Clinical Applications of Magnetic Resonance Spectroscopy in Brain Tumors: From Diagnosis to Treatment

The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.

Progressive Multifocal Leukoencephalopathy in a Patient With Cirrhosis and Hepatocellular Carcinoma

The following case describes a constellation of progressive cognitive and motor deficits in a 73-year-old man with cirrhosis and history of early-stage hepatocellular carcinoma confined to his liver. He had deficits in calculation, language, and writing, as well as subtle right-sided weakness. Magnetic resonance imaging (MRI) of the brain demonstrated non-enhancing white matter lesions without mass effect in the bilateral parietal and left occipitotemporal regions, correlating with neurologic exam findings. The patient's basic blood and cerebrospinal fluid (CSF) studies were within normal limits. Our differential included inflammatory and demyelinating conditions, hepatic encephalopathy, posterior reversible encephalopathy syndrome, progressive multifocal leukoencephalopathy (PML), and central nervous system (CNS) tumors. He did not improve with an empiric course of high-dose steroids or adequate hepatic encephalopathy treatment. A repeat lumbar puncture sent for additional CSF studies revealed a positive John Cunningham (JC) virus PCR test, confirming diagnosis of PML. Although the patient did not have any known overt immunosuppressive condition or treatment, the patient's cirrhosis and age placed him at higher risk for developing JC virus CNS reactivation. In a published case series of patients with PML and no classic immunosuppressive condition that includes several patients with concomitant cirrhosis, prognosis is much worse compared to those with known, reversible causes of immunosuppression.

MRI detection of free-contrast agent nanoparticles

PURPOSE

The integration of nanotechnology into biomedical imaging has significantly advanced diagnostic and theranostic capabilities. However, nanoparticle detection in imaging relies on functionalization with appropriate probes. In this work, a new approach to visualize free-label nanoparticles using MRI and MRS techniques is described, consisting of detecting by 1H CSI specific proton signals belonging to the components naturally present in most of the nanosystems used in preclinical and clinical research.

METHODS

Three different nanosystems, namely lipid-based micelles, liposomes, and perfluorocarbon-based nanoemulsions, were synthesized, characterized by high resolution NMR and then visualized by 1H CSI at 300 MHz. Subsequently the best 1H CSI performing system was administered to murine models of cancer to evaluate the possibility of tracking the nanosystem by looking at its proton associated signal. Furthermore, an in vitro comparison between 1H CSI and 19F MRI was carried out.

RESULTS

The study successfully demonstrates the feasibility of detecting nanoparticles using MRI/MRS without probe functionalization, employing 1H CSI. Among the nanosystems tested, the perfluorocarbon-based nanoemulsion exhibited the highest SNR. Consequently, it was evaluated in vivo, where its detection was achievable within tumors and inflamed regions via 1H CSI, and in lymph nodes via PRESS.

CONCLUSIONS

These findings present a promising avenue for nanoparticle imaging in biomedical applications, offering potential enhancements to diagnostic and theranostic procedures. This non-invasive approach has the capacity to advance imaging techniques and expand the scope of nanoparticle-based biomedical research. Further exploration is necessary to fully explore the implications and applications of this method.

Role of Magnetic Resonance Spectroscopy in Evaluating Choline Levels in Gallbladder Carcinoma: A Comprehensive Review

Gallbladder carcinoma (GBC) presents a significant clinical challenge due to its aggressive nature and often asymptomatic progression, resulting in late-stage diagnoses and a poor prognosis. Early detection and accurate staging are pivotal for improving patient outcomes, highlighting the critical role of advanced imaging techniques in oncological practice. Magnetic resonance spectroscopy (MRS) has emerged as a valuable non-invasive tool capable of assessing biochemical changes within tissues, including alterations in choline metabolism-a biomarker indicative of cell membrane turnover and proliferation. This review explores the application of MRS in evaluating choline levels in gallbladder carcinoma, synthesizing current literature to elucidate its potential in clinical settings. By analyzing studies investigating the correlation between choline levels detected via MRS and tumor characteristics, this review underscores MRS's role in enhancing diagnostic precision and guiding therapeutic decision-making. Moreover, it discusses the challenges and limitations associated with MRS in clinical practice alongside future research and technological advancement directions. Ultimately, integrating MRS into the diagnostic armamentarium for gallbladder carcinoma promises to improve early detection and treatment outcomes. This review provides insights into the evolving landscape of MRS in oncology, emphasizing its contribution to personalized medicine approaches aimed at optimizing patient care and management strategies for GBC.

