Metabolic imbalance and brain tumors: The interlinking metabolic pathways and therapeutic actions of antidiabetic drugs

Brain tumors are complex, heterogeneous malignancies, often associated with significant morbidity and mortality. Emerging evidence suggests the important role of metabolic syndrome, such as that observed in diabetes mellitus, in the progression of brain tumors. Several studies indicated that hyperglycemia, insulin resistance, oxidative stress, and altered adipokine profiles influence tumor growth, proliferation, and treatment resistance. Intriguingly, antidiabetic drugs (e.g., metformin, sulfonylureas, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and thiazolidinediones) have shown promise as adjunctive or repurposed agents in managing brain tumors. Metformin can impair tumor cell proliferation, enhance treatment sensitivity, and modify the tumor microenvironment by activating AMP-activated protein kinase (AMPK) and inhibiting mammalian target of rapamycin (mTOR) signaling pathways. DPP-4 inhibitors and GLP-1 receptor agonists can target both metabolic and inflammatory aspects of brain tumors, while thiazolidinediones may induce apoptosis in tumor cells and synergize with other therapeutics. Consequently, further studies and clinical trials are needed to confirm the efficacy, safety, and utility of metabolic interventions in treating brain tumors. Here, we review the evidence for the metabolic interconnections between metabolic diseases and brain tumors and multiple actions of anti-diabetes drugs in brain tumors.

Autophagy in brain tumors: molecular mechanisms, challenges, and therapeutic opportunities

Autophagy is responsible for maintaining cellular balance and ensuring survival. Autophagy plays a crucial role in the development of diseases, particularly human cancers, with actions that can either promote survival or induce cell death. However, brain tumors contribute to high levels of both mortality and morbidity globally, with resistance to treatments being acquired due to genetic mutations and dysregulation of molecular mechanisms, among other factors. Hence, having knowledge of the role of molecular processes in the advancement of brain tumors is enlightening, and the current review specifically examines the role of autophagy. The discussion would focus on the molecular pathways that control autophagy in brain tumors, and its dual role as a tumor suppressor and a supporter of tumor survival. Autophagy can control the advancement of different types of brain tumors like glioblastoma, glioma, and ependymoma, demonstrating its potential for treatment. Autophagy mechanisms can influence metastasis and drug resistance in glioblastoma, and there is a complex interplay between autophagy and cellular responses to stress like hypoxia and starvation. Autophagy can inhibit the growth of brain tumors by promoting apoptosis. Hence, focusing on autophagy could offer fresh perspectives on creating successful treatments.

Exploring adult glioma through MRI: A review of publicly available datasets to guide efficient image analysis

Background

Publicly available data are essential for the progress of medical image analysis, in particular for crafting machine learning models. Glioma is the most common group of primary brain tumors, and magnetic resonance imaging (MRI) is a widely used modality in their diagnosis and treatment. However, the availability and quality of public datasets for glioma MRI are not well known.

Methods

In this review, we searched for public datasets of glioma MRI using Google Dataset Search, The Cancer Imaging Archive, and Synapse.

Results

A total of 28 datasets published between 2005 and May 2024 were found, containing 62 019 images from 5515 patients. We analyzed the characteristics of these datasets, such as the origin, size, format, annotation, and accessibility. Additionally, we examined the distribution of tumor types, grades, and stages among the datasets. The implications of the evolution of the World Health Organization (WHO) classification on tumors of the brain are discussed, in particular the 2021 update that significantly changed the definition of glioblastoma.

Conclusions

Potential research questions that could be explored using these datasets were highlighted, such as tumor evolution through malignant transformation, MRI normalization, and tumor segmentation. Interestingly, only 2 datasets among the 28 studied reflect the current WHO classification. This review provides a comprehensive overview of the publicly available datasets for glioma MRI currently at our disposal, providing aid to medical image analysis researchers in their decision-making on efficient dataset choice.

Robust brain MRI image classification with SIBOW-SVM

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) is the most effective technique for brain tumor detection, generating comprehensive brain scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning methods, such as Convolutional Neural Networks (CNNs), have demonstrated significantly higher accuracy than manual screening. However, deep learning-based image classification methods, including CNNs, face challenges in estimating class probabilities without proper model calibration (Guo et al., 2017; Minderer et al., 2021). In this paper, we propose a novel brain tumor image classification method called SIBOW-SVM, which integrates the Bag-of-Features model with SIFT feature extraction and weighted Support Vector Machines. This new approach can effectively extract hidden image features, enabling differentiation of various tumor types, provide accurate label predictions, and estimate probabilities of images belonging to each class, offering high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive image datasets. To benchmark our method, we apply SIBOW-SVM to a public dataset of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results demonstrate that the new method outperforms state-of-the-art techniques, including CNNs, in terms of uncertainty quantification, classification accuracy, computational efficiency, and data robustness.

Neurological insights into brain-targeted cancer therapy and bioinspired microrobots

Cancer, a multifaceted and pernicious disease, continuously challenges medicine, requiring innovative treatments. Brain cancers pose unique and daunting challenges due to the intricacies of the central nervous system and the blood-brain barrier. In this era of precision medicine, the convergence of neurology, oncology, and cutting-edge technology has given birth to a promising avenue - targeted cancer therapy. Furthermore, bioinspired microrobots have emerged as an ingenious approach to drug delivery, enabling precision and control in cancer treatment. This Keynote review explores the intricate web of neurological insights into brain-targeted cancer therapy and the paradigm-shifting world of bioinspired microrobots. It serves as a critical and comprehensive overview of these evolving fields, aiming to underscore their integration and potential for revolutionary cancer treatments.

