Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma

Early identification of IDH mutation status is of great significance in clinical therapeutic decision-making in the treatment of glioma. We demonstrate a technological solution to improve the accuracy and reliability of IDH mutation detection by combining MRI-based prediction and a CRISPR-based automatic integrated gene detection system (AIGS). A model was constructed to predict the IDH mutation status using whole slices in MRI scans with a Transformer neural network, and the predictive model achieved accuracies of 0.93, 0.87, and 0.84 using the internal and two external test sets, respectively. Additionally, CRISPR/Cas12a-based AIGS was constructed, and AIGS achieved 100% diagnostic accuracy in terms of IDH detection using both frozen tissue and FFPE samples in one hour. Moreover, the feature attribution of our predictive model was assessed using GradCAM, and the highest correlations with tumor cell percentages in enhancing and IDH-wildtype gliomas were found to have GradCAM importance (0.65 and 0.5, respectively). This MRI-based predictive model could, therefore, guide biopsy for tumor-enriched, which would ensure the veracity and stability of the rapid detection results. The combination of our predictive model and AIGS improved the early determination of IDH mutation status in glioma patients. This combined system of MRI-based prediction and CRISPR/Cas12a-based detection can be used to guide biopsy, resection, and radiation for glioma patients to improve patient outcomes.

Quantifying the Growth of Glioblastoma Tumors Using Multimodal MRI Brain Images

Predicting the eventual volume of tumor cells, that might proliferate from a given tumor, can help in cancer early detection and medical procedure planning to prevent their migration to other organs. In this work, a new statistical framework is proposed using Bayesian techniques for detecting the eventual volume of cells expected to proliferate from a glioblastoma (GBM) tumor. Specifically, the tumor region was first extracted using a parallel image segmentation algorithm. Once the tumor region was determined, we were interested in the number of cells that could proliferate from this tumor until its survival time. For this, we constructed the posterior distribution of the tumor cell numbers based on the proposed likelihood function and a certain prior volume. Furthermore, we extended the detection model and conducted a Bayesian regression analysis by incorporating radiomic features to discover those non-tumor cells that remained undetected. The main focus of the study was to develop a time-independent prediction model that could reliably predict the ultimate volume a malignant tumor of the fourth-grade severity could attain and which could also determine if the incorporation of the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.

Assessment of brain cancer atlas maps with multimodal imaging features

BACKGROUND

Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity.

MAIN TEXT

Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers.

CONCLUSIONS

The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.

Brain tumour segmentation with incomplete imaging data

Progress in neuro-oncology is increasingly recognized to be obstructed by the marked heterogeneity-genetic, pathological, and clinical-of brain tumours. If the treatment susceptibilities and outcomes of individual patients differ widely, determined by the interactions of many multimodal characteristics, then large-scale, fully-inclusive, richly phenotyped data-including imaging-will be needed to predict them at the individual level. Such data can realistically be acquired only in the routine clinical stream, where its quality is inevitably degraded by the constraints of real-world clinical care. Although contemporary machine learning could theoretically provide a solution to this task, especially in the domain of imaging, its ability to cope with realistic, incomplete, low-quality data is yet to be determined. In the largest and most comprehensive study of its kind, applying state-of-the-art brain tumour segmentation models to large scale, multi-site MRI data of 1251 individuals, here we quantify the comparative fidelity of automated segmentation models drawn from MR data replicating the various levels of completeness observed in real life. We demonstrate that models trained on incomplete data can segment lesions very well, often equivalently to those trained on the full completement of images, exhibiting Dice coefficients of 0.907 (single sequence) to 0.945 (complete set) for whole tumours and 0.701 (single sequence) to 0.891 (complete set) for component tissue types. This finding opens the door both to the application of segmentation models to large-scale historical data, for the purpose of building treatment and outcome predictive models, and their application to real-world clinical care. We further ascertain that segmentation models can accurately detect enhancing tumour in the absence of contrast-enhancing imaging, quantifying the burden of enhancing tumour with an R 2 > 0.97, varying negligibly with lesion morphology. Such models can quantify enhancing tumour without the administration of intravenous contrast, inviting a revision of the notion of tumour enhancement if the same information can be extracted without contrast-enhanced imaging. Our analysis includes validation on a heterogeneous, real-world 50 patient sample of brain tumour imaging acquired over the last 15 years at our tertiary centre, demonstrating maintained accuracy even on non-isotropic MRI acquisitions, or even on complex post-operative imaging with tumour recurrence. This work substantially extends the translational opportunity for quantitative analysis to clinical situations where the full complement of sequences is not available and potentially enables the characterization of contrast-enhanced regions where contrast administration is infeasible or undesirable.

Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology

Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods

Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.

A brain tumor reporting and data system to optimize imaging surveillance and prognostication in high-grade gliomas

BACKGROUND AND PURPOSE

High-grade glioma (HGG), including glioblastoma, is the most common primary brain neoplasm and has a dismal prognosis. After initial treatment, follow-up decisions are guided by longitudinal MRI performed at routine intervals. The Brain Tumor Reporting and Data System (BT-RADS) is a proposed structured reporting system for posttreatment brain MRIs. The purpose of this study is to determine the relationship between BT-RADS scores and overall survival in HGG patients.

METHODS

Chart review of grade 4 glioma patients who had an MRI at a single institution from November 2018 to November 2019 was performed. BT-RADS scores, tumor characteristics, and overall survival were recorded. Likelihood of improvement, stability, or worsening on the subsequent study was calculated for each score. Survival analysis was performed using Kaplan-Meier method, log-rank test, and a time-dependent cox model. Significance level of .05 was used.

RESULTS

The study identified 91 HGG patients who underwent a total of 538 MRIs. Mean age of patients was 57 years old. Score with the highest likelihood for worsening on the next follow-up was 3b. The risk of death was 53% higher with each incremental increase in BT-RADS scores (hazard ratio, 1.53; 95% confidence interval [CI], 1.07-2.19; p = .019). The risk of death was 167% higher in O-6-methylguanine-DNA-methyltransferase unmethylated tumors (hazard ratio, 2.67; 95% CI, 1.34-5.33; p = .005).

CONCLUSIONS

BT-RADS scores can be used as a reference guide to anticipate whether patients' subsequent MRI will be improved, stable, or worsened. The scoring system can also be used to predict clinical outcomes and prognosis.

Integrated MRI–Immune–Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma

Simple Summary

Despite crucial scientific advances, Glioblastoma (GB) remains a fatal disease with limited therapeutic options and a lack of suitable biomarkers. The unveiled competence of the brain immune system together with the breakthrough advent of immunotherapy has shifted the present translational research on GB towards an immune-focused perspective. Several clinical trials targeting the immunosuppressive GB background are ongoing. So far, results are inconclusive, underpinning our partial understanding of the complex cancer-immune interplay in brain tumors. High throughput Magnetic Resonance (MR) imaging has shown the potential to decipher GB heterogeneity, including pathologic and genomic clues. However, whether distinct GB immune contextures can be deciphered at an imaging scale is still elusive, leaving unattained the non-invasive achievement of prognostic and predictive biomarkers. Along these lines, we integrated genetic, immunopathologic and imaging features in a series of GB patients. Our results suggest that multiparametric approaches might offer new efficient risk stratification models, opening the possibility to intercept the critical events implicated in the dismal prognosis of GB.

Abstract

Background: The aim of the present study was to dissect the clinical outcome of GB patients through the integration of molecular, immunophenotypic and MR imaging features. Methods: We enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI sequences were used to perform a manual evaluation of multiple quantitative variables, among which we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS (median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged as the most significant prognostic factor (median OS = 23 months in V-CD4^high vs. 13 months in V-CD4^low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMT^meth GB, while p53^mut appeared to condition a desert immune background. When individual genetic (MGMT^unmeth), MR imaging (mean ADC^low) and TIME (V-CD4+TILs^low) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI 7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion: Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models in GB patients.

Standard clinical approaches and emerging modalities for glioblastoma imaging

Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters Ktrans, ve, Kep, vp, τi and Fp. There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between ve and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on Ktrans, Fp and ve as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

T2/FLAIR Abnormity Could be the Sign of Glioblastoma Dissemination

Purpose

Newly emerged or constantly enlarged contrast-enhancing (CE) lesions were the necessary signs for the diagnosis of glioblastoma (GBM) progression. This study aimed to investigate whether the T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) abnormal transformation could predict and assess progression for GBMs, especially for tumor dissemination.

Methods

A consecutive cohort of 246 GBM patients with regular follow-up and sufficient radiological data was included in this study. The series of T2/FLAIR and T1CE images were retrospectively reviewed. The patients were separated into T2/FLAIR and T1CE discordant and accordant subgroups based on the initial progression images.