A Comprehensive Review on the Role of MRI in the Assessment of Supratentorial Neoplasms: Comparative Insights Into Adult and Pediatric Cases

Magnetic resonance imaging (MRI) is a critical diagnostic tool in assessing supratentorial neoplasms, offering unparalleled detail and specificity in brain imaging. Supratentorial neoplasms in the cerebral hemispheres, basal ganglia, thalamus, and other structures above the tentorium cerebelli present significant diagnostic and therapeutic challenges. These challenges vary notably between adult and pediatric populations due to differences in tumor types, biological behavior, and patient management strategies. This comprehensive review explores the role of MRI in diagnosing, planning treatment, monitoring response, and detecting recurrence in supratentorial neoplasms, providing comparative insights into adult and pediatric cases. The review begins with an overview of the epidemiology and pathophysiology of these tumors in different age groups, followed by a detailed examination of standard and advanced MRI techniques, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy (MRS). We discuss the specific imaging characteristics of various neoplasms and the importance of tailored approaches to optimize diagnostic accuracy and therapeutic efficacy. The review also addresses the technical and interpretative challenges unique to pediatric imaging and the implications for long-term patient outcomes. By highlighting the comparative utility of MRI in adult and pediatric cases, this review aims to enhance the understanding of its pivotal role in managing supratentorial neoplasms. It underscores the necessity of age-specific diagnostic and therapeutic strategies. Emerging MRI technologies and future research directions are also discussed, emphasizing the potential for advancements in personalized imaging approaches and improved patient care across all age groups.

Posterior fossa Hodgkin's lymphoma radiographically mimicking an arteriovenous malformation: illustrative case

BACKGROUND

Intracranial Hodgkin's lymphoma (HL) is an exceedingly rare condition that is at an increased risk of misdiagnosis and mismanagement, especially when initial radiographic evidence points to an alternative pathology.

OBSERVATIONS

The authors describe the case of a 75-year-old female who presented with a posterior fossa lesion initially concerning for a vascular malformation on computed tomography imaging due to perilesional hypervascularity. Subsequent angiography revealed a developmental venous anomaly (DVA) but no arteriovenous shunting. The patient's clinical history combined with magnetic resonance imaging findings prompted a tissue biopsy, which demonstrated a rare case of central nervous system (CNS) HL. The neoangiogenesis of this CNS HL with an adjacent DVA contributed to the original radiographic misdiagnosis of an arteriovenous malformation. HL's angiogenic potential, coupled with the proangiogenic environment induced around DVAs, may have contributed to this rare CNS HL metastasis to the cerebellum. The potential misdiagnosis of posterior fossa CNS HL has also been seen in several prior cases reviewed herein.

LESSONS

Hypervascular tumors, especially when associated with an adjacent DVA, should also be considered when first evaluating suspected intracranial vascular lesions. Although rare, CNS HL should be included in the differential diagnosis for patients with a prior history of HL. https://thejns.org/doi/10.3171/CASE24238.

Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry

The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model

Glioblastoma (GBM) stands as the most prevalent and lethal malignant brain tumor, characterized by its highly infiltrative nature. This study aimed to identify additional MRI and metabolomic biomarkers of GBM and its impact on healthy tissue using an advanced-stage C6 glioma rat model. Wistar rats underwent a stereotactic injection of C6 cells (GBM group, n = 10) or cell medium (sham group, n = 4). A multiparametric MRI, including anatomical T2W and T1W images, relaxometry maps (T2, T2*, and T1), the magnetization transfer ratio (MTR), and diffusion tensor imaging (DTI), was performed. Additionally, ex vivo magnetic resonance spectroscopy (MRS) HRMAS spectra were acquired. The MRI analysis revealed significant differences in the T2 maps, T1 maps, MTR, and mean diffusivity parameters between the GBM tumor and the rest of the studied regions, which were the contralateral areas of the GBM rats and both regions of the sham rats (the ipsilateral and contralateral). The ex vivo spectra revealed markers of neuronal loss, apoptosis, and higher glucose uptake by the tumor. Notably, the myo-inositol and phosphocholine levels were elevated in both the tumor and the contralateral regions of the GBM rats compared to the sham rats, suggesting the effects of the tumor on the healthy tissue. The MRI parameters related to inflammation, cellularity, and tissue integrity, along with MRS-detected metabolites, serve as potential biomarkers for the tumor evolution, treatment response, and impact on healthy tissue. These techniques can be potent tools for evaluating new drugs and treatment targets.