DA, K. KK. Diagnostic Utility of Integration of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MR Perfusion Employing Split Bolus Technique in Differentiating High-Grade Glioma

Background : Despite documented correlation between glioma grades and dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion-derived parameters, and its inherent advantages over dynamic susceptibility contrast (DSC) perfusion, the former remains underutilized in clinical practice. Given the inherent spatial heterogeneity in high-grade diffuse glioma (HGG) and assessment of different perfusion parameters by DCE (extravascular extracellular space volume [Ve] and volume transfer constant in unit time [k-trans]) and DSC (rCBV), integration of the two into a protocol could provide a holistic assessment. Considering therapeutic and prognostic implications of differentiating WHO grade 3 from 4, we analyzed the two grades based on a combined DCE and DSC perfusion. Methods : Perfusion sequences were performed on 3-T MR. Cumulative dose of 0.1 mmol/kg of gadodiamide, split into two equal boluses, was administered with an interval of 6 minutes between the DCE and DSC sequences. DCE data were analyzed utilizing commercially available GenIQ software. Results : Of the 41 cases of diffuse gliomas analyzed, 24 were WHO grade III and 17 grade IV gliomas (2016 WHO classification). To differentiate grade III and IV gliomas, Ve cut-off value of 0.178 provided the best combination of sensitivity (88.24%) and specificity (87.50%; AUC: 0.920; p  < 0.001). A relative cerebral blood volume (rCBV) of value 3.64 yielded a sensitivity of 70.59% and specificity of 62.50% ( p  = 0.018). The k-trans value, although higher in grade III than in grade IV gliomas, did not reach statistical significance ( p  = 0.108). Conclusion : Uniqueness of employed combined perfusion technique, treatment naïve patients at imaging, user-friendly postprocessing software utilization, and ability of Ve and rCBV to differentiate between grade III and IV gliomas ( p  < 0.05) are the strengths of the present study, contributing to the existing literature and moving a step closer to achieving accurate MR perfusion-based glioma grading.

Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis

BACKGROUND

Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor types.

METHODS

A systematic review and meta-analysis were conducted, searching PubMed, Embase, and Web of Science up to March 14, 2023. Studies that only investigated image segmentation accuracy or brain tumor detection instead of classification were excluded. We extracted binary diagnostic accuracy data, constructing contingency tables to derive sensitivity and specificity.

RESULTS

Fifty-one studies were included. The pooled area under the curve for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% confidence interval [CI]: 0.98-1.00) and 0.89, respectively. The pooled sensitivity and specificity for benign versus malignant tumors were 0.90 (95% CI: 0.85-0.93) and 0.93 (95% CI: 0.90-0.95), respectively. The pooled sensitivity and specificity for low-grade versus high-grade gliomas were 0.99 (95% CI: 0.97-1.00) and 0.94, (95% CI: 0.79-0.99), respectively. Primary versus metastatic tumor identification yields sensitivity and specificity of 0.89, (95% CI: 0.83-0.93) and 0.87 (95% CI: 0.82-0.91), correspondingly. The differentiation of gliomas from pituitary tumors yielded the highest results among primary brain tumor classifications: sensitivity of 0.99 (95% CI: 0.99-1.00) and specificity of 0.99 (95% CI: 0.98-1.00).

CONCLUSIONS

ML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.

Multifaceted perspectives of detecting and targeting solid tumors

Solid tumors are the most prevalent form of cancer. Considerable technological and medical advancements had been achieved for the diagnosis of the disease. However, detection of the disease in an early stage is of utmost importance, still far from reality. On the contrary, the treatment and therapeutic area to combat solid tumors are still in its infancy. Conventional treatments like chemotherapy and radiation therapy pose challenges due to their indiscriminate impact on healthy and cancerous cells. Contextually, efficient drug targeting is a pivotal approach in solid tumor treatment. This involves the precise delivery of drugs to cancer cells while minimizing harm to healthy cells. Targeted drugs exhibit superior efficacy in eradicating cancer cells while impeding tumor growth and mitigate side effects by optimizing absorption which further diminishes the risk of resistance. Furthermore, tailoring targeted therapies to a patient's tumor-specific molecular profile augments treatment efficacy and reduces the likelihood of relapse. This chapter discuss about the distinctive characteristics of solid tumors, the possibility of early detection of the disease and potential therapeutic angle beyond the conventional approaches. Additionally, the chapter delves into a hitherto unknown attribute of magnetic field effect to target cancer cells which exploit the relatively less susceptibility of normal cells compared to cancer cells to magnetic fields, suggesting a future potential of magnetic nanoparticles for selective cancer cell destruction. Lastly, bioinformatics tools and other unconventional methodologies such as AI-assisted codon bias analysis have a crucial role in comprehending tumor biology, aiding in the identification of futuristic targeted therapies.

Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging

Background

Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations. Exploring the use of machine learning (ML) on magnetic resonance imaging to predict IDH mutation status shows promise but encounters challenges in generalizability and translation into clinical practice because most studies either use single institution or homogeneous datasets for model training and validation. Our study aims to bridge this gap by using multi-institution data for model validation.

Methods

This retrospective study utilizes data from large, annotated datasets for internal (377 cases from Yale New Haven Hospitals) and external validation (207 cases from facilities outside Yale New Haven Health). The 6-step research process includes image acquisition, semi-automated tumor segmentation, feature extraction, model building with feature selection, internal validation, and external validation. An extreme gradient boosting ML model predicted the IDH mutation status, confirmed by immunohistochemistry.

Results

The ML model demonstrated high performance, with an Area under the Curve (AUC), Accuracy, Sensitivity, and Specificity in internal validation of 0.862, 0.865, 0.885, and 0.713, and external validation of 0.835, 0.851, 0.850, and 0.847.

Conclusions

The ML model, built on a heterogeneous dataset, provided robust results in external validation for the prediction task, emphasizing its potential clinical utility. Future research should explore expanding its applicability and validation in diverse global healthcare settings.

