNF-alpha [23,24][23][24]. The imaging features of radiation-induced necrosis present challenges in differentiation, as the contrast-enhancing mass on T1-weighted imaging with gadolinium appears similar to tumor progression using conventional MRI techniques [25,26][25][26].
C.
Recurrence: Recurrence is one of the leading causes of death in glioma and GBM [27]. Recurrence timelines can demonstrate substantial variability. A study centered on patients diagnosed with low-grade glioma highlighted that a proportion of 28% had recurrence events within two years subsequent to their main surgery. Conversely, a more substantial proportion, 72%, witnessed recurrence events after this two-year threshold [28]. The timing of recurrence is influenced by the grade of the glioma. High-grade gliomas like glioblastoma have a high recurrence rate, with most recurrences found near the original tumor site [29,30][29][30].
The gold standard for diagnosing recurrent glioma is histologic confirmation. Still, the decision to perform a biopsy must weigh the diagnostic value against procedural risks, especially considering potential complications from previous treatments like radiation or chemotherapy. During the first six months post-treatment, radiographic changes might indicate pseudoprogression, leading many doctors to opt for regular MR imaging instead of immediate biopsy. However, suppose a new lesion emerges on MR images, particularly outside the initial high-dose radiotherapy zone or 6–12 months postradiotherapy. In that case, it might favor tumor recurrence and prompt further actions even without histologic proof of recurrence [30]. Noteworthily, compared to primary tumors, recurrent gliomas more frequently exhibit features such as copy number variations (CNV), combined IDH1 and TERT mutations, compromised cell cycle signaling pathways, and low tumor mutational burden (TMB) [31]. Remarkably, upon recurrence, gliomas might display variations in their histological characteristics. A previously identified low-grade glioma might escalate to a high-grade form. This evolution could be linked to prior therapeutic interventions, such as the intensity of radiation or chemotherapy sessions [32].
D.
True progression: Malignant progression alongside the recurrence of low-grade glioma primarily contributes to its treatment complications and poor prognosis [33]. Pathologically, true progression (TP) is characterized by neovascularization, the proliferation of tumoral cells, and the disruption of the BBB [20]. Numerous determinants, including genetic evolution, microenvironmental interplay, and histological features alterations, mark the progression of gliomas. Additionally, the presence or absence of IDH mutations plays a role in shaping the course of glioma progression, having implications for patient prognosis and the degree of cell malignancy [34]. The glioma’s molecular details and brain location play a critical role in determining its progression rate, which can span from a mere two years to well over ten years [35]. Of note, glioblastoma can exhibit different progression patterns, such as local, diffuse, distant, and multifocal [36]. Although several molecular markers have been identified to predict the progression of the glioma, a lack of standardized methods and insufficient clinical trials have hindered the practicality of this approach in clinical settings [37].

