Cerebral blood volume calculated by dynamic susceptibility contrast-enhanced perfusion MR imaging: preliminary correlation study with glioblastoma genetic profiles

Deciphering glioblastoma: Unveiling imaging markers for predicting MGMT promoter methylation status

Glioblastoma, the most common primary malignant tumor of the central nervous system in adults, is also among the most lethal. Despite a comprehensive treatment approach which utilizes surgery and postoperative chemoradiation, prognosis typically remains dismal. However certain epigenetic modifications, such as methylation of the MGMT promoter, have been proven to correlate with improved post-treatment outcomes. The 2021 WHO classification emphasizes molecular characteristics, highlighting shared genomic alterations across different grades and positioning MGMT methylation as a key influencer of outcomes. A combined diagnostic approach involving current imaging technology and emerging radiomics and deep learning models may allow for timely and accurate prediction of MGMT methylation status and therefore earlier and more individualized treatment and prognostication. Though these advanced radiomics models are rapidly emerging, additional development, standardization, and implementation may lead to a higher and more individualized level of patient care. This review explores the potential of imaging features in predicting MGMT promoter methylation, a critical determinant of therapeutic response and patient outcomes.

Dynamic susceptibility contrast‑enhanced perfusion magnetic resonance imaging parameters for predicting MGMT promoter methylation and prognostic value in newly diagnosed patients with glioblastoma

O6-methylguanine DNA methyltransferase (MGMT) promoter methylation is an important clinical biomarker of newly diagnosed glioblastoma. Previous radiological studies using dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion have aimed to predict MGMT methylation status non-invasively in gliomas with radiological characteristics. The possibility of predicting MGMT methylation status using DSC-MRI perfusion with a radiological approach remains controversial. The present study aimed to evaluate the usefulness of MRI perfusion parameters as non-invasive markers to predict MGMT methylation status and prognosis in newly diagnosed glioblastoma patients. Thus, 50 patients with histologically confirmed primary glioblastoma, IDH-wildtype who underwent tumor resection at Osaka University Hospital (Suita, Japan) between January 2017 and January 2023 were included in this study. The mean cerebral blood volume (CBV) ratio (rCBV) and cerebral blood flow (CBF) ratio (rCBF) for tumors with MGMT methylation (mean rCBV:2.09 and mean rCBF:3.08) were significantly higher compared with those for tumors without MGMT methylation (mean rCBV:1.33 and mean rCBF:1.85; P<0.05). While patients with MGMT methylation had longer progression-free survival (PFS) compared with those without MGMT methylation (P<0.05), there was no significant difference in OS with or without MGMT methylation (P=0.06). By contrast, there was no association between MRI perfusion parameters and OS or PFS in patients with glioblastoma. Furthermore, the association between CBV, CBF, MGMT promotor methylation status, OS, and PFS were explored. There was no significant prognostic difference between low vascularity tumors (rCBV <1.3 or rCBF <1.8) with or without MGMT methylation. On the other hand, high vascularity tumors (rCBF ≥1.8) with MGMT promotor methylation were associated to longer OS and PFS compared with those without. However, there was no association between MGMT methylation status and OS or PFS in patients with high rCBV (rCBV ≥1.3). The present study indicated that CBV and CBF could be used to predict the MGMT methylation status in glioblastomas. However, the prognostic value of tumor vascularity and MGMT methylation status may be limited.

Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status

Background.Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis.O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients.Purpose.To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities.Methods.Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset.Results.The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively.Conclusions.SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

BACKGROUND AND OBJECTIVE

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.

METHODS

We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.

RESULTS

WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.

CONCLUSIONS

This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Exploring MGMT methylation-driven structural connectivity changes in insular gliomas: a tractography and graph theoretical analysis

OBJECTIVES

This study aims to explore the relationship between the methylation levels of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter and the structural connectivity in insular gliomas across hemispheres.

METHODS

We analyzed 32 left and 29 right insular glioma cases and 50 healthy controls, using differential tractography, correlational tractography, and graph theoretical analysis to investigate the correlation between structural connectivity and the methylation level.

RESULTS

The differential tractography results revealed that in left insular glioma, the volume of affected inferior fronto-occipital fasciculus (IFOF, p = 0.019) significantly correlated with methylation levels. Correlational tractography results showed that the quantitative anisotropy (QA) value of peritumoral fiber tracts also exhibited a significant correlation with methylation levels (FDR < 0.05). On the other hand, in right insular glioma, anterior internal part of the reticular tract, IFOF, and thalamic radiation showed a significant correlation with methylation levels but at a different correlation direction from the left side (FDR < 0.05). The graph theoretical analysis showed that in the left insular gliomas, only the radius of graph was significantly lower in methylated MGMT group than unmethylated group (p = 0.047). No significant correlations between global properties and methylation levels were observed in insular gliomas on both sides.

CONCLUSION

Our findings highlight a significant, hemisphere-specific correlation between MGMT promoter methylation and structural connectivity in insular gliomas. This study provides new insights into the genetic influence on glioma pathology, which could inform targeted therapeutic strategies.

Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study

PURPOSE

Molecular marker status is clinically relevant for treatment planning and predicting the prognosis of gliomas. This study aimed to assess whether quantitative imaging parameters from dynamic susceptibility contrast- (DSC-) and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict the molecular marker status of lower-grade gliomas (LGGs).

MATERIALS AND METHODS

Overall, 132 patients with LGGs who underwent DSC- and DCE-MRI were retrospectively enrolled. Statuses of relevant molecular markers including isocitrate dehydrogenase isoenzyme (IDH), 1p19q codeletion, epidermal growth factor receptor (EGFR), O6-methylguanine-DNA methyltransferase (MGMT), and telomerase reverse transcriptase (TERT) were collected. For each molecular marker, age, tumor diameter and location, and DSC- and DCE-MRI parameters, including the normalized cerebral blood volume (nCBV), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp), were compared. Multivariable logistic regression analyses were performed.

RESULTS

The nCBV was significantly lower in LGGs with IDH mutation (p = .001) and TERT mutation (p = .027) than those without these mutations. Ktrans (p = .034), Ve (p = .023), and Vp (p = .044) values were significantly lower in MGMT methylated LGGs than in MGMT unmethylated LGGs. Perfusion parameters were not significantly associated with EGFR amplification and 1p19q codeletion. Young age (p < .001) and small diameter (p = .001) were significantly associated with IDH mutation. The nCBV was independently associated with IDH status (AUC, 0.817; 95% CI: 0.739-0.894).

CONCLUSIONS

DSC- and DCE-MRI parameters demonstrated correlations with molecular markers of LGGs. Especially, the nCBV can be helpful in predicting the IDH mutation status.

The peritumoral brain zone in glioblastoma: where we are and where we are going

Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.

Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge

Simple Summary

O6-methylguanine-DNA methyl transferase (MGMT) methylation in glioblastoma is an important prognostic and predictive factor that requires an invasive surgical procedure for identification. In several recent studies, MGMT methylation prediction models were developed using MR images, and good diagnostic performance was achieved, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MRI. With unexpected negative results, approximately 80% of the developed models showed no significant difference with the chance level of 50% in terms of external validation accuracy. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MRI, even using deep learning.

Abstract

O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.

Non-invasive prediction of p53 and Ki-67 labelling indices and O-6-methylguanine-DNA methyltransferase promoter methylation status in adult patients with isocitrate dehydrogenase wild-type glioblastomas using diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging combined with conventional MRI

AIM

To assess whether conventional magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) could non-invasively predict p53 and Ki-67 labelling index (LI) and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in adult isocitrate dehydrogenase (IDH) wild-type glioblastomas.

METHODS

The conventional MRI, DWI, and DSC-PWI results of 120 adult patients with IDH wild-type glioblastomas were reviewed retrospectively and their efficacy was analysed using chi-square tests or Fisher's exact test. Relative minimum apparent diffusion coefficient (rADCmin) and relative maximum cerebral blood volume (rCBVmax) values were compared between glioblastomas with different molecular statuses using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves and logistic regression were used to evaluate predictive performance.

RESULTS

Glioblastomas with a high p53 LI were more likely to show a well-defined enhancement margin (p=0.047). Glioblastomas in the high-Ki-67-LI group demonstrated significantly lower rADCmin (p<0.001) and higher rCBVmax (p=0.001) values than those in the low-Ki-67-LI group. Tumours without MGMT promoter methylation showed lower rADCmin (p<0.001) and higher rCBVmax (p<0.001) values than those with it. The rCBVmax value exhibited a greater efficacy in predicting the MGMT promoter methylation status of adult IDH wild-type glioblastomas than the rADCmin value (p=0.001).

CONCLUSIONS

The present results suggest that conventional and DWI and DSC-PWI results are influenced by the molecular status of the glioblastoma and indicate that DWI and DSC-PWI may help to identify regions of high invasiveness within heterogeneous glioblastomas.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis

PURPOSE

We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients.

METHODS

A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis.

RESULTS

A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%-90%) and 84% (95% CI 78%-88%) in the training cohort (n=15) and 84% (95% CI 70%-92%) and 78% (95% CI 63%-88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88-0.93) in the training cohort and 0.88 (95% CI 0.85-0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance.

CONCLUSION

This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods.

Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma

Objectives

Phosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.

Methods

In this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC).

Results

The CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).

Conclusions

The combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.

Perfusion parameters derived from MRI for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas

PURPOSE

To investigate the feasibility for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas(GBMs) by intravoxel incoherent motion(IVIM) and dynamic susceptibility contrast(DSC).

METHODS

Preoperative IVIM and DSC images of 71 patients(IDH mutation:45, IDH wildtype: 26; MGMT methylation: 31, MGMT unmethylation:40) with glioblastomas were analyzed retrospectively. Perfusion parameters including microcirculation perfusion coefficient(D*), perfusion fraction(f), cerebral blood volume(CBV) and cerebral blood flow(CBF) were measured. Corrected perfusion parameters containing corrected perfusion coefficient(ADC_perf) and simplified perfusion fraction(SPF) were from the simplified IVIM with 3 b values. Correlations among parameters were analyzed by Spearman correlation. All parameters were compared with Mann-Whitney U test. Univariate and multivariate logistic regression models were constructed. The receiver operating characteristic(ROC) curve was analyzed.