Return to Play Following Concussion: Role for Imaging?

This review surveys concussion management, focusing on the use of neuroimaging techniques in return to play (RTP) decisions. Clinical assessments traditionally were the foundation of concussion diagnoses. However, their subjective nature prompted an exploration of neuroimaging modalities to enhance diagnosis and management. Magnetic resonance spectroscopy provides information about metabolic changes and alterations in the absence of structural abnormalities. Diffusion tensor imaging uncovers microstructural changes in white matter. Functional magnetic resonance imaging assesses neuronal activity to reveal changes in cognitive and sensorimotor functions. Positron emission tomography can assess metabolic disturbances using radiotracers, offering insight into the long-term effects of concussions. Vestibulo-ocular dysfunction screening and eye tracking assess vestibular and oculomotor function. Although these neuroimaging techniques demonstrate promise, continued research and standardization are needed before they can be integrated into the clinical setting. This review emphasizes the potential for neuroimaging in enhancing the accuracy of concussion diagnosis and guiding RTP decisions.

Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma

Background

Glioma is one of the most drug and radiation-resistant tumors. Gliomas suffer from inter- and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed.

Materials and Methods

The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings.

Results

Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F-FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F-FET, 11C-MET, and 18F-FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. 2-hydroxyglutarate metabolite and dynamic 18F-FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations.

Conclusion

Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F-FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.

Challenges in radiological evaluation of brain metastases, beyond progression

MRI is the cornerstone in the evaluation of brain metastases. The clinical challenges lie in discriminating metastases from mimickers such as infections or primary tumors and in evaluating the response to treatment. The latter sometimes leads to growth, which must be framed as pseudo-progression or radionecrosis, both inflammatory phenomena attributable to treatment, or be considered as recurrence. To meet these needs, imaging techniques are the subject of constant research. However, an exponential growth after radiotherapy must be interpreted with caution, even in the presence of results suspicious of tumor progression by advanced techniques, because it may be due to inflammatory changes. The aim of this paper is to familiarize the reader with inflammatory phenomena of brain metastases treated with radiotherapy and to describe two related radiological signs: "the inflammatory cloud" and "incomplete ring enhancement", in order to adopt a conservative management with close follow-up.

MRI Treatment Response Assessment Maps (TRAMs) for differentiating recurrent glioblastoma from radiation necrosis

BACKGROUND

MRI treatment response assessment maps (TRAMs) were introduced to distinguish recurrent malignant glioma from therapy related changes. TRAMs are calculated with two contrast-enhanced T1-weighted sequences and reflect the "late" wash-out (or contrast clearance) and wash-in of gadolinium. Vital tumor cells are assumed to produce a wash-out because of their high turnover rate and the associated hypervascularization, whereas contrast medium slowly accumulates in scar tissue. To examine the real value of this method, we compared TRAMs with the pathology findings obtained after a second biopsy or surgery when recurrence was suspected.

METHODS

We retrospectively evaluated TRAMs in adult patients with histologically demonstrated glioblastoma, contrast-enhancing tissue and a pre-operative MRI between January 1, 2017, and December 31, 2022. Only patients with a second biopsy or surgery were evaluated. Volumes of the residual tumor, contrast clearance and contrast accumulation before the second surgery were analyzed.