Results

A total of 170 qualified patients were finally analyzed. The incidence of discordant T2/FLAIR and T1CE images was 25.9% (44/170). The median time-span of T2/FLAIR indicated tumor progression was 119.5 days (ranging from 57 days-unreached) prior to T1CE. Nearly half of patients (20/44, 45.5%) in the discordant subgroup suffered from tumor dissemination, substantially higher than accordant patients (23/126, 20.6%, p < 0.001). The median time to progression (TTP), post-progression survival (PPS), and overall survival (OS) were not statistically different (all p > 0.05) between discordant and accordant patients.

Conclusions

T2/FLAIR abnormity could be the sign of GBM progression, especially for newly emerged lesions disseminating from the primary cavity. Physicians should cast more attention on the dynamic change of T2/FLAIR images, which might be of great significance for progression assessment and subsequent clinical decision-making.

Predicting Glioblastoma Cellular Motility from In Vivo MRI with a Radiomics Based Regression Model

Simple Summary

A diagnosis of glioblastoma carries a uniformly dismal prognosis. Contributing to this is the near certain chance of aggressive tumor spread and recurrence following treatment. Tumor cell motility may provide one way to characterize the tendencies of glioblastomas to spread and recur. We sought to develop a non-invasive technique for assessing tumor cell motility using quantitative features derived from in vivo preoperative magnetic resonance imaging. Our regression model accurately predicted tumor cell motility in a cohort of participants with preoperative imaging who also had mean cellular motility calculated for their resected tumor cells from time-lapse videos. This work establishes the feasibility of non-invasively characterizing the kinetic properties of tumors and could be used to select patients for motility-targeting precision therapies.

Abstract

Characterizing the motile properties of glioblastoma tumor cells could provide a useful way to predict the spread of tumors and to tailor the therapeutic approach. Radiomics has emerged as a diagnostic tool in the classification of tumor grade, stage, and prognosis. The purpose of this work is to examine the potential of radiomics to predict the motility of glioblastoma cells. Tissue specimens were obtained from 31 patients undergoing surgical resection of glioblastoma. Mean tumor cell motility was calculated from time-lapse videos of specimen cells. Manual segmentation was used to define the border of the enhancing tumor T1-weighted MR images, and 107 radiomics features were extracted from the normalized image volumes. Model parameter coefficients were estimated using the adaptive lasso technique validated with leave-one-out cross validation (LOOCV) and permutation tests. The R-squared value for the predictive model was 0.60 with p-values for each individual parameter estimate less than 0.0001. Permutation test models trained with scrambled motility failed to produce a model that out-performed the model trained on the true data. The results of this work suggest that it is possible for a quantitative MRI feature-based regression model to non-invasively predict the cellular motility of glioblastomas.

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Simple Summary

Radiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the role of radiomics in providing more accurate diagnoses, prognostication, and surveillance of patients with high-grade glioma, and on the potential application of radiomics in clinical practice, with the overarching goal of advancing precision medicine for optimal patient care.

Abstract

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.

AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?

Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers to predict clinically relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis, and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics and selected through Boruta algorithm. A Grid Search algorithm was applied when computing ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.

BRCAness as a Biomarker of Susceptibility to PARP Inhibitors in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is the most common primary brain cancer. GBMs commonly acquire resistance to standard-of-care therapies. Among the novel means to sensitize GBM to DNA-damaging therapies, a promising strategy is to combine them with inhibitors of the DNA damage repair (DDR) machinery, such as inhibitors for poly(ADP-ribose) polymerase (PARP). PARP inhibitors (PARPis) have already shown efficacy and have received regulatory approval for breast, ovarian, prostate, and pancreatic cancer treatment. In these cancer types, after PARPi administration, patients carrying specific mutations in the breast cancer 1 (BRCA1) and 2 (BRCA2) suppressor genes have shown better response when compared to wild-type carriers. Mutated BRCA genes are infrequent in GBM tumors, but their cells can carry other genetic alterations that lead to the same phenotype collectively referred to as 'BRCAness'. The most promising biomarkers of BRCAness in GBM are related to isocitrate dehydrogenases 1 and 2 (IDH1/2), epidermal growth factor receptor (EGFR), phosphatase and tensin homolog (PTEN), MYC proto-oncogene, and estrogen receptors beta (ERβ). BRCAness status identified by accurate biomarkers can ultimately predict responsiveness to PARPi therapy, thereby allowing patient selection for personalized treatment. This review discusses potential biomarkers of BRCAness for a 'precision medicine' of GBM patients.