RESULTS

The IVIM parameters showed merely moderate correlations with CBV and showed no correlation with CBF. IDH mutation GBMs showed lower D*, ADC_perf, SPF, CBV and higher f than IDH wildtype GBMs(all p < 0.05). D* was the independent predictor for IDH mutation with the highest AUC of 0.912(95%CI: 0.821-0.966). The D*, ADC_perf, SPF and CBV of MGMT promoter methylation GBMs were lower than unmethylation GBMs while f was higher(all p < 0.05). Multivariate model showed the highest prediction efficacy for MGMT promoter methylation with an AUC of 0.915(95%CI: 0.824-0.968). The CBF was not useful in distinguishing IDH mutation and MGMT promoter methylation status(p = 0.055, 0.215).

CONCLUSION

IDH mutation and MGMT promoter methylation status in GBMs can be assessed effectively by IVIM and DSC. Besides, D* was the independent predictor of IDH mutation status.

State of Radiomics in Glioblastoma

Radiomics is an emerging discipline that aims to make intelligent predictions and derive medical insights based on quantitative features extracted from medical images as a means to improve clinical diagnosis or outcome. Pertaining to glioblastoma, radiomics has provided powerful, noninvasive tools for gaining insights into pathogenesis and therapeutic responses. Radiomic studies have yielded meaningful biological understandings of imaging features that are often taken for granted in clinical medicine, including contrast enhancement on glioblastoma magnetic resonance imaging, the distance of a tumor from the subventricular zone, and the extent of mass effect. They have also laid the groundwork for noninvasive detection of mutations and epigenetic events that influence clinical outcomes such as isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT). In this article, we review advances in the field of glioblastoma radiomics as they pertain to prediction of IDH mutation status and MGMT promoter methylation status, as well as the development of novel, higher order radiomic parameters.

Correlation between dynamic susceptibility contrast perfusion MRI and genomic alterations in glioblastoma

PURPOSE

To determine if dynamic susceptibility contrast perfusion MR imaging (DSC-pMRI) can predict significant genomic alterations in glioblastoma (GB).

METHODS

A total of 47 patients with treatment-naive GB (M/F: 23/24, mean age: 54 years, age range: 20-90 years) having DSC-pMRI with leakage correction and genomic analysis were reviewed. Mean relative cerebral blood volume (rCBV), maximum rCBV, relative percent signal recovery (rPSR), and relative peak height (rPH) were derived from T2* signal intensity-time curves by ROI analysis. Major genomic alterations of IDH1-132H, MGMT, p53, EGFR, ATRX, and PTEN status were correlated with DSC-pMRI-derived GB parameters. Statistical analysis was performed utilizing the independent-samples t-test, ROC (receiver operating characteristic) curve analysis, and multivariable stepwise regression model.

RESULTS

rCBVmean and rCBVmax were significantly different in relation to the IDH1, MGMT, p53, and PTEN mutation status (all p < 0.05). The rPH of the p53 mutation-positive GBs (mean 5.8 ± 2.8) was significantly higher than those of the p53 mutation-negative GBs (mean 4.0 ± 1.5) (p = 0.022). Multivariable stepwise regression analysis revealed that the presence of IDH-1 mutation (B = - 2.81, p = 0.005) was associated with decreased rCBVmean; PTEN mutation (B = - 1.21, p = 0.003) and MGMT methylation (B = - 1.47, p = 0.038) were associated with decreased rCBVmax; and ATRX loss (B = - 1.05, p = 0.008) was associated with decreased rPH.

CONCLUSION

Significant associations were identified between DSC-pMRI-derived parameters and major genomic alterations, including IDH-1 mutation, MGMT methylation, ATRX loss, and PTEN mutation status in GB.

MGMT Promoter Methylation Status Is Not Related to Histological or Radiological Features in IDH Wild-type Glioblastomas

O6-methylguanine DNA methyltransferase (MGMT) promoter methylation is an important favorable predictive marker in patients with glioblastoma (GBM). We hypothesized that MGMT status could be a surrogate marker of pretreatment tumor biology observed as histopathological and radiological features. Apart from some radiological studies aiming to noninvasively predict the MGMT status, few studies have investigated relationships between MGMT status and phenotypical tumor biology. We have therefore aimed to investigate such relationships in 85 isocitrate dehydrogenase (IDH) wild-type GBMs. MGMT status was determined by methylation-specific PCR and was assessed for associations with 22 histopathological features, immunohistochemical proliferative index and microvessel density measurements, conventional magnetic resonance imaging characteristics, preoperative speed of tumor growth, and overall survival. None of the investigated histological or radiological features were significantly associated with MGMT status. Methylated MGMT status was a significant independent predictor of improved overall survival. In conclusion, our results suggest that MGMT status is not related to the pretreatment phenotypical biology in IDH wild-type GBMs. Furthermore, our findings suggest the survival benefit of MGMT methylated GBMs is not due to an inherently less aggressive tumor biology, and that conventional magnetic resonance imaging features cannot be used to noninvasively predict the MGMT status.

