Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures

Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma

PURPOSE

Analyzing post-treatment MRIs from glioblastoma patients can be challenging due to similar radiological presentations of disease progression and treatment effects. Identifying radiomics features (RFs) revealing progressive glioblastoma can contribute to an improved evaluation of the response assessment.

METHODS

3 Tesla MRI data from 560 glioblastoma patients (mean age 58.1 years) after treatment according to Stupp's protocol were analyzed retrospectively. A total of 418 RFs were extracted from contrast-enhancing tumors, non-enhancing lesions, peritumoral regions (PeriCET) and normal-appearing white matter as regions of interest using PyRadiomics. Dataset was initially split into a training (70%) and a validation (30%) cohort. The training cohort was used for feature selection and model-optimization. Logistic regression was used as a machine-learning model to identify patients with progression-free survival (PFS) as defined by the RANO criteria at 6 and 12 months after treatment. Models were trained with (i) clinical features only, (ii) RFs only, and (iii) a combination of clinical and radiomics features. The performance of each model was evaluated with the validation cohort.

RESULTS

The predictive performances of the model trained with only RFs from the PeriCET were AUC = 0.61 (95%-CI: 0.51-0.70) and AUC = 0.71 (95%-CI: 0.61-0.81) for 6-months and 12-months PFS respectively. Combining clinical and RFs from PeriCET resulted in overall best performance in predicting patients with progression within 12-months AUC = 0.75 (95%-CI: 0.65-0.85).

CONCLUSION

RFs from peritumoral region combined with clinical features including age, sex, and MGMT status can identify patients with 12-months PFS, suggesting the important role of peritumoral regions for the progression of glioblastoma.

The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers

BACKGROUND

Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.

METHODS

We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity.

RESULTS

Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas.

CONCLUSIONS

This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.

Intrinsic Gene Expression Correlates of the Biophysically Modeled Diffusion Magnetic Resonance Imaging Signal

Magnetic resonance imaging (MRI) is a powerful tool to identify the structural and functional correlates of neurological illness but provides limited insight into molecular neurobiology. Using rat genetic models of autism spectrum disorder, we show that image texture-processed neurite orientation dispersion and density imaging (NODDI) diffusion MRI possesses an intrinsic relationship with gene expression that corresponds to the biophysically modeled cellular compartments of the NODDI diffusion signal. Specifically, we demonstrate that neurite density index and orientation dispersion index signals are correlated with intracellular and extracellular gene expression, respectively. Moreover, we further demonstrate that these imaging signals correlate with genes specifically relevant to the etiopathogenesis of autism spectrum disorder. In sum, our data suggest fundamental relationships between gene expression and diffusion MRI, implicating the potential of diffusion MRI to probe causal neurobiological mechanisms in neuroimaging phenotypes in autism spectrum disorder.

Single-cell sequencing elucidates the mechanism of NUSAP1 in glioma and its diagnostic and prognostic significance

Background

Personalized precision medicine (PPPM) in cancer immunology and oncology is a rapidly advancing field with significant potential. Gliomas, known for their poor prognosis, rank among the most lethal brain tumors. Despite advancements, there remains a critical need for precise, individualized treatment strategies.

Methods

We conducted a comprehensive analysis of RNA-seq and microarray data from the TCGA and GEO databases, supplemented by single-cell RNA sequencing (scRNA-seq) data from glioma patients. By integrating single-cell sequencing analysis with foundational experiments, we investigated the molecular variations and cellular interactions within neural glioma cell subpopulations during tumor progression.

Results

Our single-cell sequencing analysis revealed distinct gene expression patterns across glioma cell subpopulations. Notably, differentiation trajectory analysis identified NUSAP1 as a key marker for the terminal subpopulation. We found that elevated NUSAP1 expression correlated with poor prognosis, prompting further investigation of its functional role through both cellular and animal studies.