Brain Tumors: Development, Drug Resistance, and Sensitization - An Epigenetic Approach

In this article, we describe contrasting developmental aspects of paediatric and adult brain tumours. We hypothesize that the formation of cancer progenitor cells, for both paediatric and adult, could be due to epigenetic events. However, the progression of adult brain tumours selectively involves more mutations compared to paediatric tumours. We further discuss epigenetic switches, comprising both histone modifications and DNA methylation, and how they can differentially regulate transcription and expression of oncogenes and tumour suppressor genes. Next, we summarize the currently available therapies for both types of brain tumours, explaining the merits and failures leading to drug resistance. We analyse different mechanisms of drug resistance and the role of epigenetics in this process. We then provide a rationale for combination therapy, which includes epigenetic drugs. In the end, we postulate a concept which describes how a combination therapy could be initiated. The timing, doses, and order of individual drug regimens will depend on the individual case. This type of combination therapy will be part of a personalized medicine which will differ from patient to patient.

Role of Permeability Surface Area Product in Grading of Brain Gliomas using CT Perfusion

Purpose  The aim of this study was to evaluate the role of permeability surface area product in grading brain gliomas using computed tomography (CT) perfusion Materials and Methods  CT perfusion was performed on 33 patients with brain glioma diagnosed on magnetic resonance imaging. Of these, 19 had high-grade glioma and 14 had low-grade glioma on histopathological follow-up. CT perfusion values were obtained and first compared between the tumor region and normal brain parenchyma. Then the relative values of perfusion parameters were compared between high- and low-grade gliomas. Cut-off values, sensitivity, specificity, and strength of agreement for each parameter were calculated and compared subsequently. A conjoint factor (permeability surface area product + cerebral blood volume) was also evaluated since permeability surface area product and cerebral blood volume are considered complimentary factors for tumor vascularity. Results  All five perfusion parameters namely permeability surface area product, cerebral blood volume, cerebral blood flow, mean transit time, and time to peak were found significantly higher in the tumor region than normal brain parenchyma. Among these perfusion parameters, only relative permeability surface area product and relative cerebral blood volume were found significant in differentiating high- and low-grade glioma. Moreover, relative permeability surface area product was significantly better than all other perfusion parameters with highest sensitivity and specificity (97.74 and 100%, respectively, at a cut-off of 9.0065). Relative permeability surface area product had a very good agreement with the histopathology grade. The conjoint factor did not yield any significant diagnostic advantage over permeability surface area product. Conclusion  Relative permeability surface area product and relative cerebral blood volume were helpful in differentiating high- and low-grade glioma; however, relative permeability surface area product was significantly better than all other perfusion parameters. Grading brain gliomas using relative permeability surface area product can add crucial value in their management and prognostication; hence, it should be evaluated in the routine CT perfusion imaging protocol.

Clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: neurosurgical and otolaryngologic conditions

BACKGROUND

Medical three dimensional (3D) printing is performed for neurosurgical and otolaryngologic conditions, but without evidence-based guidance on clinical appropriateness. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides appropriateness recommendations for neurologic 3D printing conditions.

METHODS

A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with neurologic and otolaryngologic conditions. Each study was vetted by the authors and strength of evidence was assessed according to published guidelines.

RESULTS

Evidence-based recommendations for when 3D printing is appropriate are provided for diseases of the calvaria and skull base, brain tumors and cerebrovascular disease. Recommendations are provided in accordance with strength of evidence of publications corresponding to each neurologic condition combined with expert opinion from members of the 3D printing SIG.

CONCLUSIONS

This consensus guidance document, created by the members of the 3D printing SIG, provides a reference for clinical standards of 3D printing for neurologic conditions.

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

Preclinical and Clinical Use of Indigenously Developed 99mTc-Diethylenetriaminepentaacetic Acid-Bis-Methionine: l-Type Amino Acid Transporter 1-Targeted Single Photon Emission Computed Tomography Radiotracer for Glioma Management

A new era in tumor classification, diagnosis, and prognostic evaluation has begun as a consequence of recent developments in the molecular and genetic characterization of central nervous system tumors. In this newly emerging era, molecular imaging modalities are essential for preoperative diagnosis, surgical planning, targeted treatment, and post-therapy evaluation of gliomas. The radiotracers are able to identify brain tumors, distinguish between low- and high-grade lesions, confirm a patient's eligibility for theranostics, and assess post-radiation alterations. We previously synthesized and reported the novel l-type amino acid transporter 1 (LAT-1)-targeted amino acid derivative in light of the use of amino acid derivatives in imaging technologies. Further, we have developed a single vial ready to label Tc-lyophilized kit preparations of diethylenetriaminepentaacetic acid-bis-methionine [DTPA-bis(Met)], also referred to as methionine-diethylenetriaminepentaacetic acid-methionine (MDM) and evaluated its imaging potential in numerous clinical studies. This review summarizes our previous publications on 99mTc-DTPA-bis(Met) in different clinical studies such as detection of breast cancer, as a prognostic marker, in detection of recurrent/residual gliomas, for differentiation of recurrent/residual gliomas from radiation necrosis, and for comparison of 99mTc-DTPA-bis(Met) with 11C-L-methionine (11C-MET), with relevant literature on imaging modalities in glioma management.

The Role of Cellular Immunity and Adaptive Immunity in Pathophysiology of Brain and Spinal Cord Tumors

Major advances have been made in our understanding of CNS tumors, especially glioma, however, the survival of patients with malignant glioma remains poor. While radiation and chemotherapy have increased overall survival, glioblastoma multiforme (GBM) still has one of the worst 5-year survival rates of all human cancers. Here, in this chapter, the authors review the abrogation of the immune system in the tumor setting, revealing many plausible targets for therapy and the current immunotherapy treatment strategies employed. Notably, glioma has also been characterized as a subset of primary spinal cord tumor and current treatment recommendations are outlined here.