MGMT Promoter Methylation Status in Initial and Recurrent Glioblastoma: Correlation Study with DWI and DSC PWI Features

BACKGROUND AND PURPOSE

O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in primary and recurrent glioblastoma may change during treatment. The purpose of this study was to correlate MGMT promoter methylation status changes with DWI and DSC PWI features in patients with recurrent glioblastoma after standard treatment.

MATERIALS AND METHODS

Between January 2008 and November 2016, forty patients with histologically confirmed recurrent glioblastoma were enrolled. Patients were divided into 3 groups according to the MGMT promoter methylation status for the initial and recurrent tumors: 2 groups whose MGMT promoter methylation status remained, group methylated (n = 13) or group unmethylated (n = 18), and 1 group whose MGMT promoter methylation status changed from methylated to unmethylated (n = 9). Normalized ADC and normalized relative CBV values were obtained from both the enhancing and nonenhancing regions, from which histogram parameters were calculated. The ANOVA and the Kruskal-Wallis test followed by post hoc tests were performed to compare histogram parameters among the 3 groups. The t test and Mann-Whitney U test were used to compare parameters between group methylated and group methylated to unmethylated. Receiver operating characteristic curve analysis was used to measure the predictive performance of the normalized relative CBV values between the 2 groups.

RESULTS

Group methylated to unmethylated showed significantly higher means and 90th and 95th percentiles of the cumulative normalized relative CBV values of the nonenhancing region of the initial tumor than group methylated and group unmethylated (all P < .05). The mean normalized relative CBV value of the nonenhancing region of the initial tumor was the best predictor of methylation status change (P < .001), with a sensitivity of 77.78% and specificity of 92.31% at a cutoff value of 2.594.

CONCLUSIONS

MGMT promoter methylation status might change in recurrent glioblastoma after standard treatment. The normalized relative CBV values of the nonenhancing region at the first preoperative MR imaging were higher in the MGMT promoter methylation change group from methylation to unmethylation in recurrent glioblastoma.

Noninvasive Assessment of O(6)-Methylguanine-DNA Methyltransferase Promoter Methylation Status in World Health Organization Grade II-IV Glioma Using Histogram Analysis of Inflow-Based Vascular-Space-Occupancy Combined with Structural Magnetic Resonance Imaging

BACKGROUND

O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation is an important prognostic factor for gliomas and is associated with tumor angiogenesis. Arteriolar cerebral blood volume (CBVa) obtained from inflow-based vascular-space-occupancy (iVASO) magnetic resonance imaging (MRI) is assumed to be an indicator of tumor microvasculature. Its preoperative predictive ability for MGMT promoter methylation remains unclear.

PURPOSE

To investigate the role of iVASO-CBVa histogram features in determining MGMT promoter methylation status of grade II-IV gliomas.

STUDY TYPE

Retrospective SUBJECTS: Forty-six patients consisting of 20 MGMT methylated and 26 unmethylated gliomas.

FIELD STRENGTH/SEQUENCE

3.0 T magnetic resonance images containing iVASO MRI, T₁ -weighted image (T₁ WI), T₂ -weighted image, T₂ -weighted fluid attenuated inversion recovery image images, and enhanced T₁ WI.

ASSESSMENT

Sixteen structural imaging features were visually evaluated on structural MRI and 14 CBVa histogram features were extracted from iVASO-CBVa maps.

STATISTICAL TESTS

Imaging features were screened and ranked using Fisher's exact test, Mann-Whitney U-test, and randomforest algorithm. Features with higher importance were selected to develop logistic regression models to determine MGMT methylation status. Receiver operating characteristics (ROC) curve with the area under the curve (AUC) and leave-one-out cross-validation (LOOCV) were used to assess effectiveness and stability.

RESULTS

The top two CBVa histogram features were root mean squared (RMS) and variance. The top two structural imaging features were contrast-enhancing component of the tumor (CET) location and tumor location. Both the CBVa model of RMS and variance (ROC, AUC = 0.867; LOOCV, AUC = 0.819) and the model of structural features (ROC, AUC = 0.882; LOOCV, AUC = 0.802) accurately identified MGMT methylation. The fusion model of CBVa RMS and CET location improved diagnostic performance (ROC, AUC = 0.931; LOOCV, AUC =0.906). DATA CONCLUSION: iVASO-CBVa has potential in evaluating MGMT methylation status in grade II-IV gliomas.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

The Practical Application of Emerging Technologies Influencing the Diagnosis and Care of Patients With Primary Brain Tumors

Over the past decade, a variety of new and innovative technologies has led to important advances in the diagnosis and management of patients with primary malignant brain tumors. New approaches to surgical navigation and tumor localization, advanced imaging to define tumor biology and treatment response, and the widespread adoption of a molecularly defined integrated diagnostic paradigm that complements traditional histopathologic diagnosis continue to impact the day-to-day care of these patients. In the neuro-oncology clinic, discussions with patients about the role of tumor treating fields (TTFields) and the incorporation of next-generation sequencing (NGS) data into therapeutic decision-making are now a standard practice. This article summarizes newer applications of technology influencing the pathologic, neuroimaging, neurosurgical, and medical management of patients with malignant primary brain tumors.