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Simple Summary

Radiogenomics enables prediction of the status and prognosis of patients using non-invasively obtained imaging data. Current machine learning (ML) methods used in radiogenomics require huge datasets, which involve the handling of large heterogeneous datasets from multiple cohorts/hospitals. In this study, two different glioma datasets were used to test various ML and image pre-processing methods to confirm whether the models trained on one dataset are universally applicable to other datasets. Our result suggested that the ML method that yielded the highest accuracy in a single dataset was likely to be overfitted. We demonstrated that implementation of standardization and dimension reduction procedures prior to classification, enabled the development of ML methods that are less affected by the multiple cohort difference. We advocate using caution in interpreting the results of radiogenomic studies of the training and testing datasets that are small or mixed, with a view to implementing practical ML methods in radiogenomics.

Abstract

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

A Voxel Model to Decipher the Role of Molecular Communication in the Growth of Glioblastoma Multiforme

Glioblastoma Multiforme (GBM), the most malignant human tumour, can be defined by the evolution of growing bio-nanomachine networks within an interplay between self-renewal (Grow) and invasion (Go) potential of mutually exclusive phenotypes of transmitter and receiver cells. Herein, we present a mathematical model for the growth of GBM tumour driven by molecule-mediated inter-cellular communication between two populations of evolutionary bio-nanomachines representing the Glioma Stem Cells (GSCs) and Glioma Cells (GCs). The contribution of each subpopulation to tumour growth is quantified by a voxel model representing the end to end inter-cellular communication models for GSCs and progressively evolving invasiveness levels of glioma cells within a network of diverse cell configurations. Mutual information, information propagation speed and the impact of cell numbers and phenotypes on the communication output and GBM growth are studied by using analysis from information theory. The numerical simulations show that the progression of GBM is directly related to higher mutual information and higher input information flow of molecules between the GSCs and GCs, resulting in an increased tumour growth rate. These fundamental findings contribute to deciphering the mechanisms of tumour growth and are expected to provide new knowledge towards the development of future bio-nanomachine-based therapeutic approaches for GBM.

Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma

PURPOSE

We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

METHODS

This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

RESULTS

Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

CONCLUSIONS

MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.

MRI Imaging Characteristics of Glioblastoma with Concurrent Gain of Chromosomes 19 and 20

Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Some of the genetic variations identified thus far, such as IDH mutation and MGMT promotor methylation, have implications for survival and response to therapy. A recent analysis of long-term GBM survivors showed that concurrent gain of chromosomes 19 and 20 (19/20 co-gain) is a positive prognostic factor that is independent of IDH mutation status. In this study, we retrospectively identified 18 patients with 19/20 co-gain and compared their imaging features to a control cohort without 19/20 co-gain. Imaging features such as tumor location, size, pial invasion, and ependymal extension were examined manually. When compared without further genetic subclassification, both groups showed similar imaging features except for rates of pial invasion. When each group was subclassified by MGMT promotor methylation status however, the two groups showed different imaging features in a number of additional ways including tumor location, size, and ependymal extension. Our results indicate that different permutations of various genetic mutations that coexist in GBM may interact in unpredictable ways to affect imaging appearance, and that imaging prognostication may be better approached in the context of the global genomic profile rather than individual genetic alterations.

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

Aptamer Generated by Cell-SELEX for Specific Targeting of Human Glioma Cells

The most prevalent primary brain tumors are gliomas, which start in the glial cells. Although there have been significant technological advances in surgery and radio-chemotherapy, the prognosis and survival of patients with malignant gliomas remain poor. For routine diagnosis of glioma, computed tomography and magnetic resonance imaging primarily depend on anatomical changes and fail to detect the cellular changes that occur early in the development of malignant gliomas. Therefore, it is urgent to find effective molecular diagnostic tools to detect early stages of malignant gliomas. Currently, cell-based Systematic Evolution of Ligands by EXponential enrichment (cell-SELEX) technology is one effective tool to obtain DNA or RNA aptamers capable of differentiating the molecular signatures among different types of cell lines. Using cell-SELEX, we generated and characterized an aptamer, termed S6-1b, that can distinguish the molecular differences between glioma cell line SHG44 and human astrocytes. Under the conditions of 4 and 37 °C, respectively, the dissociation constants of aptamer-cell interaction were both measured in the low nanomolar range. The aptamer S6-1b also exhibited excellent selectivity, making it suitable for use in a complex biological environment. Furthermore, the aptamer can effectively target glioma cells for in vivo fluorescence imaging of tumors. The target type of aptamer S6-1b was identified as a cell membrane protein. Our work indicates that aptamer S6-1b has diagnostic and therapeutic potential to specifically deliver imaging or therapeutic agents to malignant gliomas.