DNA methylation array analysis for diffuse leptomeningeal glioneuronal tumor with conspicuous hypothalamic mass. A case report

Diffuse leptomeningeal glioneuronal tumor (DLGNT) is a rare glioneuronal neoplasm newly included in the 2016 World Health Organization Classification of Tumors of the Central Nervous System. Owing to the wide spectrum of its histopathological and radiological features, accurate diagnosis can be challenging. Recently, molecular testing including DNA methylation array has been introduced with the possibility of improving diagnostic accuracy and contributing to the subtyping especially for brain tumors with ambiguous histology. Two molecularly distinct subtypes of DLGNT have been reported: methylation class-1 (MC-1) with an indolent clinical course and MC-2, the latter aggressive. Herein, we report a case of a 14-year-old girl with a conspicuous hypothalamic mass lesion and diffuse leptomeningeal enhancement on magnetic resonance imaging. Biopsy specimens obtained from the hypothalamic lesion endoscopically were mainly composed of oligodendrocyte-like cells. However, it was difficult to make a definite diagnosis from these non-specific histological findings. Thus, DNA methylation array analysis was performed additionally by using formalin-fixed, paraffin-embedded tissue, resulting in a diagnosis of "MC-1 subtype of DLGNT" with a high calibrated score (0.99). Consequently, she was treated conservatively, with neither progression of the tumor nor aggravation of symptoms for the next 12 months. It was concluded that DNA methylation array analysis for DLGNT, a rare glioneuronal tumor, could be a powerful tool not only for accurate diagnosis but also decision-making in selecting the best treatment.

Major Changes in 2021 World Health Organization Classification of Central Nervous System Tumors

The World Health Organization (WHO) published the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5) in 2021, as an update of the WHO central nervous system (CNS) classification system published in 2016. WHO CNS5 was drafted on the basis of recommendations from the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) and expounds the classification scheme of the previous edition, which emphasized the importance of genetic and molecular changes in the characteristics of CNS tumors. Multiple newly recognized tumor types, including those for which there is limited knowledge regarding neuroimaging features, are detailed in WHO CNS5. The authors describe the major changes introduced in WHO CNS5, including revisions to tumor nomenclature. For example, WHO grade IV tumors in the fourth edition are equivalent to CNS WHO grade 4 tumors in the fifth edition, and diffuse midline glioma, H3 K27M-mutant, is equivalent to midline glioma, H3 K27-altered. With regard to tumor typing, isocitrate dehydrogenase (IDH)-mutant glioblastoma has been modified to IDH-mutant astrocytoma. In tumor grading, IDH-mutant astrocytomas are now graded according to the presence or absence of homozygous CDKN2A/B deletion. Moreover, the molecular mechanisms of tumorigenesis, as well as the clinical characteristics and imaging features of the tumor types newly recognized in WHO CNS5, are summarized. Given that WHO CNS5 has become the foundation for daily practice, radiologists need to be familiar with this new edition of the WHO CNS tumor classification system. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. ^©RSNA, 2022.

Comparison of Glioblastoma Outcomes in Two Geographically and Ethnically Distinct Patient Populations in Disparate Health Care Systems

Introduction  Variations in glioblastoma (GBM) outcomes between geographically and ethnically distinct patient populations has been rarely studied. To explore the possible similarities and differences, we performed a comparative analysis of GBM patients at the University of Kentucky (UK) in the United States and the Aga Khan University Hospital (AKUH) in Pakistan.

Methods  A retrospective review was conducted of consecutive patients who underwent surgery for GBM between January 2013 and December 2016 at UK, and July 2014 and December 2017 at AKUH. Patients with recurrent or multifocal disease on presentation and those who underwent only a biopsy were excluded. SPSS (v.25 IBM, Armonk, New York, United States) was used to collect and analyze data.

Results  Eighty-six patients at UK (mean age: 58.8 years; 37 [43%] < 60 years and 49 [57%] > 60 years) and 38 patients at AKUH (mean age: 49.1 years; 30 (79%) < 60 years and 8 (21%) > 60 years) with confirmed GBM were studied. At UK, median overall survival (OS) was 11.5 (95% confidence interval [CI]: 8.9–14) months, while at AKUH, median OS was 18 (95% CI: 13.9–22) months ( p  = 0.002). With gross-total resection (GTR), median OS at UK was 16 (95% CI: 9.5–22.4) months, whereas at AKUH, it was 24 (95% CI: 17.6–30.3) months ( p  = 0.011).

Conclusion  Median OS at UK was consistent with U.S. data but was noted to be longer at AKUH, likely due to a younger patient cohort and higher preoperative Karnofsky's performance scale (KPS). GTR, particularly in patients younger than 60 years of age and a higher preoperative KPS had a significant positive impact on OS and progression-free survival (PFS) at both institutions.

Diffuse Leptomeningeal Glioneuronal Tumour with 9-Year Follow-Up: Case Report and Review of the Literature

In 2016, the World Health Organisation Classification (WHO) of Tumours was updated with diffuse leptomeningeal glioneuronal tumour (DLGNT) as a provisional unit of mixed neuronal and glial tumours. Here, we report a DLGNT that has been re-diagnosed with the updated WHO classification, with clinical features, imaging, and histopathological findings and a 9-year follow-up. A 16-year-old girl presented with headache, vomiting, and vertigo. Magnetic resonance imaging (MRI) demonstrated a hyperintense mass with heterogenous enhancement in the right cerebellopontine angle and internal auditory canal. No leptomeningeal involvement was seen. The histological examination revealed neoplastic tissue of moderate cellularity formed mostly by oligodendrocyte-like cells. Follow-up MRI scans demonstrated cystic lesions in the subarachnoid spaces in the brain with vivid leptomeningeal enhancement. Later spread of the tumour was found in the spinal canal. On demand biopsy samples were re-examined, and pathological diagnosis was identified as DLGNT. In contrast to most reported DLGNTs, the tumour described in this manuscript did not present with diffuse leptomeningeal spread, but later presented with leptomeningeal involvement in the brain and spinal cord. Our case expands the spectrum of radiological features, provides a long-term clinical and radiological follow-up, and highlights the major role of molecular genetic testing in unusual cases.