2. Current Differentiating Approaches in Glioma after Treatment

Imaging modalities greatly influence the primary diagnosis and post-therapeutic follow-up of brain gliomas and glioblastomas. Different modalities, such as MRI, computed tomography (CT), and PET, have been utilized in these scenarios [12,13,14][12][13][14]. MRI is a commonly used modality in diagnosing and managing patients with glioblastoma or glioma and is the standard of care for the radiographic characterization of glioblastoma after treatment. New foci of enhancement within and around the resection cavity/radiation field can be seen in both recurrence and radiation necrosis. Usually, nodular boundaries with noticeable edges are more common in tumor recurrence, and blurred plumed borders favor RN. The corpus callosum’s involvement with midline crossing, subependymal dissemination, and numerous enhancing lesions promoted tumor recurrence over RN [26,38,39][26][38][39]. It is also stated that the subependymal enhancement could be the only predictive sign in cases of early progression [40]. If a new focus of enhancement appears after treatment but does not change or gets smaller over time, MRI helps show pseudoprogression [41]. Further, pseudoprogression appears as a self-resorbing focal contrast enhancement in this modality [42]. However, contrast enhancement can be both indicator of the therapy response or tumor relapse. It also manifests the increased permeability of the BBB, which is not specific to these scenarios [43]. The other issue is that GBM and anaplastic glioma may show no or minimal enhancement on MRI, limiting decisions determining invasion and the boundaries of the tumor [44].
In daily practice, the most common modality for differentiation between recurrence/progression and RN is dynamic susceptibility contrast (DSC) MR perfusion. It is well known that the relative cerebral blood volume (rCBV) is higher in tumor progression/recurrence than RN. However, radiation necrosis usually shows heterogeneity on diffusion-weighted imaging (DWI) images [45]. With apparent diffusion coefficient (ADC) values that are larger in necrotic tissue than in recurrent tumor tissue, DWI can distinguish recurrence from pseudoprogression. In contrast to pseudoprogression, tumor progression exhibits higher DSC-derived parameters such as peak height and the percentage of signal recovery [46]. However, neither DWI nor diffusion tensor imaging (DTI) offer enough details to distinguish pseudoprogression from TP reliably. Both DWI and ADC maps produce diverse signal intensities, with regions of reduced diffusion that might signify either highly cellular tumor areas or inflammatory processes [43,47,48,49][43][47][48][49]. Moreover, diffusion imaging can have limitations in resolving lesions with a mixture of recurrence and treatment necrosis since ADC values can be similar in both cases [50]. On top of that, there is a lack of validated diagnostic criteria on an individual level. This means there is no standard way to interpret DWI images, leading to differences in diagnosis between radiologists [51]. Variability in sequences from one center to another, between scanners in the same center, or even in a single scanner can lead to differences in image quality, affecting the accuracy of the diagnosis [52]. Equally important, Rcbv is only semiquantitative (hence the term relative CBV), and that model’s assumptions are violated when there is leakage of contrast agent from the intra- to the extravascular compartment, which is invariably the case in enhancing tumors [20].
MRS is another noninvasive diagnostic tool that measures biochemical changes in the brain, especially the presence of tumors. The choline/creatine ratio used in this method is a good predictor of differentiation between true progression and treatment-induced changes [53]. Recurrent brain neoplasms exhibit an elevation in choline (Cho) as a reflection of increased cell membrane turnover [54]. Moreover, features of radiation necrosis include a variable decrease in n-acetyl-aspartate (NAA), a lack of pronounced Cho elevation, and the presence of lipid-lactate peaks [55]. However, relatively large voxel sizes may lead to partial volume effects between recurrent tumors and treatment-induced changes, limiting its differential power [56]. Beyond that, due to low metabolite concentrations, many acquisitions are required, resulting in long scan times [22]. In the same vein, the need to exclude signal contamination from tissues adjacent to the tumor, such as lipids (from the scalp) and water (from ventricles), makes magnetic resonance spectroscopy (MRS) a challenging technique. Surgical clips also disrupt the local field homogeneity, affecting data quality [53]. Furthermore, there is a considerable overlap of metabolite peaks. This overlap can make distinguishing between different metabolites difficult and lead to misinterpretation of the results [57].
Different modalities of PET scans can also assist in differentiating these stages. Tumor recurrence will usually appear as metabolically active lesions on FDG-PET [48]. Also, radiation necrosis manifests as a metabolically inactive lesion on FDG-PET [48]. However, a lack of specificity and high background uptake in healthy brain tissue curb its ability in differentiating glioma progression from pseudoprogression [58]. On the other hand, amino acid PET is well suited for monitoring treatment response and diagnosing pseudoprogression because amino acid tracers can cross the blood–brain barrier [50]. It can specifically differentiate the cellular component of a tumor mass from inflammatory and necrotic lesions, providing an early response to therapy [59]. This modality can also differentiate between true progression and pseudoprogression, with higher Tbrmax and mean tumor-to-brain ratio (tbrmean) favoring true progression [60], while pseudoprogression manifests with a relatively lower uptake of the radioactive tracer [61]. Nevertheless, amino acid PET requires longer acquisition times than other PET imaging modalities [62]. Additionally, both progression and pseudoprogression can increase metabolic activity on PET scans, making it difficult to differentiate between them [63]. Another issue is that high uptake soon after radiotherapy may be treatment-related, which can be mistaken for recurrence [64]. Moreover, PET is a costly approach and lacks approval and reimbursement by national insurance, which limits its practicality in all settings [65]. Lastly, amino acid PET requires an on-site cyclotron due to its short half-life of 11C, further limiting its widespread usage. [15].
Combining these modalities provides a more accurate diagnosis in these scenarios [66]. In contrast to PET/CT, PET/MRI does not burden ionizing radiation. This can be specifically important when planning for multiple scanning sessions [66]. However, PET/MRI scanners are expensive and not widely accessible. Moreover, the PET/MR imaging parameter cutoff values are not standardized [16]. What is more, the combined approach needs a high scanning time, which may be divided into several daunting sessions [67].
Additionally, a review study established that conventional MRI and 18F-FDG PET possessed higher sensitivity, while thallium single-photon emission CT (SPECT) maintained superior specificity in distinguishing between progressive disease, pseudoprogression, and RN [68]. However, low photon flux, low anatomic resolution, and tracer uptake in the choroid plexus or pituitary limits the SPECTs data for diagnosing [10].
Radiomics is a quantitative approach aiming to extract mineable data from medical images using advanced feature analysis [92,93][69][70]. Radiomics allows medical personnel to extract and analyze quantitative features from medical images to predict tumor behavior, treatment response, and patient outcomes [94][71]. This leading-edge paradigm has exhibited competencies in determining and grading glioma tumors. It has also been utilized in survival prediction [95][72] and has paved the way for precision medicine in these tumors [96][73]. Traditionally, radiomics features include shape, histogram (intensity/density), and texture features, often called handcraft features. More recently, the features extracted by deep learning models have a robust research momentum, often called deep features. Considering radiomics as an invaluable tool in the diagnosis, treatment, and prognosis of brain tumors, radiomics has proven to differentiate high-grade gliomas versus tumefactive demyelinating diseases significantly [97,98][74][75]. Notably, radiomics has shown practical applications in pretreatment evaluation, prognosis, survival, and post-treatment evaluation of glioma and GBM. Regarding the pretreatment assessment, radiomics may detect infiltration and the extent of brain tumors [99,100][76][77]. However, its use in differentiating radionecrosis and pseudoprogression from the true progression and recurrence of these tumors is a controversial debate.

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