Imaging Beyond Seeing: Early Prognosis of Cancer Treatment

Assessing cancer response to therapeutic interventions has been realized as an important course to early predict curative efficacy and treatment outcomes due to tumor heterogeneity. Compared to the traditional invasive tissue biopsy method, molecular imaging techniques have fundamentally revolutionized the ability to evaluate cancer response in a spatiotemporal manner. The past few years has witnessed a paradigm shift on the efforts from manufacturing functional molecular imaging probes for seeing a tumor to a vantage stage of interpreting the tumor response during different treatments. This review is to stand by the current development of advanced imaging technologies aiming to predict the treatment response in cancer therapy. Special interest is placed on the systems that are able to provide rapid and noninvasive assessment of pharmacokinetic drug fates (e.g., drug distribution, release, and activation) and tumor microenvironment heterogeneity (e.g., tumor cells, macrophages, dendritic cells (DCs), T cells, and inflammatory cells). The current status, practical significance, and future challenges of the emerging artificial intelligence (AI) technology and machine learning in the applications of medical imaging fields is overviewed. Ultimately, the authors hope that this review is timely to spur research interest in molecular imaging and precision medicine.

Epigenetics of glioblastoma multiforme: From molecular mechanisms to therapeutic approaches

Glioblastoma multiforme (GBM) is the most common form of brain cancer and one of the most aggressive cancers found in humans. Most of the signs and symptoms of GBM can be mild and slowly aggravated, although other symptoms might demonstrate it as an acute ailment. However, the precise mechanisms of the development of GBM remain unknown. Due to the improvement of molecular pathology, current researches have reported that glioma progression is strongly connected with different types of epigenetic phenomena, such as histone modifications, DNA methylation, chromatin remodeling, and aberrant microRNA. Furthermore, the genes and the proteins that control these alterations have become novel targets for treating glioma because of the reversibility of epigenetic modifications. In some cases, gene mutations including P16, TP53, and EGFR, have been observed in GBM. In contrast, monosomies, including removals of chromosome 10, particularly q23 and q25-26, are considered the standard markers for determining the development and aggressiveness of GBM. Recently, amid the epigenetic therapies, histone deacetylase inhibitors (HDACIs) and DNA methyltransferase inhibitors have been used for treating tumors, either single or combined. Specifically, HDACIs are served as a good choice and deliver a novel pathway to treat GBM. In this review, we focus on the epigenetics of GBM and the consequence of its mutations. We also highlight various treatment approaches, namely gene editing, epigenetic drugs, and microRNAs to combat GBM.

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma

Purpose

There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network “Glioma MR Imaging 2.0” (GliMR) which we present in this article.

Methods

GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current “state-of-the-art” in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019.

Results

GliMR’s first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website.

Conclusion

The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.

Depressive and anxiety disorders worsen the prognosis of glioblastoma

Glioblastoma multiforme (GBM) is one of the most malignant tumors. Depressive and anxiety disorders may co-exist with GBM. We investigated whether depression and anxiety influenced the outcomes of GBM. The Patient Health Questionnaire 9-item (PHQ-9) and Generalized Anxiety Disorder 7-item (GAD-7) scales were used to investigate the mental condition of GBM patients in our department, and the overall survival times of these patients were monitored. The scores on both scales were higher in GBM patients than in healthy controls. For each scale, GBM patients were divided into high- and low-score groups based on the average score. The prognosis was poorer for GBM patients in the high-score groups than for those in the low-score groups. Moreover, magnetic resonance imaging revealed that tumor necrosis was more prevalent among high-scored GBM patients. Cellular experiments were performed on primary GBM cells from patients with either high or low scores on both scales. Sphere formation, EdU and wound healing assays revealed greater proliferation and invasion capacities in GBM cells from patients with high scores on both scales. Western blotting assay revealed significantly different expression of epithelial and mesenchymal markers between the two groups. Thus, our analysis revealed a clinically important correlation between depression/anxiety and GBM prognosis.

Aging-related tumor associated fibroblasts changes could worsen the prognosis of GBM patients

Background

Glioblastoma multiforme (GBM) is the most malignant tumor in human brain, with highly heterogeneity among different patients. Age could function as an incidence and prognosis risk factor for many tumors.

Method

A series of bioinformatic experiments were conducted to evaluate the differences of incidence, differential expressed genes, enriched pathways with the data from Surveillance, Epidemiology, and End Results (SEER) program, the cancer genome atlas (TCGA) and Chinese glioma genome atlas (CGGA) project.