Diffusion and perfusion imaging biomarkers of H3 K27M mutation status in diffuse midline gliomas

PURPOSE

H3K27M-mutant diffuse midline gliomas (M-DMGs) exhibit a clinically aggressive course. We studied diffusion-weighted imaging (DWI) and perfusion (PWI) MRI features of DMG with the hypothesis that DWI-PWI metrics can serve as biomarkers for the prediction of the H3K27M mutation status in DMGs.

METHODS

A retrospective review of the institutional database (imaging and histopathology) of patients with DMG (July 2016 to July 2020) was performed. Tumoral apparent diffusion coefficient (ADC) and peritumoral ADC (PT ADC) values and their normalized values (nADC and nPT ADC) were computed. Perfusion data were analyzed with manual arterial input function (AIF) and leakage correction (LC) Boxerman-Weiskoff models. Normalized maximum relative CBV (rCBV) was evaluated. Intergroup analysis of the imaging variables was done between M-DMGs and wild-type (WT-DMGs) groups.

RESULTS

Ninety-four cases (M-DMGs-n = 48 (51%) and WT-DMGs-n = 46(49%)) were included. Significantly lower PT ADC (mutant-1.1 ± 0.33, WT-1.23 ± 0.34; P = 0.033) and nPT ADC (mutant-1.64 ± 0.48, WT-1.83 ± 0.54; P = 0.040) were noted in the M-DMGs. The rCBV (mutant-25.17 ± 27.76, WT-13.73 ± 14.83; P = 0.018) and nrCBV (mutant-3.44 ± 2.16, WT-2.39 ± 1.25; P = 0.049) were significantly higher in the M-DMGs group. Among thalamic DMGs, the min ADC, PT ADC, and nADC and nPT ADC were lower in M-DMGs while nrCBV (corrected and uncorrected) was significantly higher. Receiver operator characteristic curve analysis demonstrated that PT ADC (cut-off-1.245), nPT ADC (cut-off-1.853), and nrCBV (cut-off-1.83) were significant independent predictors of H3K27M mutational status in DMGs.

CONCLUSION

DWI and PWI features hold value in preoperative prediction of H3K27M-mutation status in DMGs.

An Overview of Nanotechnologies for Drug Delivery to the Brain

Drug delivery to the brain has been one of the toughest challenges researchers have faced to develop effective treatments for brain diseases. Owing to the blood–brain barrier (BBB), only a small portion of administered drug can reach the brain. A consequence of that is the need to administer a higher dose of the drug, which, expectedly, leads to a variety of unwanted side effects. Research in a variety of different fields has been underway for the past couple of decades to address this very serious and frequently lethal problem. One area of research that has produced optimistic results in recent years is nanomedicine. Nanomedicine is the science birthed by fusing the fields of nanotechnology, chemistry and medicine into one. Many different types of nanomedicine-based drug-delivery systems are currently being studied for the sole purpose of improved drug delivery to the brain. This review puts together and briefly summarizes some of the major breakthroughs in this crusade. Inorganic nanoparticle-based drug-delivery systems, such as gold nanoparticles and magnetic nanoparticles, are discussed, as well as some organic nanoparticulate systems. Amongst the organic drug-delivery nanosystems, polymeric micelles and dendrimers are discussed briefly and solid polymeric nanoparticles are explored in detail.

Preoperative Contrast-Enhanced MRI in Differentiating Glioblastoma From Low-Grade Gliomas in The Cancer Imaging Archive Database: A Proof-of-Concept Study

Purpose

This study aimed to develop a repeatable MRI-based machine learning model to differentiate between low-grade gliomas (LGGs) and glioblastoma (GBM) and provide more clinical information to improve treatment decision-making.

Methods

Preoperative MRIs of gliomas from The Cancer Imaging Archive (TCIA)–GBM/LGG database were selected. The tumor on contrast-enhanced MRI was segmented. Quantitative image features were extracted from the segmentations. A random forest classification algorithm was used to establish a model in the training set. In the test phase, a random forest model was tested using an external test set. Three radiologists reviewed the images for the external test set. The area under the receiver operating characteristic curve (AUC) was calculated. The AUCs of the radiomics model and radiologists were compared.

Results

The random forest model was fitted using a training set consisting of 142 patients [mean age, 52 years ± 16 (standard deviation); 78 men] comprising 88 cases of GBM. The external test set included 25 patients (14 with GBM). Random forest analysis yielded an AUC of 1.00 [95% confidence interval (CI): 0.86–1.00]. The AUCs for the three readers were 0.92 (95% CI 0.74–0.99), 0.70 (95% CI 0.49–0.87), and 0.59 (95% CI 0.38–0.78). Statistical differences were only found between AUC and Reader 1 (1.00 vs. 0.92, respectively; p = 0.16).

Conclusion

An MRI radiomics-based random forest model was proven useful in differentiating GBM from LGG and showed better diagnostic performance than that of two inexperienced radiologists.

Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

Automatic brain tumor classification is a practicable means of accelerating clinical diagnosis. Recently, deep convolutional neural network (CNN) training with MRI datasets has succeeded in computer-aided diagnostic (CAD) systems. To further improve the classification performance of CNNs, there is still a difficult path forward with regards to subtle discriminative details among brain tumors. We note that the existing methods heavily rely on data-driven convolutional models while overlooking what makes a class different from the others. Our study proposes to guide the network to find exact differences among similar tumor classes. We first present a "dual suppression encoding" block tailored to brain tumor MRIs, which diverges two paths from our network to refine global orderless information and local spatial representations. The aim is to use more valuable clues for correct classes by reducing the impact of negative global features and extending the attention of salient local parts. Then we introduce a "factorized bilinear encoding" layer for feature fusion. The aim is to generate compact and discriminative representations. Finally, the synergy between these two components forms a pipeline that learns in an end-to-end way. Extensive experiments exhibited superior classification performance in qualitative and quantitative evaluation on three datasets.

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model

Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

OBJECTIVES

Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods.

METHODS

A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033).

RESULTS

After the literature search (n = 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82-0.86). In addition, a DSC score of 0.83 (95% CI: 0.80-0.87) and 0.82 (95% CI: 0.78-0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed.

CONCLUSION

MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.

KEY POINTS

• MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82-0.86), indicating a good performance. • MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas. • For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.

Diffuse leptomeningeal glioneuronal tumor with high-grade features masquerading as tubercular meningitis—a case report

Background

Diffuse leptomeningeal glioneuronal tumor (DLGNT) has been recently described in the literature. The complete neuroimaging spectrum and histopathological characteristics of this entity are yet to be elucidated. In an endemic region, diffuse leptomeningeal enhancement on neuroimaging with associated communicating hydrocephalus is usually suggestive of infective meningitis and the patients are started on empirical anti-microbial therapy. However, it is important to consider other differential diagnosis of leptomeningeal enhancement in such cases, particularly if the clinical condition does not improve on anti-microbial therapy. An early diagnosis of a neoplastic etiology may be of particular importance as the treatment regimens vary considerably depending on the underlying disease condition.

Case presentation

In this case report, we describe a case of DLGNT with high-grade histopathological features which was initially managed as tubercular meningitis based on the initial neuroimaging findings. Due to worsening of the clinical course and subsequent imaging findings at follow-up, a diagnosis of DLGNT was considered and subsequently proven to be DLGNT with features of anaplasia on histopathological examination of leptomeningeal biopsy specimen.

Conclusion

This case highlights the importance of recognizing certain subtle finding on MRI which may help in an early diagnosis of DLGNT which is crucial for appropriate treatment.

Artificial Intelligence Algorithm-Based Analysis of Ultrasonic Imaging Features for Diagnosis of Pregnancy Complicated with Brain Tumor

This research was to explore the accuracy of ultrasonic diagnosis based on artificial intelligence algorithm in the diagnosis of pregnancy complicated with brain tumors. In this study, 18 patients with pregnancy complicated with brain tumor confirmed by pathology were selected as the research object. Ultrasound contrast based on artificial bee colony algorithm was performed and diagnosed by experienced clinicians. Ultrasonic image will be reconstructed by artificial bee colony algorithm to improve its image display ability. The pathological diagnosis will be handed over to the physiological pathology laboratory of the hospital for diagnosis. The doctor's ultrasonic diagnosis results were compared with the pathological diagnosis stage results of patients, and the results were analyzed by statistical analysis to evaluate its diagnostic value. The comparison results showed that the number and classification of benign tumors were the same, while in malignant tumors, the number diagnosis was the same, but there was one patient with diagnostic error in classification. One case of mixed glial neuron tumor was diagnosed as glial neuron tumor, and the diagnostic accuracy was 94.44% and the K value was 0.988. The diagnostic results of the two were in excellent agreement. The results show that, in the ultrasonic image diagnosis of patients with brain tumors during pregnancy based on artificial intelligence algorithm, most of them are benign and have obvious symptoms. Ultrasound has a good diagnostic accuracy and can be popularized in clinical diagnosis. The results can provide experimental data for the clinical application of ultrasonic image feature analysis based on artificial intelligence as the diagnosis of pregnancy complicated with brain tumors.

Consensus core clinical data elements for meningiomas

BACKGROUND

With increasing molecular analyses of meningiomas, there is a need to harmonize language used to capture clinical data across centers to ensure that molecular alterations are appropriately linked to clinical variables of interest. Here the International Consortium on Meningiomas presents a set of core and supplemental meningioma-specific Common Data Elements (CDEs) to facilitate comparative and pooled analyses.

METHODS

The generation of CDEs followed the four-phase process similar to other National Institute of Neurological Disorders and Stroke (NINDS) CDE projects: discovery, internal validation, external validation, and distribution.

RESULTS

The CDEs were organized into patient- and tumor-level modules. In total, 17 core CDEs (10 patient-level and 7-tumour-level) as well as 14 supplemental CDEs (7 patient-level and 7 tumour-level) were defined and described. These CDEs are now made publicly available for dissemination and adoption.

CONCLUSIONS

CDEs provide a framework for discussion in the neuro-oncology community that will facilitate data sharing for collaborative research projects and aid in developing a common language for comparative and pooled analyses. The meningioma-specific CDEs presented here are intended to be dynamic parameters that evolve with time and The Consortium welcomes international feedback for further refinement and implementation of these CDEs.

Brain tumor detection and classification using machine learning: a comprehensive survey

Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

Case Report: Identification of a Novel GNAS Mutation and 1p/22q Co-Deletion in a Patient With Multiple Recurrent Meningiomas Sensitive to Sunitinib

Background

Although surgical resection can cure the majority of meningiomas, there are still approximately 20% of patients suffering from an aggressive course with recurrence or progression. In this study, we reported a novel GNAS mutation and 1p/22q co-deletion responding to sunitinib in a patient with multiple recurrent meningiomas.

