Molecular Subtype Classification in Lower-Grade Glioma with Accelerated DTI

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach

BACKGROUND

The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas.

METHODS

This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data.

RESULTS

We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617].

CONCLUSIONS

Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Advances in molecular and imaging biomarkers in lower-grade gliomas

INTRODUCTION

Lower-grade (grade 2-3) gliomas (LGGs) constitutes a group of primary brain tumors with variable clinical behaviors and treatment responses. Recent advancements in molecular biology have redefined their classification, and novel imaging modalities emerged for the noninvasive diagnosis and follow-up.

AREAS COVERED

This review comprehensively analyses the current knowledge on molecular and imaging biomarkers in LGGs. Key molecular alterations, such as IDH mutations and 1p/19q codeletion, are discussed for their prognostic and predictive implications in guiding treatment decisions. Moreover, the authors explore theranostic biomarkers for the potential of tailored therapies. Additionally, they also describe the utility of advanced imaging modalities, including widely available techniques, as dynamic susceptibility contrast perfusion-weighted imaging and less validated, emerging approaches, for the noninvasive LGGs characterization and follow-up.

EXPERT OPINION

The integration of molecular markers enhanced the stratification of LGGs, leading to the new concept of integrated histomolecular classification. While the IDH mutation is an established key prognostic and predictive marker, recent results from IDH inhibitors trials showed its potential value as a theranostic marker. In this setting, advanced MRI techniques such as 2-D-hydroxyglutarate spectroscopy are very promising for the noninvasive diagnosis and monitoring of LGGs. This progress offers exciting prospects for personalized medicine and improved treatment outcomes in LGGs.

MRI features predict tumor grade in isocitrate dehydrogenase (IDH)-mutant astrocytoma and oligodendroglioma

PURPOSE

Nearly all literature for predicting tumor grade in astrocytoma and oligodendroglioma pre-dates the molecular classification system. We investigated the association between contrast enhancement, ADC, and rCBV with tumor grade separately for IDH-mutant astrocytomas and molecularly-defined oligodendrogliomas.

METHODS

For this retrospective study, 44 patients with IDH-mutant astrocytomas (WHO grades II, III, or IV) and 39 patients with oligodendrogliomas (IDH-mutant and 1p/19q codeleted) (WHO grade II or III) were enrolled. Two readers independently assessed preoperative MRI for contrast enhancement, ADC, and rCBV. Inter-reader agreement was calculated, and statistical associations between MRI metrics and WHO grade were determined per reader.

RESULTS

For IDH-mutant astrocytomas, both readers found a stepwise positive association between contrast enhancement and WHO grade (Reader A: OR 7.79 [1.97, 30.80], p = 0.003; Reader B: OR 6.62 [1.70, 25.82], p = 0.006); both readers found that ADC was negatively associated with WHO grade (Reader A: OR 0.74 [0.61, 0.90], p = 0.002); Reader B: OR 0.80 [0.66, 0.96], p = 0.017), and both readers found that rCBV was positively associated with WHO grade (Reader A: OR 2.33 [1.35, 4.00], p = 0.002; Reader B: OR 2.13 [1.30, 3.57], p = 0.003). For oligodendrogliomas, both readers found a positive association between contrast enhancement and WHO grade (Reader A: OR 15.33 [2.56, 91.95], p = 0.003; Reader B: OR 20.00 [2.19, 182.45], p = 0.008), but neither reader found an association between ADC or rCBV and WHO grade.

CONCLUSIONS

Contrast enhancement predicts WHO grade for IDH-mutant astrocytomas and oligodendrogliomas. ADC and rCBV predict WHO grade for IDH-mutant astrocytomas, but not for oligodendrogliomas.