Results

We discovered in our present study that distinct difference of incidence and prognosis of different aged GBM patients. By a series of bioinformatic method, we found that the tumor associated fibroblasts (TAFs) was the most crucial tumor microenvironment (TME) component that led to this phenomenon. Epithelial-mesenchymal transition (EMT) could be the mechanism by which TAFs regulate the progression of GBM.

Conclusion

We have proposed a close correlation between age and GBM incidence and prognosis, and propose the underlying mechanism behind this correlation by mining different databases, which laid the foundation for future research.

Deep Learning AI Applications in the Imaging of Glioma

This manuscript will review emerging applications of artificial intelligence, specifically deep learning, and its application to glioblastoma multiforme (GBM), the most common primary malignant brain tumor. Current deep learning approaches, commonly convolutional neural networks (CNNs), that take input data from MR images to grade gliomas (high grade from low grade) and predict overall survival will be shown. There will be more in-depth review of recent articles that have applied different CNNs to predict the genetics of glioma on pre-operative MR images, specifically 1p19q codeletion, MGMT promoter, and IDH mutations, which are important criteria for the diagnosis, treatment management, and prognostication of patients with GBM. Finally, there will be a brief mention of current challenges with DL techniques and their application to image analysis in GBM.

Emerging Techniques in Imaging of Glioma Microenvironment

Magnetic resonance imaging (MRI) has been the cornerstone of imaging of brain tumors in the past 4 decades. Conventional MRI remains the workhorse for neuro-oncologic imaging, not only for basic information such as location, extent, and navigation but also able to provide information regarding proliferation and infiltration, angiogenesis, hemorrhage, and more. More sophisticated MRI sequences have extended the ability to assess and quantify these features; for example, permeability and perfusion acquisitions can assess blood-brain barrier disruption and angiogenesis, diffusion techniques can assess cellularity and infiltration, and spectroscopy can address metabolism. Techniques such as fMRI and diffusion fiber tracking can be helpful in diagnostic planning for resection and radiation therapy, and more sophisticated iterations of these techniques can extend our understanding of neurocognitive effects of these tumors and associated treatment responses and effects. More recently, MRI has been used to go beyond such morphological, physiological, and functional characteristics to assess the tumor microenvironment. The current review highlights multiple recent and emerging approaches in MRI to characterize the tumor microenvironment.

Multiparameter quantitative histological MRI values in high-grade gliomas: a potential biomarker of tumor progression

Background

Conventional MRI poorly distinguishes brain parenchyma microscopically invaded by high-grade gliomas (HGGs) from the normal brain. By contrast, quantitative histological MRI (hMRI) measures brain microstructure in terms of physical MR parameters influenced by histochemical tissue composition. We aimed to determine the relationship between hMRI parameters in the area surrounding the surgical cavity and the presence of HGG recurrence.

Methods

Patients were scanned after surgery with an hMRI multiparameter protocol that allowed for estimations of longitudinal relaxation rate (R1) = 1/T1, effective transverse relaxation rate (R2)*=1/T2*, magnetization transfer saturation (MT_sat), and proton density. The initial perioperative zone (IPZ) was segmented on the postoperative MRI. Once recurrence appeared on conventional MRI, the area of relapsing disease was delineated (extension zone, EZ). Conventional MRI showing recurrence and hMRI were coregistered, allowing for the extraction of parameters R1, R2*, MT_sat, and PD in 3 areas: the overlap area between the IPZ and EZ (OZ), the peritumoral brain zone, PBZ (PBZ = IPZ - OZ), and the area of recurrence (RZ = EZ - OZ).

Results

Thirty-one patients with HGG who underwent gross-total resection were enrolled. MT_sat and R1 were the most strongly associated with tumor progression. MT_sat was significantly lower in the OZ and RZ, compared to PBZ. R1 was significantly lower in RZ compared to PBZ. PD was significantly higher in OZ compared to PBZ, and R2* was higher in OZ compared to PBZ or RZ. These changes were detected 4 to 120 weeks before recurrence recognition on conventional MRI.

Conclusions

HGG recurrence was associated with hMRI parameters' variation after initial surgery, weeks to months before overt recurrence.

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas

OBJECTIVE

The current study aimed to evaluate the clinical practice for hemodynamic tissue signature (HTS) method in IDH genotype prediction in three groups derived from high-grade gliomas.

METHODS

Preoperative MRI examinations of 44 patients with known grade and IDH genotype were assigned into three study groups: glioblastoma multiforme, grade III, and high-grade gliomas. Perfusion parameters were analyzed and were used to automatically draw the four reproducible habitats (high-angiogenic enhancing tumor habitats, low-angiogenic enhancing tumor habitats, infiltrated peripheral edema habitats, vasogenic peripheral edema habitats) related to vascular heterogeneity. These four habitats were then compared between inter-patient with IDH mutation and their wild-type counterparts at these three groups, respectively. The discriminating potential for HTS in assessing IDH mutation status prediction was assessed by ROC curves.