Case Presentation

A 53-year-old woman with meningioma was hospitalized due to postoperative tumor progression for 3 weeks. WHO grade I meningioma was pathologically diagnosed after the first three surgeries, but the second recurrence occurred approximately 3 years following the third surgery. Next-generation sequencing was performed on the first two recurrent samples. GNAS mutations and 1p/22q co-deletion were both identified, and amplification at 17q and chromosome 19 was also found in the second recurrent sample, based on which WHO grade II/III meningioma was diagnosed. The lesion in the left cerebellopontine angle area enlarged after use of radiotherapy combined with temozolomide chemotherapy for 2 months. When sunitinib was added, the residual lesions began to lessen and continuously reduced.

Conclusion

This typical case suggested that timely molecular diagnosis for refractory meningiomas contributed to guiding the molecular classification and clinicians to make more reasonable individualized therapeutic regimens, consequently benefiting the patients. This case report also highlighted the potential role of sunitinib in the treatment of refractory meningiomas.

A Survey of Brain Tumor Segmentation and Classification Algorithms

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models' performance evaluation metrics.

Histologic Distribution and Characteristics on MR Imaging of Ultrasmall Superparamagnetic Iron Oxide in Ethyl-nitrosourea-induced Endogenous Rat Glioma

PURPOSE

(1) To evaluate the enhancement patterns of an ultrasmall superparamagnetic iron oxide contrast agent (USPIO-CA) compared with those of a gadolinium-based contrast agent (Gd-BCA). (2) To compare the histologic distribution of USPIO-related iron particles (USPIO-IPs) with the USPIO-enhancement area in the early vascular and in the cellular imaging phase (E- and L-phase, respectively) after intravenous CA administration.

METHODS

We performed USPIO-enhanced MRI of N-ethyl-N-nitrosourea (ENU)-induced endogenous rat glioma, including spin-echo (SE) T₁-weighted images (T₁WIs) and gradient-recalled-echo (GRE) T₂-weighted images (T₂WIs), before and at 3-6 h after USPIO-CA administration for E-phase images. For L-phase images, MRI was performed at 16-19 and 62-69 h after administration. Two observers determined the USPIO-enhancement area on E-phase images and Gd-enhancement areas. We compared the USPIO-enhancement size (USPIO-ES) and Gd-ES on SE T₁WIs, and the hypo-intense USPIO-ES on GRE T₂WIs and Gd-ES using the Wilcoxon signed-rank test. In addition, two raters visually evaluated the correspondence between the histologic distribution of USPIO-IPs and the USPIO-enhancement area on corresponding GRE T₂WIs at each phase using a 3-rating scale.

RESULTS

Significantly smaller hyper-intense, hypo-intense and combined hyper-/hypo-intense areas were observed on USPIO-enhanced SE T₁WIs compared with Gd-enhanced images (all P < 0.001). The hypo-intense USPIO-ES on GRE T₂WIs was significantly smaller than the Gd-ES (P = 0.001). The distribution of USPIO-IPs on histopathological specimen and USPIO-enhancement on GRE T₂WIs exhibited poor agreement in 5 of 9 tumors with enhancement from rats sacrificed early. The distribution of microglia containing USPIO-IPs corresponded with the pattern of USPIO-enhancement in the 2 tumors with late enhancement.

CONCLUSION

The enhancement pattern and size of USPIO-CA in a rat glioma model were statistically different from those of Gd-BCA. Our histological data suggests that USPIO-enhanced MRI offers vascular bed imaging in E-phase and might depict the intra-tumoral distribution of immune effector cells in L-phase.

Radiomics and radiogenomics in gliomas: a contemporary update

The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades

The grading of glioma has clinical significance in determining a treatment strategy and evaluating prognosis to investigate a novel set of radiomic features extracted from the fractional anisotropy (FA) and mean diffusivity (MD) maps of brain diffusion tensor imaging (DTI) sequences for computer-aided grading of gliomas. This retrospective study included 108 patients who had pathologically confirmed brain gliomas and DTI scanned during 2012-2018. This cohort included 43 low-grade gliomas (LGGs; all grade II) and 65 high-grade gliomas (HGGs; grade III or IV). We extracted a set of radiomic features, including traditional texture, morphological, and novel deep features derived from pre-trained convolutional neural network models, in the manually-delineated tumor regions. We employed support vector machine and these radiomic features for two classification tasks: LGGs vs HGGs, and grade III vs IV. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity was reported as the performance metrics using the leave-one-out cross-validation method. When combining FA+MD, AUC = 0.93, accuracy = 0.94, sensitivity = 0.98, and specificity = 0.86 in classifying LGGs from HGGs, while AUC = 0.99, accuracy = 0.98, sensitivity = 0.98, and specificity = 1.00 in classifying grade III from IV. The AUC and accuracy remain close when features were extracted from only the solid tumor or additionally including necrosis, cyst, and peritumoral edema. Still, the effects in terms of sensitivity and specificity are mixed. Deep radiomic features derived from pre-trained convolutional neural networks showed higher prediction ability than the traditional texture and shape features in both classification experiments. Radiomic features extracted on the FA and MD maps of brain DTI images are useful for noninvasively classification/grading of LGGs vs HGGs, and grade III vs IV.

Early stage glioblastoma: retrospective multicentric analysis of clinical and radiological features

OBJECTIVES

The aim of this study was to report our experience with early stage glioblastoma (e-GB) and to investigate the possible clinical and imaging features that may be helpful to the radiologist to correctly diagnose this entity.

METHODS

We performed a retrospective research of patients diagnosed with glioblastoma at two hospitals during a 10-year period. We reviewed all pre-operative MR and included only patients with early stage GB lesions, characterized by hyperintense on T2-weighted signal, with or without contrast-enhancement at post-contrast T1-weighted images, without "classic" imaging appearance of GB (necrosis, haemorrhage, oedema). All preoperative MR were evaluated by an experienced neuroradiologist and information on patients' demographics, clinical presentation, follow-up, and histopathology results study were collected. When available, preoperative CT examination was also evaluated.