Correlation of Diffusion Tensor Imaging Parameters with the Pathological Grade of Brain Glioma and Expression of Vascular Endothelial Growth Factor and Ki-67

Background: Most brain gliomas are high-grade and likely to spread locally. Consequently, these patients commonly have a poor prognosis. Accurate identification of the malignancy grade of brain glioma before treatment is of great clinical significance. Objectives: This study aimed to explore the correlation of diffusion tensor imaging (DTI) parameters, fractional anisotropy (FA), and apparent diffusion coefficient (ADC) with the pathological grade of brain glioma and expression of vascular endothelial growth factor (VEGF) and Ki-67. Patients and Methods: A total of 116 patients were selected for this study from January 2018 to December 2019. All the participants underwent magnetic resonance imaging (MRI) and DTI before surgery, and the FA and ADC values were measured for the regions of interest. Surgically resected tumor specimens were collected for immunohistochemical assay. Finally, the FA and ADC values and positive expression rates of VEGF and Ki-67 were compared. Results: A significantly higher FA, besides the positive expression of VEGF and Ki-67, was reported in the high-grade group, whereas a lower ADC was found in this group compared to the low-grade group (P < 0.05). Areas of normal white matter and peritumoral edema had higher FA values, whereas lower ADCs were measured in these areas compared to the cerebrospinal fluid (P < 0.05). The FA of tumor parenchymal area was positively correlated with the World Health Organization (WHO) WHO class of tumors (r = 0.588, P = 0.028), and the expression of VEGF and Ki-67 was positively correlated with the WHO grade (r = 0.843, P = 0.002 and r = 0.743, P = 0.006, respectively). The FA of tumor parenchymal area was positively correlated with the expression of VEGF and Ki-67 (r = 0.654, P = 0.008 and r = 0.567, P = 0.012, respectively). However, the ADC of tumor parenchymal area was not significantly correlated with the WHO grade, VEGF expression, or Ki-67 expression (r = 0.143, P = 0.156, r = 0.232, P = 0.116, and r = 0.054, P = 0.179, respectively). Conclusion: The FA value, as a DTI parameter, is valuable for assessing the malignancy grade of tumor cells and can provide a proper reference for formulating treatment regimens for brain gliomas.

Feasibility of evaluating the histologic and genetic subtypes of WHO grade II-IV gliomas by diffusion-weighted imaging

BACKGROUND

To explore the feasibility of diffusion-weighted imaging (DWI) metrics to predict the histologic subtypes and genetic status of gliomas (e.g., IDH, MGMT, and TERT) noninvasively.

METHODS

One hundred and eleven patients with pathologically confirmed WHO grade II-IV gliomas were recruited retrospectively. Apparent diffusion coefficient (ADC) values were measured in solid parts of gliomas on co-registered T2-weighted images and were compared with each other in terms of WHO grading and genotypes using t-tests. Receiver operating characteristic analysis was performed to assess the diagnostic performances of ADC. Subsequently, multiple linear regression was used to find independent variables, which can directly affect ADC values.

RESULTS

The values of overall mean ADC (omADC) and normalized ADC (nADC) of high grade gliomas and IDH wildtype gliomas were lower than low grade gliomas and IDH mutated gliomas (P < 0.05). nADC values showed better diagnostic performance than omADC in identifying tumor grade (AUC: 0.787 vs. 0.750) and IDH status (AUC: 0.836 vs. 0.777). ADC values had limited abilities in distinguishing TERT status (AUC = 0.607 for nADC and 0.617 for omADC) and MGMT status (AUC = 0.651 for nADC). Only tumor grade and IDH status were tightly associated with ADC values.

CONCLUSION

DWI metrics can predict glioma grading and IDH mutation noninvasively, but have limited use in detecting TERT mutation and MGMT methylation.

circRPPH1_025 Overexpression Promotes Migration and Invasion of Glioblastoma Multiforme

Objective

To study the effect of circ_0000512 (circRPPH1_025) on the tumorigensis and development of glioblastoma and its molecular mechanism.

Methods

The expression levels of circ_0000512 in normal astrocytes (NHA) and human glioblastoma cell lines (U87, U251, and A172) and the expression levels of circ_0000512 and linear RNA RPPH1 in U87 cells after RNase R treatment were detected by qRT-PCR. The effects of circ_0000512 knockdown or overexpression on the proliferation, migration, invasion, and epithelial-mesenchymal transition of U87 cells were detected by CCK-8 assay, cell colony formation assay, transwell invasion assay, wound healing assay, and western blot.