RESULTS

Compared with IDH wild type, IDH mutation had significantly decreased relative cerebral blood volume (rCBV) at the high-angiogenic enhancing tumor habitats and low-angiogenic enhancing tumor habitats. ROC analysis revealed that the rCBVs in habitats had great ability to discriminate IDH mutation from their wild type in all groups. In addition, the Kaplan-Meier survival analysis yielded significant differences for the survival times observed from the populations dichotomized by low (< 4.31) and high (> 4.31) rCBV in the low-angiogenic enhancing tumor habitat.

CONCLUSIONS

The HTS method has been proven to have high prediction capabilities for IDH mutation status in high-grade glioma patients, providing a set of quantifiable habitats associated with tumor vascular heterogeneity.

KEY POINTS

• The HTS method has a high accuracy for molecular stratification prediction for all subsets of HGG. • The HTS method can give IDH mutation-related hemodynamic information of tumor-infiltrated and vasogenic edema. • IDH-relevant rCBV difference in habitats will be a great prognosis factor in HGG.

Extracellular Vesicles Involvement in the Modulation of the Glioblastoma Environment

Glioblastoma (GBM) is the most deadly primary brain tumour and is a paradigmatic example of heterogeneous cancer. Although expanding data propose the phenotypic plasticity exhibited by glioblastoma cells, as a critical feature involved in the tumour development and posttherapy recurrence, the central machinery responsible for their aggressiveness remains elusive. Despite decades of research, the complex biology of the glioblastoma is still unknown. Progress in genetic and epigenetic discoveries has improved diagnostic classification, prognostic information, and therapeutic planning. In the complex model of intercellular signalling, several studies have shown that extracellular vesicles have a key role in the intercellular communication among GBM cells and the tumour microenvironment modulation. The purpose of this review is to summarize the role of the EV-mediated intercellular crosstalk in the glioblastoma physiopathology.

Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype

Glioblastoma (GBM) is the most common and deadly malignant brain tumor with short yet varied overall survival (OS) time. Per request of personalized treatment, accurate pre-operative prognosis for GBM patients is highly desired. Currently, many machine learning-based studies have been conducted to predict OS time based on pre-operative multimodal MR images of brain tumor patients. However, tumor genotype, such as MGMT and IDH, which has been proven to have strong relationship with OS, is completely not considered in pre-operative prognosis as the genotype information is unavailable until craniotomy. In this paper, we propose a new deep learning based method for OS time prediction. It can derive genotype related features from pre-operative multimodal MR images of brain tumor patients to guide OS time prediction. Particularly, we propose a multi-task convolutional neural network (CNN) to accomplish tumor genotype and OS time prediction tasks. As the network can benefit from learning genotype related features toward genotype prediction, we verify upon a dataset of 120 GBM patients and conclude that the multi-task learning can effectively improve the accuracy of predicting OS time in personalized prognosis.

Dissecting and rebuilding the glioblastoma microenvironment with engineered materials

Glioblastoma (GBM) is the most aggressive and common form of primary brain cancer. Several decades of research have provided great insight into GBM progression; however, the prognosis remains poor with a median patient survival time of ~ 15 months. The tumour microenvironment (TME) of GBM plays a crucial role in mediating tumour progression and thus is being explored as a therapeutic target. Progress in the development of treatments targeting the TME is currently limited by a lack of model systems that can accurately recreate the distinct extracellular matrix composition and anatomic features of the brain, such as the blood-brain barrier and axonal tracts. Biomaterials can be applied to develop synthetic models of the GBM TME to mimic physiological and pathophysiological features of the brain, including cellular and ECM composition, mechanical properties, and topography. In this Review, we summarize key features of the GBM microenvironment and discuss different strategies for the engineering of GBM TME models, including 2D and 3D models featuring chemical and mechanical gradients, interfaces and fluid flow. Finally, we highlight the potential of engineered TME models as platforms for mechanistic discovery and drug screening as well as preclinical testing and precision medicine.

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging.

Analysis of peritumoral hyperintensity on pre-operative T2-weighted MR images in glioblastoma: Additive prognostic value of Minkowski functionals

Objectives

The extent of peritumoral tumor cell infiltrations in glioblastoma contributes to poor prognosis. We aimed to assess additive prognostic value of Minkowski functionals in analyzing heterogeneity of peritumoral hyperintensity on T2WI in glioblastoma patients.

Methods

Clinical data (age, sex, extent of surgical resection), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and pre-operative T2WI of 113 pathologically confirmed glioblastoma patients (from our institution, n = 61; from the Cancer Imaging Archive, n = 52) were retrospectively reviewed. The patients were randomly grouped into a training set (n = 80) and a test set (n = 33). Peritumoral T2 hyperintensity was manually segmented and Minkowski functionals—a texture analysis method capturing heterogeneity of MR images—were computed as a function of 11 grayscale thresholds. The Cox proportional hazards models were fitted with clinical variables, Minkowski functionals features as well as both combined. The risk prediction performances of the Minkowski functionals and combined models were validated on a separate test dataset. The sex-specific survival difference of the entire cohort was analyzed according to MGMT methylation status via Kaplan-Meier survival curves.

Results

Thirty-three Minkowski features (11 area, 11 perimeter and 11 genus) for each patient were acquired giving a total of 3729 features. Cox regression models fitted with clinical data, Minkowski features, and both combined had incremental concordance indices of 0.577 (P = 0.02), 0.706 (P = 0.02) and 0.714 (P = 0.01) respectively. The prediction error rate of the combined model—having clinical and Minkowski features—was lower than that of Minkowski functionals model (0.135 and 0.161, respectively) when validated on a test dataset. No sex-specific survival difference was found according to MGMT methylation status (male, P = 0.2; female, P = 0.22).

Conclusions

Minkowski functionals features computed from peritumoral hyperintensity can capture heterogeneity of glioblastoma on T2WI and have additive prognostic value in predicting survival, demonstrating their potential in complementing currently available prognostic parameters.

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

What Neuroradiologists Need to Know About Radiation Treatment for Neural Tumors

Radiation oncologists and radiologists have a unique and mutually dependent relationship. Radiation oncologists rely on diagnostic imaging to locate the tumor and define the treatment target volume, evaluation of response to therapy, and follow-up. Accurate interpretation of post-treatment imaging requires diagnostic radiologists to have a basic understanding of radiation treatment planning and delivery. There are various radiation treatment modalities such as 3D conformal radiation therapy, intensity modulated radiation therapy and stereotactic radiosurgery as well as different radiation modalities such as photons and protons that can be used for treatment. All of these have subtle differences in how the treatment is planned and how the imaging findings might be affected. This paper provides an overview of the basic principles of radiation oncology, different radiation treatment modalities, how radiation therapy is planned and delivered, how knowledge of this process can help interpretation of images, and how the radiologist can contribute to this process.

Sequencing the next generation of glioblastomas

The most aggressive brain malignancy, glioblastoma, accounts for 60-70% of all gliomas and is uniformly fatal. According to the molecular signature, glioblastoma is divided into four subtypes (proneural, neural, classical, and mesenchymal), each with its own genetic background. The Cancer Genome Atlas project provides information about the most common genetic changes in glioblastoma. They involve mutations in TP53, TERT, and PTEN, and amplifications in EFGR, PDGFRA, CDK4, CDK6, MDM2, and MDM4. Recently, epigenetics was used to demonstrate the oncogenic roles of miR-124, miR-137, and miR-128. The most important findings so far are mutations in IDH1/2 and MGMT promoter methylation, which are routinely used as predictive biomarkers in patient care. Current clinical treatment leaves patients with only a 10% chance for 5-year survival. Attempts to define the mutational profile of glioblastoma to identify clinically relevant changes have not yet yielded significant results. This can be attributed to inter- and intra-tumor heterogeneity that is present in most glioblastomas, as well as hypermutation that appears as a consequence of chemotherapy. The evolving field of radiogenomics aims to classify glioblastoma using a combination of magnetic resonance imaging and genomic information. In the era of genomic medicine, next-generation sequencing is extensively used in glioblastoma research because it can detect multiple changes in a single biological sample; its potential in detecting circulating cell-free DNA has been tested in cerebrospinal fluid and plasma, and it shows promise in the examination of the cellular content of extracellular vesicles as a potential source of biomarkers. Next-generation sequencing is making its way into glioblastoma diagnostics. Gene panels like GlioSeq, which includes the most commonly mutated genes, are currently being tested on snap frozen and formalin fixed paraffin embedded tissues. This new methodology is helping to define the "next generation of glioblastomas" - clinically defined and better understood, with greater potential to improve patient care. However, limitations of the necessary infrastructure, space for data storage, technical expertise, and data ownership need to be considered carefully.