Higher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype

BACKGROUND AND PURPOSE

Genetic classifications are crucial for understanding the heterogeneity of glioblastoma. Recently, perfusion MRI techniques have demonstrated associations molecular alterations. In this work, we investigated whether perfusion markers within infiltrated peripheral edema were associated with proneural, mesenchymal, classical and neural subtypes.

MATERIALS AND METHODS

ONCOhabitats open web services were used to obtain the cerebral blood volume at the infiltrated peripheral edema for MRI studies of 50 glioblastoma patients from The Cancer Imaging Archive: TCGA-GBM. ANOVA and Kruskal-Wallis tests were carried out in order to assess the association between vascular features and the Verhaak subtypes. For assessing specific differences, Mann-Whitney U-test was conducted. Finally, the association of overall survival with molecular and vascular features was assessed using univariate and multivariate Cox models.

RESULTS

ANOVA and Kruskal-Wallis tests for the maximum cerebral blood volume at the infiltrated peripheral edema between the four subclasses yielded false discovery rate corrected p-values of <0.001 and 0.02, respectively. This vascular feature was significantly higher (p = 0.0043) in proneural patients compared to the rest of the subtypes while conducting Mann-Whitney U-test. The multivariate Cox model pointed to redundant information provided by vascular features at the peripheral edema and proneural subtype when analyzing overall survival.

CONCLUSIONS

Higher relative cerebral blood volume at infiltrated peripheral edema is associated with proneural glioblastoma subtype suggesting underlying vascular behavior related to molecular composition in that area.

Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high-grade gliomas

OBJECTIVES

To determine the performance of pretreatment structural and arterial spin labelling (ASL) MRI in predicting p53 mutation in patients with high-grade gliomas (HGGs).

METHODS

Pre-treatment structural and ASL MRI were performed in 57 patients with histologically confirmed HGGs and information of p53 status. Whole-lesion histogram analysis of cerebral blood flow (CBF) images of the enhancing tumour and the peritumoral oedema in the HGGs were performed. Visually AcceSAble Rembrandt Images features were used as qualitative analysis. The differences of ASL histogram parameters and Visually AcceSAble Rembrandt Images features between HGGs with or without p53 mutation were analyzed with post hoc correction for multiple comparisons. LASSO regression was performed to select the optimal features that could predict p53 mutation, followed by receiver operating characteristic analysis to determine the predictive efficacy.

RESULTS

A total of 33 HGGs with p53 mutation and 24 without p53 mutation were included. HGGs with mutant p53 showed lower CBF_percentile5 and CBF_uniformity of the enhancing tumour (p < 0.05) and higher prevalence of the qualitative MRI feature of enhancing tumour crossing midline (ETCM) (p < 0.05) as compared with HGGs with wild-type p53. LASSO regression showed that the CBF_uniformity of the enhancing tumour and ETCM were predictive features for p53 mutation. CBF_uniformity showed an acceptable performance in predicting p53 mutation (area under the curve = 0.721), when combined with the feature of ETCM, its predictive efficacy was significantly improved (area under the curve = 0.814, p = 0.012).

CONCLUSION

An integrated pre-treatment structural and ASL MRI can help to predict p53 mutation in HGGs.

Analysis of MRI Data in Diagnostic Neuroradiology

Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.

Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach

BACKGROUND AND PURPOSE

This study aims to investigate whether radiomic quantitative image features (IFs) from perfusion dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) retain sufficient strength to predict O6-methylguanine-DNA methyltransferase promoter methylation (MGMT_pm) in newly diagnosed glioblastoma (GB) patients.

METHODS

We retrospectively reviewed the perfusion DSC-MRI of 59 patients with GB. Patients were classified into three groups: (1) unmethylated if MGMT_pm ≤ 9% (UM); (2) intermediate-methylated if MGMT_pm ranged between 10% and 29% (IM); (3) methylated if MGMT_pm ≥ 30% (M). A total of 92 quantitative IFs were obtained from relative cerebral blood volume and relative cerebral blood flow maps. The Mann-Whitney U-test was applied to assess whether there were statistical differences in IFs between patient groups. Those IFs showing significant difference between two patient groups were termed relevant IFs (rIFs). rIFs were uploaded to a machine learning model to predict the MGMT_pm.

RESULTS

No rIFs were found between UM and IM groups. Fourteen rIFs were found among UM-M, IM-M, and (UM + IM)-M groups. We built a multilayer perceptron deep learning model that classified patients as belonging to UM + IM and M group. The model performed well with 75% sensitivity, 85% specificity, and an area under the receiver-operating curve of .84.

CONCLUSION

rIFs from perfusion DSC-MRI are potential biomarkers in GBs with a ≥30% MGMT_pm. Otherwise, unmethylated and intermediate-methylated GBs lack of rIFs. Five of 14 rIFs show sufficient strength to build an accurate prediction model of MGMT_pm.

Imaging of intratumoral heterogeneity in high-grade glioma

High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.

Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas

OBJECTIVES

Prediction of progression-free survival (PFS) and overall survival (OS) and early identification of molecular biomarkers with prognostic information are clinically important in glioblastoma (GBM) patients. We aimed to explore the utility of arterial spin labeling perfusion-weighted imaging (ASL-PWI) in the prediction of molecular biomarkers and survival in GBM patients.