Conclusions

NUSAP1-based risk models hold potential as predictive and therapeutic tools for personalized glioma treatment. In-depth exploration of NUSAP1's mechanisms in glioblastoma could enhance our understanding of its response to immunotherapy, suggesting that targeting NUSAP1 may offer therapeutic benefits for glioma patients.

Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques

BACKGROUND

Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear.

PURPOSE

To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation.

STUDY TYPE

Systematic review and meta-analysis.

POPULATION

Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488).

FIELD STRENGTH/SEQUENCE

Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T.

ASSESSMENT

Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs.

STATISTICAL TESTS

Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant.

RESULTS

HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08).

DATA CONCLUSION

ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible.

LEVEL OF EVIDENCE: 1

TECHNICAL EFFICACY

Stage 2.

The T1/T2 Ratio is Associated With Resectability in Patients With Isocitrate Dehydrogenase-Mutant Astrocytomas Central Nervous System World Health Organization Grades 2 and 3

BACKGROUND AND OBJECTIVES

Isocitrate dehydrogenase (IDH)-mutant astrocytomas central nervous system World Health Organization grade 2 and 3 show heterogeneous appearance on MRI. In the premolecular era, the discrepancy between T1 hypointense and T2 hyperintense tumor volume in absolute values has been proposed as a marker for diffuse tumor growth. We set out to investigate if a ratio of T1 to T2 tumor volume (T1/T2 ratio) is associated with resectability and overall survival (OS) in patients with IDH-mutant astrocytomas.

METHODS

Patient data from 2 centers (Sahlgrenska University Hospital, Center A; LMU University Hospital, Center B) were collected retrospectively. Inclusion criteria were as follows: pre and postoperative MRI scans available for volumetric analysis (I), diagnosis of an IDH-mutant astrocytoma between 2003 and 2021 (II), and tumor resection at initial diagnosis (III). Tumor volumes were manually segmented. The T1/T2 ratio was calculated and correlated with extent of resection, residual T2 tumor volume, and OS.

RESULTS

The study comprised 134 patients with 65 patients included from Center A and 69 patients from Center B. The median OS was 134 months and did not differ between the cohorts ( P = .29). Overall, the median T1/T2 ratio was 0.79 (range 0.15-1.0). Tumors displaying a T1/T2 ratio of 0.33 or lower showed significantly larger residual tumor volumes postoperatively (median 17.9 cm 3 vs 4.6 cm 3 , P = .03). The median extent of resection in these patients was 65% vs 90% ( P = .03). The ratio itself did not correlate with OS. In multivariable analyses, larger postoperative tumor volumes were associated with shorter survival times (hazard ratio 1.02, 95% CI 1.01-1.03, P < .01).

CONCLUSION

The T1/T2 ratio might be a good indicator for diffuse tumor growth on MRI and is associated with resectability in patients with IDH-mutant astrocytoma. This ratio might aid to identify patients in which an oncologically relevant tumor volume reduction cannot be safely achieved.

Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI

Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: A comprehensive literature search was performed in compliance with the updated PRISMA 2020 guidelines within electronic databases MEDLINE/PubMed, Scopus, and IEEE Xplore. Search terms, including "MGMT", "methylation", "glioma", "glioblastoma", "machine learning", "deep learning", and "radiomics", were adopted in various MeSH combinations. Original studies in the English, Italian, German, and French languages were considered for inclusion. Results: This review analyzed 34 studies conducted in the last six years, focusing on assessing MGMT methylation status using radiomics (RD), deep learning (DL), or combined approaches. These studies utilized radiological data from the public (e.g., BraTS, TCGA) and private institutional datasets. Sixteen studies focused exclusively on glioblastoma (GBM), while others included low- and high-grade gliomas. Twenty-seven studies reported diagnostic accuracy, with fourteen achieving values above 80%. The combined use of DL and RD generally resulted in higher accuracy, sensitivity, and specificity, although some studies reported lower minimum accuracy compared to studies using a single model. Conclusions: The integration of RD and DL offers a powerful, non-invasive tool for precisely recognizing MGMT promoter methylation status in gliomas, paving the way for enhanced personalized medicine in neuro-oncology. The heterogeneity of study populations, data sources, and methodologies reflected the complexity of the pipeline and machine learning algorithms, which may require general standardization to be implemented in clinical practice.

Presence of Fragmented Intratumoral Thrombosed Microvasculature in the Necrotic and Peri-Necrotic Regions on SWI Differentiates IDH Wild-Type Glioblastoma From IDH Mutant Grade 4 Astrocytoma

BACKGROUND

Isocitrate dehydrogenase (IDH) wild-type (IDHwt) glioblastomas (GB) are more aggressive and have a poorer prognosis than IDH mutant (IDHmt) tumors, emphasizing the need for accurate preoperative differentiation. However, a distinct imaging biomarker for differentiation mostly lacking. Intratumoral thrombosis has been reported as a histopathological biomarker for GB.

PURPOSE

To evaluate the fragmented intratumoral thrombosed microvasculature (FTV) signs on susceptibility-weighted imaging (SWI) for distinguishing IDHwt and IDHmt tumors.

STUDY TYPE

Retrospective.

SUBJECTS

Ninety-seven treatment-naïve patients with histopathologically confirmed IDHwt GB (54 males, 26 females) and IDHmt grade 4 astrocytoma (13 males, 4 females).

FIELD STRENGTH/SEQUENCE

3-T, SWI, fluid-attenuated-inversion-recovery (FLAIR), T1-weighted, T2-weighted, PD-weighted, post-contrast T1-weighted and dynamic-contrast-enhanced (DCE)-MRI.

ASSESSMENT

SWI data were evaluated by three experienced neuroradiologists (S.S., 11 years; J.S., 15 years; R.K.G., 40 years of experience), who assessed FTV presence in necrotic and peri-necrotic regions. FTV was identified as intratumoral susceptibility signal having minimal or no interslice connections. Quantitative DCE-MRI parameters were derived using first-pass-analysis and extended Tofts model. FLAIR abnormal, contrast-enhancing, and necrotic regions were segmented using in-house developed U-Net architecture.

STATISTICAL TESTS

Fleiss' Kappa, Cohen's Kappa, Shapiro-Wilk test, t tests or Mann-Whitney U test, receiver-operating characteristic (ROC) analysis, confusion matrix. A P-value <0.05 was considered statistically significant.

RESULTS

Fleiss' kappa test provided 91% inter-rater agreement, and Cohen's kappa provided intrarater agreement ranged from 81% to 97%. The raters' accuracy in distinguishing IDHwt from IDHmt ranged from 92% to 94%. Some of the quantitative DCE-MRI parameters (CBV, Ve, and Ktrans) provided statistically significant differences in differentiating IDHwt and IDHmt. Ktrans demonstrated 80.3% sensitivity and 81.2% specificity, with ROC analysis showing an AUC of 0.77.

DATA CONCLUSION

FTV signs in necrotic and peri-necrotic regions on SWI demonstrated a high accuracy in distinguishing IDHwt from IDHmt. Qualitative assessment of FTV signs showed almost perfect inter-rater and intrarater agreement. Quantitative DCE-MRI metrics also showed statistically significant differentiation of IDHwt and IDHmt.

PLAIN LANGUAGE SUMMARY

This study demonstrates that preoperative imaging, particularly the visualization of the fragmented thrombosed vasculature (FTV) sign on susceptibility-weighted imaging (SWI), effectively differentiates isocitrate dehydrogenase (IDH) wild-type (IDHwt) glioblastoma (GB) from IDH mutant (IDHmt) grade 4 astrocytomas. Over 90% of IDHwt GB patients displayed the FTV sign, a specific imaging biomarker absent in IDHmt cases. Perfusion parameters such as cerebral blood volume, Ve, and Ktrans were elevated in IDHwt gliomas, reflecting distinct vascular profiles. SWI offers a noninvasive and accurate diagnostic method, overcoming limitations of histopathology. Despite limitations like unequal sample sizes and retrospective analysis, this study underscores the clinical potential of SWI in improving glioma characterization and aiding treatment planning.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Turning attention to tumor-host interface and focus on the peritumoral heterogeneity of glioblastoma

Approximately 90% of glioblastoma recurrences occur in the peritumoral brain zone (PBZ), while the spatial heterogeneity of the PBZ is not well studied. In this study, two PBZ tissues and one tumor tissue sample are obtained from each patient via preoperative imaging. We assess the microenvironment and the characteristics of infiltrating immune/tumor cells using various techniques. Our data indicate there are one or more regions with higher cerebral blood flow in PBZ, which we collectively name the "higher cerebral blood flow interface" (HBI). The HBI exhibited more neovascularization than the "lower cerebral blood flow interfaces" (LBI). The HBI tend to have increased infiltration of macrophages and T lymphocytes infiltration compared with that in LBI. There are more tumor cells in the HBI than in LBI, with substantial differences in the gene expression profiles of these tumor cells. HBI may be the key area of PBZ-targeting therapy after surgical resection.

Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges

RATIONALE AND OBJECTIVES

Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP.

MATERIALS AND METHODS

This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics.

RESULTS

Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation.

CONCLUSION

Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.

A prognostic neural epigenetic signature in high-grade glioma

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.

Androgen loss weakens anti-tumor immunity and accelerates brain tumor growth

Many cancers, including glioblastoma (GBM), have a male-biased sex difference in incidence and outcome. The underlying reasons for this sex bias are unclear but likely involve differences in tumor cell state and immune response. This effect is further amplified by sex hormones, including androgens, which have been shown to inhibit anti-tumor T cell immunity. Here, we show that androgens drive anti-tumor immunity in brain tumors, in contrast to its effect in other tumor types. Upon castration, tumor growth was accelerated with attenuated T cell function in GBM and brain tumor models, but the opposite was observed when tumors were located outside the brain. Activity of the hypothalamus-pituitary-adrenal gland (HPA) axis was increased in castrated mice, particularly in those with brain tumors. Blockade of glucocorticoid receptors reversed the accelerated tumor growth in castrated mice, indicating that the effect of castration was mediated by elevated glucocorticoid signaling. Furthermore, this mechanism was not GBM specific, but brain specific, as hyperactivation of the HPA axis was observed with intracranial implantation of non-GBM tumors in the brain. Together, our findings establish that brain tumors drive distinct endocrine-mediated mechanisms in the androgen-deprived setting and highlight the importance of organ-specific effects on anti-tumor immunity.

Intrinsic and Microenvironmental Drivers of Glioblastoma Invasion

Gliomas are diffusely infiltrating brain tumors whose prognosis is strongly influenced by their extent of invasion into the surrounding brain tissue. While lower-grade gliomas present more circumscribed borders, high-grade gliomas are aggressive tumors with widespread brain infiltration and dissemination. Glioblastoma (GBM) is known for its high invasiveness and association with poor prognosis. Its low survival rate is due to the certainty of its recurrence, caused by microscopic brain infiltration which makes surgical eradication unattainable. New insights into GBM biology at the single-cell level have enabled the identification of mechanisms exploited by glioma cells for brain invasion. In this review, we explore the current understanding of several molecular pathways and mechanisms used by tumor cells to invade normal brain tissue. We address the intrinsic biological drivers of tumor cell invasion, by tackling how tumor cells interact with each other and with the tumor microenvironment (TME). We focus on the recently discovered neuronal niche in the TME, including local as well as distant neurons, contributing to glioma growth and invasion. We then address the mechanisms of invasion promoted by astrocytes and immune cells. Finally, we review the current literature on the therapeutic targeting of the molecular mechanisms of invasion.

Revealing the biology behind MRI signatures in high grade glioma

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.