Results

The expression of circ_0000512 was upregulated in glioblastoma cells, and the overexpression of circ_0000512 was beneficial to the proliferation, migration, invasion, and epithelial-mesenchymal transition of U87 cells, while knockdown of circ_0000512 showed the opposite results.

Conclusion

circ_0000512 can be used as a potential target for early diagnosis and targeted therapy of glioblastoma multiforme.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

RFC2: a prognosis biomarker correlated with the immune signature in diffuse lower-grade gliomas

Diffuse lower-grade gliomas (LGG) represent the highly heterogeneous and infiltrative neoplasms in the central nervous system (CNS). Replication factor C 2 (RFC2) is a subunit of the RFC complex that modulates DNA replication and repair. However, the prognosis value of RFC2 and its association with the immune signature of tumor microenvironment (TME) in LGG remains unknown. Based on Oncomine, TCGA, GTEx, TIMER, GEPIA, and HPA databases, we evaluated RFC2 expression levels and its clinical prognostic value in LGG and other cancers. Then we analyzed the correlations between RFC2 expression and tumor mutation burden (TMB), tumor microsatellite instability (MSI), and mismatch repair (MMR) genes across cancers. And CIBERSORT and ESTIMATE algorithms were conducted to estimate the association of RFC2 with immune cell infiltration of LGG. Additionally, we performed the functional enrichment analyses of RFC2 in LGG. Then functional experiments were employed to further validate the oncogenic role of RFC2 in LGG. Our results showed that RFC2 was widely highly expressed in most types of cancer. And its expression was closely related to the clinicopathological features and prognosis in LGG and other cancer types. RFC2 levels were also correlated with TMB and MSI across various cancers. Furthermore, RFC2 was positively associated with the infiltration levels of immune cells and immune checkpoint genes in LGG. Additionally, in vitro experiments revealed that RFC2 played an oncogenic role in LGG progression. In conclusion, our findings revealed that RFC2 could serve as a reliable biomarker to predict the prognosis and immune signature for LGG.

MRI biomarkers in neuro-oncology

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

FABP5 enhances malignancies of lower-grade gliomas via canonical activation of NF-κB signaling

Low‐grade gliomas (LGGs) are grade III gliomas based on the WHO classification with significant genetic heterogeneity and clinical properties. Traditional histological classification of gliomas has been challenged by the improvement of molecular stratification; however, the reproducibility and diagnostic accuracy of LGGs classification still remain poor. Herein, we identified fatty acid binding protein 5 (FABP5) as one of the most enriched genes in malignant LGGs and elevated FABP5 revealed severe outcomes in LGGs. Functionally, lentiviral suppression of FABP5 reduced malignant characters including proliferation, cloning formation, immigration, invasion and TMZ resistance, contrarily, the malignancies of LGGs were enhanced by exogenous overexpression of FABP5. Mechanistically, epithelial‐mesenchymal transition (EMT) was correlated to FABP5 expression in LGGs and tumour necrosis factor α (TNFα)‐dependent NF‐κB signalling was involved in this process. Furthermore, FABP5 induced phosphorylation of inhibitor of nuclear factor kappa‐B kinase α (IKKα) thus activated nuclear factor kappa‐B (NF‐κB) signalling. Taken together, our study indicated that FABP5 enhances malignancies of LGGs through canonical activation of NF‐κB signalling, which could be used as individualized prognostic biomarker and potential therapeutic target of LGGs.

Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters

Objectives

To explore whether a simplified lesion delineation method and a set of diffusion tensor imaging (DTI) metric-based histogram parameters (mean, 25th percentile, 75th percentile, skewness, and kurtosis) are efficient at predicting the molecular pathology status (MGMT methylation, IDH mutation, TERT promoter mutation, and 1p19q codeletion) of lower grade insular gliomas (grades II and III).