METHODS

We retrospectively analyzed 149 consecutive GBM patients, who had undergone maximal surgical resection or biopsy followed by concurrent chemoradiotherapy and adjuvant chemotherapy using temozolomide between November 2010 and June 2016. On preoperative ASL-PWI, cerebral blood flow (CBF) within contrast-enhancing (CE) and nonenhancing (NE) portions were evaluated both qualitatively (perfusion pattern_[CE] and perfusion pattern_[NE]) and quantitatively (nCBF_CE and nCBF_NE). ASL-PWI findings were correlated with molecular biomarkers, including isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) methylation statuses, and survival, using the Mann-Whitney U-test, Spearman rank correlation, Kaplan-Meier analysis, and receiver operating characteristics analysis.

RESULTS

nCBF_CE was significantly higher in the IDH wild-type group than in the IDH mutant group (p = .013) and in the MGMT unmethylated group than in the methylated group (p = .047). Areas under the receiver operating characteristic curve were 0.678 for IDH mutation (p = .022) and 0.601 for MGMT promoter methylation (p = .043). Hyperperfusion was associated with the shortest median PFS for both perfusion pattern_[CE] (7.6 months) and perfusion pattern_[NE] (4.0 months). The perfusion pattern_[NE] remained an independent predictor for PFS and OS even after adjusting for clinical and molecular predictors, unlike perfusion pattern_[CE].

CONCLUSIONS

ASL-PWI can aid to predict survival and molecular biomarkers including IDH mutation and MGMT promoter methylation statuses in GBM patients.

KEY POINTS

• ASL-PWI can aid to predict survival in GBM patients. • ASL-PWI can aid to predict IDH and MGMT promoter methylation statuses in GBM.

Dynamic Contrast-Enhanced T1-Weighted Perfusion Magnetic Resonance Imaging Identifies Glioblastoma Immunohistochemical Biomarkers via Tumoral and Peritumoral Approach: A Pilot Study

OBJECTIVE

We aimed to evaluate the usefulness of dynamic contrast-enhanced T1-weighted perfusion magnetic resonance imaging (DCE-pMRI) to predict certain immunohistochemical (IHC) biomarkers of glioblastoma (GB) in this pilot study.

METHODS

We retrospectively reviewed 36 patients (male/female, 25:11; mean age, 53 years; age range, 29-85 years) who had pretreatment DCE-pMRI with IHC analysis of their excised GBs. Regions of interest of the enhancing tumor (ER) and nonenhancing peritumoral region (NER) were used to calculate DCE-pMRI parameters of volume transfer constant, back flux constant, volume of the extravascular extracellular space, initial area under enhancement curve, and maximum slope. IHC biomarkers including Ki-67 labeling index, epidermal growth factor receptor (EGFR), oligodendrocyte transcription factor 2 (OLIG2), isocitrate dehydrogenase 1 (IDH1), and p53 mutation status were determined. The imaging metrics of GB with IHC markers were compared using the Kruskal-Wallis test and Spearman correlation analysis.

RESULTS

Among 30 patients with available IDH1 status, 14 patients (46.6%) had IDH1 mutation. EGFR amplification was present in 24/36 (66.6%) patients. Mean Ki-67 labeling index was 29% (range, 1.5%-80%). p53 mutation was present in 20/36 GBs (55%), whereas OLIG2 expression was positive in 29/36 GBs (80.5%). Various DCE-pMRI parameters gathered from the ER and NER were significantly correlated with IDH1 mutation, EGFR amplification, and OLIG2 expression (P < 0.05). Ki-67 labeling index showed a strong positive correlation with initial area under enhancement curve (r = 0.619; P < 0.001).

CONCLUSIONS

DCE-pMRI could determine surrogate IHC biomarkers in GB via tumoral and peritumoral approach, potential targets for individualized treatment protocols.

Magnetic Resonance Imaging Parameters and Their Impact on Survival of Patients with Glioblastoma: Tumor Perfusion Predicts Survival

BACKGROUND

Many prognostic factors influence overall survival (OS) of patients with glioblastoma. Despite gross total resection and Stupp protocol adherence, many patients have poor survival. Perfusion magnetic resonance imaging may assist in diagnosis, treatment monitoring, and prognostication.

METHODS

This retrospective study of 36 patients with glioblastoma assessed influence of preoperative magnetic resonance imaging parameters reflecting tumor cell density and vascularity and patient age on OS.

RESULTS

The area under curve based on optimal receiver operating characteristic curves for the perfusion parameters normalized relative tumor blood volume (n_rTBV) and normalized relative tumor blood flow (n_rTBF) were 0.92 and 0.89, respectively, and the highest among all imaging parameters and age. OS showed strongly negative correlations with corrected n_rTBV (R = -0.70; P < 0.001) and n_rTBF (R = -0.67; P < 0.001). The Cox model, which included age and imaging parameters, demonstrated that n_rTBV and n_rTBF were most predictive of OS, with hazard ratios of 5.97 (P = 0.0001) and 8.76 (P = 0.0001), respectively, compared with 1.63 (P = 0.19) for age. Eighteen patients with corrected n_rTBV ≤2.5 (best cutoff value) had a median OS of 15.1 months (95% confidence interval (CI), 11.34-21.25) compared with 2.8 months (95% CI, 1.48-4.03; P < 0.001) for 18 patients with corrected n_rTBV >2.5. Twenty-four patients with n_rTBF ≤2.79 had a median OS of 12 months (95% CI, 10.46-17.9) compared with 2.8 months for 12 patients with n_rTBF >2.79 (95% CI, 1.31-4.2; P < 0.001).

