Genetic signature of adult gliomas and correlation with MRI features

Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis

OBJECTIVES

To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas.

METHODS

PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity.

RESULTS

We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01).

CONCLUSIONS

Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice.

CLINICAL RELEVANCE STATEMENT

MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery.

KEY POINTS

• Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Regional Development of Glioblastoma: The Anatomical Conundrum of Cancer Biology and Its Surgical Implication

Glioblastoma (GBM) are among the most common malignant central nervous system (CNS) cancers, they are relatively rare. This evidence suggests that the CNS microenvironment is naturally equipped to control proliferative cells, although, rarely, failure of this system can lead to cancer development. Moreover, the adult CNS is innately non-permissive to glioma cell invasion. Thus, glioma etiology remains largely unknown. In this review, we analyze the anatomical and biological basis of gliomagenesis considering neural stem cells, the spatiotemporal diversity of astrocytes, microglia, neurons and glutamate transporters, extracellular matrix and the peritumoral environment. The precise understanding of subpopulations constituting GBM, particularly astrocytes, is not limited to glioma stem cells (GSC) and could help in the understanding of tumor pathophysiology. The anatomical fingerprint is essential for non-invasive assessment of patients’ prognosis and correct surgical/radiotherapy planning.

Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis

The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.

Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma

BACKGROUND AND OBJECTIVE

The O⁶-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively.

METHODS

A retrospective analysis of MR images from GBM patients was conducted. Multivariate prediction models were obtained by machine-learning methods and tested on data from The Cancer Genome Atlas (TCGA) database.

RESULTS

The status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%. Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in GBM.

CONCLUSIONS

The obtained results provide further evidence of an association between standard preoperative MRI variables and MGMT methylation status in GBM.

Genetics and imaging of oligodendroglial tumors

Oligodendroglial tumors are chemosensitive with a favorable prognosis compared with other histological subtypes. The genetic hallmark of co-deletion of 1p and 19q determines both treatment response and prognosis. While this test now forms part of routine histopathology diagnosis in many laboratories, alternative noninvasive imaging biomarkers of tumor genotype remain an attractive proposition. This review will focus on imaging biomarkers of molecular genetics in oligodendroglial tumors.

Glioma diagnostics and biomarkers: an ongoing challenge in the field of medicine and science

Glioma is the most common brain tumor. For the more aggressive form, glioblastoma, standard treatment includes surgical resection, irradiation with adjuvant temozolomide and, on recurrence, experimental chemotherapy. However, the survival of patients remains poor. There is a critical need for minimally invasive biomarkers for diagnosis and as measures of response to therapeutic interventions. Glioma shed extracellular vesicles (EVs), which invade the surrounding tissue and circulate within both the cerebrospinal fluid and the systemic circulation. These tumor-derived EVs and their content serve as an attractive source of biomarkers. In this review, we discuss the current state of the art of biomarkers for glioma with emphasis on their EV derivation.

Potential role of preoperative conventional MRI including diffusion measurements in assessing epidermal growth factor receptor gene amplification status in patients with glioblastoma

BACKGROUND AND PURPOSE

Epidermal growth factor receptor amplification is a common molecular event in glioblastomas. The purpose of this study was to examine the potential usefulness of morphologic and diffusion MR imaging signs in the prediction of epidermal growth factor receptor gene amplification status in patients with glioblastoma.

MATERIALS AND METHODS

We analyzed pretreatment MR imaging scans from 147 consecutive patients with newly diagnosed glioblastoma and correlated MR imaging features with tumor epidermal growth factor receptor amplification status. The following morphologic tumor MR imaging features were qualitatively assessed: 1) border sharpness, 2) cystic/necrotic change, 3) hemorrhage, 4) T2-isointense signal, 5) restricted water diffusion, 6) nodular enhancement, 7) subependymal enhancement, and 8) multifocal discontinuous enhancement. A total of 142 patients had DWI available for quantitative analysis. ADC maps were calculated, and the ADCmean, ADCmin, ADCmax, ADCROI, and ADCratio were measured.

RESULTS

Epidermal growth factor receptor amplification was present in 60 patients (40.8%) and absent in 87 patients (59.2%). Restricted water diffusion correlated with epidermal growth factor receptor amplification (P = .04), whereas the other 7 morphologic MR imaging signs did not (P > .12). Quantitative DWI analysis found that all ADC measurements correlated with epidermal growth factor receptor amplification, with the highest correlations found with ADCROI (P = .0003) and ADCmean (P = .0007).

CONCLUSIONS

Our results suggest a role for diffusion MR imaging in the determination of epidermal growth factor receptor amplification status in glioblastoma. Additional work is necessary to confirm these results and isolate new imaging biomarkers capable of noninvasively characterizing the molecular status of these tumors.

Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images

BACKGROUND

Subtypes of glioblastoma multiforme (GBM) based on genetic and molecular alterations are thought to cause alterations in anatomic MRI owing to downstream biological changes, such as edema production, blood-brain barrier breakdown, and necrosis. The purpose of the current study was to identify a potential relationship between imaging features and the mesenchymal (MES) GBM subtype, which has the worst patient prognosis.

METHODS

MRIs from 46 patients with histologically confirmed GBM were retrospectively analyzed. The volume of contrast enhancement, regions of central necrosis, and hyperintensity of T2/fluid attenuated inversion recovery (FLAIR) were measured. Additionally, the ratio of T2/FLAIR hyperintense volume to the volume of contrast enhancement and necrosis was calculated.

RESULTS

The volume of contrast enhancement, volume of central necrosis, combined volume of contrast enhancement and central necrosis, and the ratio of T2/FLAIR to contrast enhancement and necrosis were significantly different in MES compared with non-MES GBM (Mann-Whitney, P < .05). Receiver-operator characteristics indicated that these 4 metrics were all significant predictors of the MES phenotype. The volume ratio of T2 hyperintensity to contrast enhancement and central necrosis was significantly lower in MES vs non-MES GBM (P < .0001), was a significant predictor of the MES phenotype (area under the curve = 0.93, P < .001), and could be used to stratify short- and long-term overall survival (log-rank, P = .0064 using cutoff of 3.0). These trends were also present when excluding isocitrate dehydrogenase 1 mutant tumors and incorporating covariates such as age and KPS score.

CONCLUSIONS

Results suggest that volume ratio may be a simple, cost-effective, and noninvasive biomarker for quickly identifying MES GBM.

Towards MIB-1 and p53 detection in glioma magnetic resonance image: a novel computational image analysis method

Glioma is the primary tumor in the central nervous system, and poses one of the greatest challenges in clinical treatment. MIB-1 and p53 are the most useful biomarkers for gliomas and could help neurosurgeons establish a therapeutic schedule. However, these biomarkers are commonly detected with the help of immunohistochemistry (IHC), which wastes time and energy and is often influenced by subjective factors. To reduce the subjective factors and improve the efficiency in the judgment of IHC, a novel magnetic resonance image (MRI) analysis method is proposed in the present study to detect the expression status of MIB-1 and p53 in IHC. The proposed method includes two kinds of MRI acquisition (FLAIR and T1 FLAIR images), regions of interest (ROIs) selection, texture features (i.e. the gray level gradient co-occurrence matrix (GLGCM), Minkowski functions (MFs), etc) extraction in ROIs, and classification with a support vector machine in a leave-one-out cross validation strategy. By classifying the ROIs, the performance of the method was evaluated by accuracy, area under ROC curve (AUC), etc. A high accuracy (0.7640 ± 0.0225) and AUC (0.7873 ± 0.0377) for MIB-I detection were achieved. In terms of the texture features, 0.7621 ± 0.0199, 0.7666 ± 0.0365 and 0.7426 ± 0.0451 AUC can be obtained using only GLCM, RLM or GLGCM for MIB-1 detection, respectively. In all, the experimental results demonstrated that MR image texture features are associated with the expression status of MIB-1 and p53. The proposed method has the potential to realize high accuracy and robust detection for MIB-I expression status, which makes it promising for clinical glioma diagnosis and prognosis.

Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma

BACKGROUND AND PURPOSE

Both IDH1 mutation and MGMT promoter methylation are associated with longer survival. We investigated the ability of imaging correlates to serve as noninvasive biomarkers for these molecularly defined GBM subtypes.

MATERIALS AND METHODS

MR imaging from 202 patients with GBM was retrospectively assessed for nonenhancing tumor and edema among other imaging features. IDH1 mutational and MGMT promoter methylation status were determined by DNA sequencing and methylation-specific PCR, respectively. Overall survival was determined by using a multivariate Cox model and the Kaplan-Meier method with a log rank test. A logistic regression model followed by ROC analysis was used to classify the IDH1 mutation and methylation status by using imaging features.

RESULTS

MGMT promoter methylation and IDH1 mutation were associated with longer median survival. Edema levels stratified survival for methylated but not unmethylated tumors. Median survival for methylated tumors with little/no edema was 2476 days (95% CI, 795), compared with 586 days (95% CI, 507-654) for unmethylated tumors or tumors with edema. All IDH1 mutant tumors were nCET positive, and most (11/14, 79%) were located in the frontal lobe. Imaging features including larger tumor size and nCET could be used to determine IDH1 mutational status with 97.5% accuracy, but poorly predicted MGMT promoter methylation.

CONCLUSIONS

Imaging features are potentially predictive of IDH1 mutational status but were poorly correlated with MGMT promoter methylation. Edema stratifies survival in MGMT promoter methylated but not in unmethylated tumors; patients with methylated tumors with little or no edema have particularly long survival.