RESULTS

Among 339 patients with mGBM who underwent MRI, we identified 29 repeated surgeries/biopsies in 27 patients 59 ± 12 (mean ± standard deviation) years of age. Twenty-eight biopsies were from patients with recurrent glioblastoma histology, and only one was from a patient with radiation necrosis. We volumetrically evaluated the 29 pre-surgery TRAMs. In recurrent glioblastoma, the ratio of wash-out volume to tumor volume was 36 ± 17% (range 1-73%), and the ratio of the wash-out volume to the sum of wash-out and wash-in volumes was 48 ± 21% (range 22-92%). For the one biopsy with radiation necrosis, the ratios were 42% and 54%, respectively.

CONCLUSIONS

Typical recurrent glioblastoma shows a > 20%ratio of the wash-out volume to the sum of wash-out and wash-in volumes. The one biopsy with radiation necrosis indicated that such necrosis can also produce high wash-out in individual cases. Nevertheless, the additional information provided by TRAMs increases the reliability of diagnosis.

The etiological spectrum of multiple ring-enhancing lesions of the brain: a systematic review of published cases and case series

OBJECTIVE

Multiple ring-enhancing lesions of the brain are enigmatic neuroimaging abnormality. In this systematic review, we evaluated the etiological spectrum of these lesions.

METHODS

This systematic review adhered to the PRISMA guidelines. We searched PubMed, Embase, Scopus, and Google Scholar up until 15 June 2023. We included case reports and case series. Quality evaluation of each case was based on selection, ascertainment, causality, and reporting. The extracted information included demographic characteristics, clinical features, type and number of multiple enhancing brain lesions, diagnostic procedures, final diagnoses, treatments, and patient outcomes.

PROTOCOL REGISTRATION

PROSPERO CRD42023437081.

RESULTS

We analyzed 156 records representing 161 patients, 60 of whom were immunocompromised. The mean age was 42.6 years, and 67% of patients experienced symptoms for up to 1 month. A higher proportion of immunocompromised patients (42% vs. 30%) exhibited encephalopathy. Chest or CT thorax abnormalities were reported in 27.3% of patients, while CSF abnormalities were found in 31.7%, more frequently among the immunocompromised. Definitive diagnoses were established via brain biopsy, aspiration, or autopsy in 60% of cases, and through CSF examination or other ancillary tests in 40% of cases. Immunocompromised patients had a higher incidence of Toxoplasma gondii infection and CNS lymphoma, while immunocompetent patients had a higher incidence of Mycobacterium tuberculosis infection and immune-mediated and demyelinating disorders. The improvement rate was 74% in immunocompetent patients compared to 52% in the immunocompromised group.

CONCLUSION

Multiple ring-enhancing lesions of the brain in immunocompromised patients are more frequently caused by Toxoplasma gondii infections and CNS lymphoma. Conversely, among immunocompetent patients, Mycobacterium tuberculosis infection and immune-related demyelinating conditions are common.

Central nervous system involvement in chronic inflammatory demyelinating polyradiculoneuropathy-MRS and DTI study

Objective

The current research aimed to analyze the alterations within the motor cortex and pyramidal pathways and their association with the degree of damage within the peripheral nerve fibers in patients with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). To achieve that goal, we investigated the microstructural changes within the pyramidal white matter tracts using diffusion tensor imaging (DTI) parameters, evaluated metabolic alterations in both precentral gyri using magnetic resonance spectroscopy (MRS) ratios, and correlated them with the neurographic findings in patients with CIDP.

Methods

The spectroscopic ratios of NAA/Cr, Cho/Cr, and mI/Cr from both precentral gyri and the values of fractional anisotropy (FA), axial diffusivity (AD), and mean diffusivity (MD) from both of the corticospinal tracts were correlated with the results of neurological and neurographic findings. The comparison of DTI parameters between the patients and controls was performed using Student's t-test or the Mann-Whitney U test. Due to the lack of normal distribution of most variables, Spearman's Rho rank coefficient was used to test all correlations. All analyses were performed at a significant level of alpha = 0.05 using STATISTICA 13.3.

Results

Compared to the control group (CG), the patient group showed significantly lower ratios of NAA/Cr (1.66 ± 0.11 vs. 1.61 ± 0.15; p = 0.022), higher ratios of ml/Cr in the right precentral gyrus (0.57 ± 0.15 vs. 0.61 ± 0.08; p = 0.005), and higher levels of Cho/Cr within the left precentral gyrus (0.83 ± 0.09 vs. 0.88 ± 0.14, p = 0.012). The DTI parameters of MD from the right CST and AD from the right and left CSTs showed a strong positive correlation (0.52-0.53) with the sural sensory nerve action potential (SNAP) latency of the right sural nerve. There were no other significant correlations between other DTI and MRS parameters and neurographic results.

Significance

In our study, significant metabolic alterations were found in the precentral gyri in patients with CIDP without clinical symptoms of central nervous system involvement. The revealed changes reflected neuronal loss or dysfunction, myelin degradation, and increased gliosis. Our results suggest coexisting CNS damage in these patients and may provide a new insight into the still unknown pathomechanism of CIDP.

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Brain Tumor Imaging: Review of Conventional and Advanced Techniques

Approaches to central nervous system (CNS) tumor classification and evaluation have undergone multiple iterations over the past few decades, in large part due to our growing understanding of the influence of genetics on tumor behavior and our refinement of brain tumor imaging techniques. Computed tomography and magnetic resonance imaging (MRI) both play a critical role in the diagnosis and monitoring of brain tumors, although MRI has become especially important due to its superior soft tissue resolution. The purpose of this article will be to briefly review the fundamentals of conventional and advanced techniques used in brain tumor imaging. We will also highlight the applications of these imaging tools in the context of commonly encountered tumors based on the most recently updated 2021 World Health Organization (WHO) classification of CNS tumors framework.

Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme

Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.

Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks

BACKGROUND

Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation.

METHODS

This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation.

RESULTS

The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method.

CONCLUSIONS

The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.

Imaging Cancer in Neuroradiology

Neuroimaging plays a pivotal role in the diagnosis, management, and prognostication of brain tumors. Recently, the World Health Organization published the fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS5), which places greater emphasis on tumor genetics and molecular markers to complement the existing histological and immunohistochemical approaches. Recent advances in computational power allowed modern neuro-oncological imaging to move from a strictly morphology-based discipline to advanced neuroimaging techniques with quantifiable tissue characteristics such as tumor cellularity, microstructural organization, hemodynamic, functional, and metabolic features, providing more precise tumor diagnosis and management. The aim of this review is to highlight the key imaging features of the recently published CNS5, outlining the current imaging standards and summarizing the latest advances in neuro-oncological imaging techniques and their role in complementing traditional brain tumor imaging and management.

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.

Imaging in translational cancer research

This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.

A Rapid Late Enhancement MRI Protocol Improves Differentiation between Brain Tumor Recurrence and Treatment-Related Contrast Enhancement of Brain Parenchyma

PURPOSE

Differentiation between tumor recurrence and treatment-related contrast enhancement in MRI can be difficult. Late enhancement MRI up to 75 min after contrast agent application has been shown to improve differentiation between tumor recurrence and treatment-related changes. We investigated the diagnostic performance of late enhancement using a rapid MRI protocol optimized for clinical workflow.

METHODS

Twenty-three patients with 28 lesions suspected for glioma recurrence underwent MRI including T1-MPRAGE-series acquired 2 and 20 min after contrast agent administration. Early contrast series were subtracted from late contrast series using motion correction. Contrast enhancing lesions were retrospectively and independently evaluated by two readers blinded to the patients' later clinical course and histology with or without the use of late enhancement series. Sensitivity, specificity, NPV, and PPV were calculated for both readers by comparing results of MRI with histological samples.

RESULTS

Using standard MR sequences, sensitivity, specificity, PPV, and NPV were 0.84, 0, 0.875, and 0 (reader 1) and 0.92, 0, 0.885, and 0 (reader 2), respectively. Early late enhancement increased sensitivity, specificity, PPV, and NPV to 1 for each value and for both readers. Inter-reader reliability increased from 0.632 (standard MRI sequences) to 1.0 (with early late enhancement).

CONCLUSION

The described rapid late enhancement MRI protocol improves MRI-based discrimination between tumor tissue and treatment-related changes of the brain parenchyma.

MRSCloud: A cloud-based MRS tool for basis set simulation

PURPOSE

The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T.

METHODS

The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio.

RESULTS

Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96.

CONCLUSIONS

Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.

Magnetic resonance imaging of cardiac metabolism in heart failure: how far have we come?

As one of the highest energy consumer organs in the body, the heart requires tremendous amount of adenosine triphosphate (ATP) to maintain its continuous mechanical work. Fatty acids, glucose, and ketone bodies are the primary fuel source of the heart to generate ATP with perturbations in ATP generation possibly leading to contractile dysfunction. Cardiac metabolic imaging with magnetic resonance imaging (MRI) plays a crucial role in understanding the dynamic metabolic changes occurring in the failing heart, where the cardiac metabolism is deranged. Also, targeting and quantifying metabolic changes in vivo noninvasively is a promising approach to facilitate diagnosis, determine prognosis, and evaluate therapeutic response. Here, we summarize novel MRI techniques used for detailed investigation of cardiac metabolism in heart failure including magnetic resonance spectroscopy (MRS), hyperpolarized MRS, and chemical exchange saturation transfer based on evidence from preclinical and clinical studies and to discuss the potential clinical application in heart failure.

Convolutional neural networks to predict brain tumor grades and Alzheimer’s disease with MR spectroscopic imaging data

Purpose

To evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer’s disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions.

Methods

A shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a classification layer that outputs class predictions for the given brain voxel.

Results

Our CNN method separated glioma grades 3 and 4 and identified Alzheimer’s disease patients using MRSI and complementary MRI data with high MCC score (Area Under the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness over other popular methods as Partial Least Squares or Support Vector Machines. Also, our method automatically identified the spectral regions most important in the diagnosis process and we show that these are in good agreement with existing biomarkers from the literature.

Conclusion

Shallow CNNs models integrating image and spectral features improved quantitative and exploration and diagnosis of brain diseases for research and clinical purposes. Software is available at https://bitbucket.org/TeslaH2O/cnn_mrsi.

Magnetic Resonance Spectroscopy May Help Diagnose Sporadic Meningioangiomatosis Associated With Meningioma: A Case Report

Herein, we have presented the clinical features of meningioangiomatosis associated with meningioma, which is considered to be a rare neoplastic lesion. Magnetic resonance spectroscopy (MRS) demonstrated a remarkably decreased N-acetylaspartate peak and an increase in the choline peak of the lesion, suggesting neuronal injury and active cell proliferation. These findings substantially differed from those observed in the case of pure meningioangiomatosis.

Cell-free plasma microRNAs that identify patients with glioblastoma

Glioblastoma (GBM) is still one of the most commonly diagnosed advanced stage primary brain tumors. Current treatments for patients with primary GBM (pGBM) are often not effective and a significant proportion of the patients with pGBM recur. The effective treatment options for recurrent GBM (rGBM) are limited and survival outcomes are poor. This retrospective multicenter pilot study aims to determine potential cell-free microRNAs (cfmiRs) that identify patients with pGBM and rGBM tumors. 2,083 miRs were assessed using the HTG miRNA whole transcriptome assay (WTA). CfmiRs detection was compared in pre-operative plasma samples from patients with pGBM (n = 32) and rGBM (n = 13) to control plasma samples from normal healthy donors (n = 73). 265 cfmiRs were found differentially expressed in plasma samples from pGBM patients compared to normal healthy donors (FDR < 0.05). Of those 193 miRs were also detected in pGBM tumor tissues (n = 15). Additionally, we found 179 cfmiRs differentially expressed in rGBM, of which 68 cfmiRs were commonly differentially expressed in pGBM. Using Random Forest algorithm, specific cfmiR classifiers were found in the plasma of pGBM, rGBM, and both pGBM and rGBM combined. Two common cfmiR classifiers, miR-3180-3p and miR-5739, were found in all the comparisons. In receiving operating characteristic (ROC) curves analysis for rGBM miR-3180-3p showed a specificity of 87.7% and a sensitivity of 100% (AUC = 98.5%); while miR-5739 had a specificity of 79.5% and sensitivity of 92.3% (AUC = 90.2%). This study demonstrated that plasma samples from pGBM and rGBM patients have specific miR signatures. CfmiR-3180-3p and cfmiR-5739 have potential utility in diagnosing patients with pGBM and rGBM tumors using a minimally invasive blood assay.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C-Means Algorithm

This study was aimed to explore the diagnostic value of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in brain tumors under the fuzzy C-means (FCM) algorithm. The two-dimensional FCM hybrid algorithm was improved to be three-dimensional. The MRI images and MRS spectra of 127 patients with brain tumors (low-grade glioma group) and 54 healthy people (healthy group) were analyzed. The results suggested that the membership matrix of the improved algorithm had lower ambiguity, higher segmentation accuracy, closer relationship of intrapixels, and stronger irrelevance of interclass pixels. Through the analysis of gray matter volume, it was found that, compared with the healthy group, the gray matter and white matter volumes in the brain of high-grade glioma were higher, and those of low-grade glioma group were lower. The improved FCM algorithm could obtain a higher accuracy of 88.64% in segmenting images. It had a higher sensitivity to gray matter changes in brain tumors, reaching 92.72%; its specificity was not much different from that of traditional FCM, which were 83.61% and 88.06%, respectively. In the diagnostic value, the area under the curve of mean kurtosis was the largest, which was 0.962 (P < 0.001). The best critical value was 0.4096, which had a greater reference significance for clinical treatment and prognosis. The ratio of choline/N-acetyl-aspartate and the ratio of choline/creatine also showed significant differences in high- and low-grade gliomas (P < 0.05), but the specificity and sensitivity were slightly lower. It also had guiding significance for the grading of gliomas. Overall, the improved FCM algorithm had obvious advantages in the segmentation process of MRI images, which provided help for the clinical diagnosis of brain tumors.

Differences in the Impact of COVID-19 on Pathology Laboratories and Cancer Diagnosis in Girona

Introduction: The recent COVID-19 pandemic has compromised socio-health care, with consequences for the diagnosis and follow-up of other pathologies. The aim of this study was to evaluate the impact of COVID-19 on cancer diagnosis in Girona, Spain. Methodology: Observational study of samples received in two pathology laboratories during 2019–2020 (tertiary hospital in Girona and county hospital in Figueres). Date, sample type, and location and morphology were available. Samples were recoded to determine malignancy and grouped by location. Comparisons were made by calendar year and period of exposure to COVID-19. Results: 102,360 samples were included: 80,517 from Girona and 21,843 from Figueres. The reduction in activity in the pathology laboratories in 2020 compared to the previous year was 25.4% in Girona and 27.5% in Figueres. The reduction in cancer diagnoses in 2020 compared to 2019 was 6.8% in Girona and 21% in Figueres. In both laboratories, a decrease was observed in the diagnoses of neoplasms of the lip, oral cavity and pharynx, larynx, colon, rectum and anus, kidney and urinary system, melanoma, and central nervous system. A statistically significant higher probability of a sample received in the pathology laboratory displaying malignancy during COVID-19 was found (Girona: OR = 1.28, 95% CI: 1.23–1.34; Figueres: OR = 1.10, 95% CI: 1.01–1.20) with respect to the COVID-19-free period. Conclusions: The COVID-19 pandemic has resulted in a reduction in cancer diagnoses by pathology departments that varies according to tumor location and type of hospital. Despite this, the optimization of care resources and the recovery effort have partially reduced the impact of the pandemic in certain neoplasms.

Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas

ABSTRACT

Advanced imaging techniques provide a powerful tool to assess the intratumoral and intertumoral heterogeneity of gliomas. Advances in the molecular understanding of glioma subgroups may allow improved diagnostic assessment combining imaging and molecular tumor features, with enhanced prognostic utility and implications for patient treatment. In this article, a comprehensive overview of the physiologic basis for conventional and advanced imaging techniques is presented, and clinical applications before and after treatment are discussed. An introduction to the principles of radiomics and the advanced integration of imaging, clinical outcomes, and genomic data highlights the future potential for this field of research to better stratify and select patients for standard as well as investigational therapies.