CONCLUSIONS

The dominant predictors of OS are normalized perfusion parameters n_rTBV and n_rTBF. Preoperative perfusion imaging may be used as a surrogate to predict glioblastoma aggressiveness and survival independent of treatment.

Advances in Neuro-Oncology Imaging Techniques

Background

Central nervous system (CNS) tumors are a rare but devastating malignancy, often robbing patients of the basic quality of life. Despite advances in our understanding of the CNS tumor disease processes, the prognosis for patients with CNS tumors remains poor. Better characterization and diagnostic and monitoring approaches are necessary to assist in diagnosis and treatment of CNS tumors. One important tool in the neuro-oncology armamentarium is the use of advanced imaging techniques.

Methods

We searched PubMed using the keywords neuro-oncology imaging, pseudoprogression, molecular imaging, and biomarkers. We limited our search to full-text English articles and identified other relevant articles from the reference lists of previously identified articles.

Results

Advances in imaging techniques have allowed investigators to explore various imaging modalities, from tumor characterization to differentiating pseudoprogression from tumor progression. Better imaging can result in better diagnostic approaches, greater and safer resection techniques, and improved monitoring of tumor progression.

Conclusion

This review highlights advances in neuro-oncology imaging techniques and their clinical utility in the treatment and management of primary brain tumors.

Clinically Relevant Imaging Features for MGMT Promoter Methylation in Multiple Glioblastoma Studies: A Systematic Review and Meta-Analysis

BACKGROUND

O6-methylguanine methyltransferase (MGMT) promoter methylation status has been reported as a prognostic biomarker in clinical trials.

PURPOSE

Our aim was to systematically evaluate imaging features of MGMT promoter methylated glioblastoma and to determine the diagnostic performance of MR imaging for prediction of MGMT promoter methylation in patients with newly diagnosed glioblastoma.

DATA SOURCES

A computerized search of Ovid MEDLINE and EMBASE up to February 27, 2018, was conducted.

STUDY SELECTION

We selected studies evaluating imaging features of MGMT promoter methylated glioblastoma and the diagnostic performance of MR imaging for prediction of MGMT promoter methylation.

DATA ANALYSIS

Pooled estimates of sensitivity and specificity were calculated using a hierarchic logistic regression model. Meta-regression and sensitivity analysis were performed.

DATA SYNTHESIS

Twenty-two articles including 2199 patients were included. MGMT promoter methylated glioblastoma is likely to show less edema, high ADC, and low perfusion. Ten articles including 753 patients were included in the meta-analysis. The summary sensitivity was 79% (95% CI, 72%-85%), and the summary specificity was 78% (95% CI, 71%-84%). In the meta-regression, MGMT promoter methylation and mean age were associated with heterogeneity. Sensitivity analysis excluding 1 study resolved the heterogeneity.

LIMITATIONS

Included studies used a variety of different MR imaging techniques to predict MGMT promoter methylation.

CONCLUSIONS

MGMT promotor methylated glioblastoma is likely to show less aggressive imaging features than MGMT promotor unmethylated glioblastoma. Despite the variety of different MR imaging techniques used, MR imaging in patients with newly diagnosed glioblastoma was shown to have the potential to predict MGMT promoter methylation noninvasively.

Conventional and advanced magnetic resonance imaging in patients with high-grade glioma

Magnetic resonance imaging is integral to the care of patients with high-grade gliomas. Anatomic detail can be acquired with conventional structural imaging, but newer approaches also add capabilities to interrogate image-derived physiologic and molecular characteristics of central nervous system neoplasms. These advanced imaging techniques are increasingly employed to generate biomarkers that better reflect tumor burden and therapy response. The following is an overview of current strategies based on advanced magnetic resonance imaging that are used in the assessment of high-grade glioma patients with an emphasis on how novel imaging biomarkers can potentially advance patient care.

Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis

Background

The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients.

Methods

Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher’s exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables.

Results

MGMT promoter methylation was associated with tumor location and necrosis (P <  0.05). Significantly increased ADC value (P <  0.001) and decreased rCBF (P <  0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914.

Conclusion

ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.

Imaging Correlates of Adult Glioma Genotypes

Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. ^© RSNA, 2017.