Methods

40 lower grade insular glioma patients in two medical centers underwent preoperative DTI scanning. For each patient, the entire abnormal area in their b-non (b0) image was defined as region of interest (ROI), and a set of histogram parameters were calculated for two DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD). Then, we compared how these DTI metrics varied according to molecular pathology and glioma grade, with their predictive performance individually and jointly assessed using receiver operating characteristic curves. The reliability of the combined prediction was evaluated by the calibration curve and Hosmer and Lemeshow test.

Results

The mean, 25th percentile, and 75th percentile of FA were associated with glioma grade, while the mean, 25th percentile, 75th percentile, and skewness of both FA and MD predicted IDH mutation. The mean, 25th percentile, and 75th percentile of FA, and all MD histogram parameters significantly distinguished TERT promoter status. Similarly, all MD histogram parameters were associated with 1p19q status. However, none of the parameters analyzed for either metric successfully predicted MGMT methylation. The 25th percentile of FA yielded the highest prediction efficiency for glioma grade, IDH mutation, and TERT promoter mutation, while the 75th percentile of MD gave the best prediction of 1p19q codeletion. The combined prediction could enhance the discrimination of grading, IDH and TERT mutation, and also with a good fitness.

Conclusions

Overall, more invasive gliomas showed higher FA and lower MD values. The simplified ROI delineation method presented here based on the combination of appropriate histogram parameters yielded a more practical and efficient approach to predicting molecular pathology in lower grade insular gliomas. This approach could help clinicians to determine the extent of tumor resection required and reduce complications, enabling more precise treatment of insular gliomas in combination with radiotherapy and chemotherapy.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

PURPOSE

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

METHODS

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10^-3mm²/s (V_ADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

RESULTS

V_ADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V_ADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V_ADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

CONCLUSION

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V_ADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. V_ADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Prognostic Value of Preoperative MRI Metrics for Diffuse Lower-Grade Glioma Molecular Subtypes

BACKGROUND AND PURPOSE

Despite the improved prognostic relevance of the 2016 WHO molecular-based classification of lower-grade gliomas, variability in clinical outcome persists within existing molecular subtypes. Our aim was to determine prognostically significant metrics on preoperative MR imaging for lower-grade gliomas within currently defined molecular categories.

MATERIALS AND METHODS

We undertook a retrospective analysis of 306 patients with lower-grade gliomas accrued from an institutional data base and The Cancer Genome Atlas. Two neuroradiologists in consensus analyzed preoperative MRIs of each lower-grade glioma to determine the following: tumor size, tumor location, number of involved lobes, corpus callosum involvement, hydrocephalus, midline shift, eloquent cortex involvement, ependymal extension, margins, contrast enhancement, and necrosis. Adjusted hazard ratios determined the association between MR imaging metrics and overall survival per molecular subtype, after adjustment for patient age, patient sex, World Health Organization grade, and surgical resection status.

RESULTS

For isocitrate dehydrogenase (IDH) wild-type lower-grade gliomas, tumor size (hazard ratio, 3.82; 95% CI, 1.94-7.75; P < .001), number of involved lobes (hazard ratio, 1.70; 95% CI, 1.28-2.27; P < .001), hydrocephalus (hazard ratio, 4.43; 95% CI, 1.12-17.54; P = .034), midline shift (hazard ratio, 1.16; 95% CI, 1.03-1.30; P = .013), margins (P = .031), and contrast enhancement (hazard ratio, 0.34; 95% CI, 0.13-0.90; P = .030) were associated with overall survival. For IDH-mutant 1p/19q-codeleted lower-grade gliomas, tumor size (hazard ratio, 2.85; 95% CI, 1.06-7.70; P = .039) and ependymal extension (hazard ratio, 6.34; 95% CI, 1.07-37.59; P = .042) were associated with overall survival.

CONCLUSIONS

MR imaging metrics offers prognostic information for patients with lower-grade gliomas within molecularly defined classes, with the greatest prognostic value for IDH wild-type lower-grade gliomas.

Prediction of lower-grade glioma molecular subtypes using deep learning

INTRODUCTION

It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively.

METHODS

A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients.

RESULTS

The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly.

CONCLUSIONS

A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype.