Application of 7T MRS to High-Grade Gliomas

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.

Improved Detection of Target Metabolites in Brain Tumors with Intermediate TE, High SNR, and High Bandwidth Spin-Echo Sequence at 5T

BACKGROUND AND PURPOSE

Due to high chemical shift displacement, challenges emerge at ultra-high fields when measuring metabolites using 1H-MRS. Our goal was to investigate how well the high SNR and high bandwidth spin-echo (HISE) technique perform at 5T for detecting target metabolites in brain tumors.

MATERIALS AND METHODS

Twenty-six subjects suspected of having brain tumors were enrolled. HISE and point-resolved spectroscopy (PRESS) single-voxel spectroscopy scans were collected with a 5T clinical scanner with an intermediate TE (TE = 144 ms). The main metabolites, including total NAA, Cr, and total Cho, were accessed and compared between HISE and PRESS using a paired Student t test, with full width at half maximum and SNR as covariates. The detection rate of specific metabolites, including lactate, alanine, and lipid, and subjective spectral quality were accessed and compared between HISE and PRESS.

RESULTS

Twenty-three pathologically confirmed brain tumors were included. Only the full width at half maximum for total NAA was significantly lower with HISE than with PRESS (P < .05). HISE showed a significantly higher SNR for total NAA, Cr, and total Cho compared with PRESS (P < .05). Lactate was detected in 21 of the 23 cases using HISE, but in only 4 cases using PRESS. HISE detected alanine in 8 of 9 meningiomas, whereas PRESS detected alanine in just 3 meningiomas. PRESS found lipid in more cases than HISE, while HISE outperformed PRESS in terms of subjective spectral quality.

CONCLUSIONS

HISE outperformed the clinical standard PRESS technique in detecting target metabolites of brain tumors at 5T, particularly lactate and alanine.

Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases

OBJECTIVES

Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks.

METHODS

Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities.

RESULTS

The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously.

CONCLUSIONS

A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

Using the metabolite alterations monitoring the AEG-1 expression level and cell biological behaviour of U251 cell in vitro

Astrocyte elevated gene-1 (AEG-1) is an important oncogene that overexpresses in gliomas and plays a vital role in their occurrence and progression. However, few reports have shown which biomarkers could reflect the level of AEG-1 expression in vivo so far. In recent years, intracellular metabolites monitored by proton magnetic resonance spectroscopy (1H MRS) as non-invasive imaging biomarkers have been applied to the precise diagnosis and therapy feedback of gliomas. Therefore, understanding the correlation between 1H MRS metabolites and AEG-1 gene expression in U251 cells may help to identify relevant biomarkers. This study constructed three monoclonal AEG-1-knockout U251 cell lines using the clustered regularly interspaced short palindromic repeat (CRISPR) /Cas9 technique and evaluated the biological behaviors and metabolite ratios of these cell lines. With the decline in AEG-1 expression, the apoptosis rate of the AEG-1-knockout cell lines increased. At the same time, the metastatic capacities decreased, and the relative contents of total choline (tCho) and lactate (Lac) were also reduced. In conclusion, deviations in AEG-1 expression influence the apoptosis rate and metastasis capacity of U251 cells, which the 1H MRS metabolite ratio could monitor. The tCho/creatinine(Cr) and Lac/Cr ratios positively correlated with the AEG-1 expression and malignant cell behavior. This study may provide potential biomarkers for accurate preoperative diagnosis and future AEG-1-targeting treatment evaluation of gliomas in vivo.

Advances in the Radiological Evaluation of and Theranostics for Glioblastoma

Imaging is essential for evaluating patients with glioblastoma. Traditionally a multimodality undertaking, CT, including CT cerebral blood profusion, PET/CT with traditional fluorine-18 fluorodeoxyglucose (18F-FDG), and MRI have been the mainstays for diagnosis and post-therapeutic assessment. However, recent advances in these modalities, in league with the emerging fields of radiomics and theranostics, may prove helpful in improving diagnostic accuracy and treating the disease.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Multinuclear Magnetic Resonance Spectroscopy at Ultra-High-Field: Assessing Human Cerebral Metabolism in Healthy and Diseased States

The brain is a highly energetic organ. Although the brain can consume metabolic substrates, such as lactate, glycogen, and ketone bodies, the energy metabolism in a healthy adult brain mainly relies on glucose provided via blood. The cerebral metabolism of glucose produces energy and a wide variety of intermediate metabolites. Since cerebral metabolic alterations have been repeatedly implicated in several brain disorders, understanding changes in metabolite levels and corresponding cell-specific neurotransmitter fluxes through different substrate utilization may highlight the underlying mechanisms that can be exploited to diagnose or treat various brain disorders. Magnetic resonance spectroscopy (MRS) is a noninvasive tool to measure tissue metabolism in vivo. ¹H-MRS is widely applied in research at clinical field strengths (≤3T) to measure mostly high abundant metabolites. In addition, X-nuclei MRS including, ¹³C, ²H, ¹⁷O, and ³¹P, are also very promising. Exploiting the higher sensitivity at ultra-high-field (>4T; UHF) strengths enables obtaining unique insights into different aspects of the substrate metabolism towards measuring cell-specific metabolic fluxes in vivo. This review provides an overview about the potential role of multinuclear MRS (¹H, ¹³C, ²H, ¹⁷O, and ³¹P) at UHF to assess the cerebral metabolism and the metabolic insights obtained by applying these techniques in both healthy and diseased states.

A vision of 14 T MR for fundamental and clinical science

OBJECTIVE

We outline our vision for a 14 Tesla MR system. This comprises a novel whole-body magnet design utilizing high temperature superconductor; a console and associated electronic equipment; an optimized radiofrequency coil setup for proton measurement in the brain, which also has a local shim capability; and a high-performance gradient set.

RESEARCH FIELDS

The 14 Tesla system can be considered a 'mesocope': a device capable of measuring on biologically relevant scales. In neuroscience the increased spatial resolution will anatomically resolve all layers of the cortex, cerebellum, subcortical structures, and inner nuclei. Spectroscopic imaging will simultaneously measure excitatory and inhibitory activity, characterizing the excitation/inhibition balance of neural circuits. In medical research (including brain disorders) we will visualize fine-grained patterns of structural abnormalities and relate these changes to functional and molecular changes. The significantly increased spectral resolution will make it possible to detect (dynamic changes in) individual metabolites associated with pathological pathways including molecular interactions and dynamic disease processes.

CONCLUSIONS

The 14 Tesla system will offer new perspectives in neuroscience and fundamental research. We anticipate that this initiative will usher in a new era of ultra-high-field MR.

Multimodal molecular imaging in the second near-infrared window

Near-infrared-II (NIR-II) fluorescence imaging has rapidly developed for the noninvasive investigation of physiological and pathological activities in living organisms with high spatiotemporal resolution. However, the penetration depth of fluorescence restricts its ability to provide deep anatomical information. Scientists integrate NIR-II fluorescence imaging with other imaging modes (such as photoacoustic and magnetic resonance imaging) to create multimodal imaging that can acquire detailed anatomical and quantitative information with deeper penetration by using multifunctional probes. This review offers a comprehensive picture of NIR-II-based dual/multimodal imaging probes and highlights advances in bioimaging and therapy. In addition, seminal studies and trends in multimodal imaging probes activated by NIR-II laser are summarized and several key points regarding future clinical translation are elucidated.