Molecular differences between cerebral blood volume and vessel size in glioblastoma multiforme

The purpose of this study was to investigate the molecular background of cerebral blood volume (CBV) and vessel size (VS) of capillaries in glioblastoma multiforme (GBM). Both parameters are derived from extended perfusion MR imaging.A prospective case study including 21 patients (median age 66 years, 10 females) was performed. Before operation, CBV and VS of contrast enhancing tumor were assessed. Tissue was sampled from the assessed areas under neuronavigation control. After RNA extraction, transcriptional data was analyzed by Weighted Gene Co-Expression Network Analysis (WGCNA) and split into modules based on its network affiliations. Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the genetic modules. These were applied on 484 GBM samples of the TCGA database.Ten modules were highly correlated to CBV and VS. One module was exclusively associated to VS and highly correlated to hypoxia, another one exclusively to CBV showing strong enrichments in the Epithelial Growth Factor (EGF) pathway and Epithelial-to-Mesenchymal-Transition (EMT). Moreover, patients with increased CBV and VS predominantly showed a mesenchymal gene-expression, a finding that could be corroborated by TCGA data.In conclusion, CBV and VS mirror different genetic pathways and reflect certain molecular subclasses of GBM.

The Initial Area Under the Curve Derived from Dynamic Contrast-Enhanced MRI Improves Prognosis Prediction in Glioblastoma with Unmethylated MGMT Promoter

BACKGROUND AND PURPOSE

Although perfusion and permeability MR parameters have known to have prognostic value, they have reproducibility issues. Our aim was to evaluate whether the initial area under the time-to-signal intensity curve (IAUC) derived from dynamic contrast-enhanced MR imaging can improve prognosis prediction in patients with glioblastoma with known MGMT status.

MATERIALS AND METHODS

We retrospectively examined 88 patients with glioblastoma who underwent preoperative dynamic contrast-enhanced MR imaging. The means of IAUC values at 30 and 60 seconds (IAUC30_mean and IAUC60_mean) were extracted from enhancing tumors. The prognostic values of IAUC parameters for overall survival and progression-free survival were assessed with log-rank tests, according to the MGMT status. Multivariate overall survival and progression-free survival models before and after adding the IAUC parameters as covariates were explored by net reclassification improvement after receiver operating characteristic analysis for 1.5-year overall survival and 1-year progression-free survival and by random survival forest.

RESULTS

High IAUC parameters were associated with worse overall survival and progression-free survival in the unmethylated MGMT group, but not in the methylated group. In the unmethylated MGMT group, 1.5-year overall survival and 1-year progression-free survival prediction improved significantly after adding IAUC parameters (overall survival area under the receiver operating characteristic curve, 0.86; progression-free survival area under the receiver operating characteristic curve, 0.74-0.76) to the model with other prognostic factors (overall survival area under the receiver operating characteristic curve, 0.81; progression-free survival area under the receiver operating characteristic curve, 0.69; P < .05 for all) except in the case of IAUC60_mean for 1-year progression-free survival prediction (P = .059). Random survival forest models indicated that the IAUC parameters were the second or most important predictors in the unmethylated MGMT group, except in the case of the IAUC60_mean for progression-free survival.

CONCLUSIONS

IAUC can be a useful prognostic imaging biomarker in patients with glioblastoma with known MGMT status, improving prediction of glioblastoma prognosis with the unmethylated MGMT promoter status.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

BACKGROUND

Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.

METHODS

We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).

RESULTS

We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).

CONCLUSION

MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Integrative Analysis of mRNA, microRNA, and Protein Correlates of Relative Cerebral Blood Volume Values in GBM Reveals the Role for Modulators of Angiogenesis and Tumor Proliferation

Dynamic susceptibility contrast-enhanced magnetic resonance imaging is routinely used to provide hemodynamic assessment of brain tumors as a diagnostic as well as a prognostic tool. Recently, it was shown that the relative cerebral blood volume (rCBV), obtained from the contrast-enhancing as well as -nonenhancing portion of glioblastoma (GBM), is strongly associated with overall survival. In this study, we aim to characterize the genomic correlates (microRNA, messenger RNA, and protein) of this vascular parameter. This study aims to provide a comprehensive radiogenomic and radioproteomic characterization of the hemodynamic phenotype of GBM using publicly available imaging and genomic data from the Cancer Genome Atlas GBM cohort. Based on this analysis, we identified pathways associated with angiogenesis and tumor proliferation underlying this hemodynamic parameter in GBM.

Neuroimaging

Imaging is integral to the management of patients with brain tumors. Conventional structural imaging provides exquisite anatomic detail but remains limited in the evaluation of molecular characteristics of intracranial neoplasms. Quantitative and physiologic biomarkers derived from advanced imaging techniques have been increasingly utilized as problem-solving tools to identify glioma grade and assess response to therapy. This chapter provides a comprehensive overview of the imaging strategies used in the clinical assessment of patients with gliomas and describes how novel imaging biomarkers have the potential to improve patient management.

Modern brain tumor imaging

The imaging and clinical management of patients with brain tumor continue to evolve over time and now heavily rely on physiologic imaging in addition to high-resolution structural imaging. Imaging remains a powerful noninvasive tool to positively impact the management of patients with brain tumor. This article provides an overview of the current state-of-the art clinical brain tumor imaging. In this review, we discuss general magnetic resonance (MR) imaging methods and their application to the diagnosis of, treatment planning and navigation, and disease monitoring in patients with brain tumor. We review the strengths, limitations, and pitfalls of structural imaging, diffusion-weighted imaging techniques, MR spectroscopy, perfusion imaging, positron emission tomography/MR, and functional imaging. Overall this review provides a basis for understudying the role of modern imaging in the care of brain tumor patients.