RESULTS

We found 14 e-GBs in 13 patients (9 males, 4 females, median age 63 years) among 660 patients diagnosed with GB between 2010 and 2020. In 10 lesions, serial imaging revealed the transformation of e-GB in classic glioblastoma in a median time of 3 months. Clinical presentation included stroke-like symptoms, vertigo, seizures and confusion. Preoperative plain CT was performed in 8/13 cases and in 7 e-GBs presented as a hyperdense lesion. Ten out of 14 lesions transformed in classic GB before surgical intervention or biopsy. All lesions revealed typical immunohistochemical pattern of primary glioblastoma.

CONCLUSIONS

E-GB is a rare entity that can often lead to misdiagnosis. However, the radiologist should be aware of its imaging appearance to suggest the diagnosis and to request close imaging follow-up, hopefully improving the prognosis of this very aggressive disease.

Characterisation of isocitrate dehydrogenase gene mutant WHO grade 2 and 3 gliomas: MRI predictors of 1p/19q co-deletion and tumour grade

AIM

To identify imaging predictors of molecular subtype and tumour grade in patients with isocitrate dehydrogenase (IDH) gene mutant (IDH_mut) World Health Organization (WHO) grade 2 or 3 gliomas.

MATERIALS AND METHODS

Patients with histologically confirmed WHO grade 2 or 3 IDH_mut gliomas between 2016 and 2019 were included in the study. Magnetic resonance imaging (MRI) images were evaluated for the presence or absence of potential imaging predictors of tumour subtype, such as T2/fluid attenuated inversion recovery (FLAIR) signal match, and these factors were examined using regression analysis. On perfusion imaging, the maximum relative cerebral blood volume (rCBV_max) was evaluated as a potential predictor of tumour grade. The performance of two experienced neuroradiologists in correctly predicting tumour type on MRI was evaluated.

RESULTS

Eighty-five patients were included in the study. The presence of T2/FLAIR signal match >50% of tumour volume (p<0.01) and intratumoural susceptibility (p=0.02) were independent predictors of 1p/19q co-deletion. Mean rCBV max was significantly higher in WHO grade 3 astrocytomas (p=0.04) than WHO grade 2 astrocytomas. The consensus prediction of 1p/19q co-deletion status by two neuroradiologists of tumour was 95% sensitive and 86% specific.

CONCLUSION

The presence of matched T2/FLAIR signal could be used to identify tumour subtype when biopsy is inconclusive or genetic analysis is unavailable. rCBV_max predicted astrocytoma grade. Experienced neuroradiologists predict tumour subtype with good sensitivity and specificity.

T2/FLAIR hyperintensity in the mesial temporal lobe: challenging differential diagnosis

T2/FLAIR hyperintensity in the mesial temporal lobe is the most common MR finding of herpes simplex encephalitis but may be observed in other infectious and non-infectious diseases. The former includes herpes human virus 6 encephalitis, Japanese encephalitis, and neurosyphilis, and the latter autoimmune encephalitis, gliomatosis cerebri, bilateral or paradoxical posterior cerebral artery infarction, status epilepticus, and hippocampal sclerosis. Thus, T2/FLAIR hyperintensity in the mesial temporal lobe is not a disease-specific magnetic resonance imaging finding, and these conditions must be differentiated to ensure proper treatment. We review diseases that are presented with T2/FLAIR hyperintensity in the mesial temporal lobe and provide a helpful flow chart based on clinical and radiologic features.

Exploring diagnostic performance of T2 mapping in diffuse glioma grading

Background

To evaluate the diagnostic performance of T2 mapping in differentiating WHO grade II glioma from high-grade glioma (HGG).

Methods

We conducted a single-center, retrospective diagnostic study. Confirmed diffuse glioma (WHO grade II-IV) patients who underwent post-contrast T1-weighted imaging, T2-weighted imaging, and T2 mapping were included. All diagnoses were based on histological and molecular tests. Seventy-five percent of cases were subsampled to generate receiver operating characteristic (ROC) curves and areas under the curve (AUC), while the remaining cases were used to test the accuracy of T2 mapping. Subsampling was repeated four times. Age, T2 relaxation time, and contrast-enhancement status were used to generate a multivariable ROC curve. T2 relaxation time was also used to generate ROC curves to predict the isocitrate dehydrogenase (IDH) status.

Results

A total of 159 patients were included in the study. After four repeats of subsampling, the AUCs of the T2 mapping ROC curve were 0.801 (95% CI: 0.724-0.879), 0.795 (95% CI: 0.714-0.875), 0.803 (95% CI: 0.723-0.884), and 0.801 (95% CI: 0.716-0.886), with an average sensitivity of 0.753 and an average specificity of 0.767. When applied to the remaining 25% of cases, the accuracy was 75%, 93.75%, 82.50%, and 71.74%. The AUC of the multivariable ROC was 0.927 (95% CI: 0.882-0.971). IDH-mutant and IDH-wildtype gliomas have significantly different T2 relaxation times (146.28 and 124.10 ms, respectively; P=0.001), and the AUC of IDH-mutant prediction was 0.687 (95% CI: 0.585-0.789).

Conclusions

Quantitative T2 mapping differentiated WHO grade II glioma from HGG with moderate sensitivity and specificity. Given the advantages of short acquisition times and the absence of a contrast agent, our study suggests the application of T2 mapping in pre-operative glioma grading is feasible.

Prediction of H3K27M mutation status of diffuse midline gliomas using MRI features

BACKGROUND AND PURPOSE

Presurgical prediction of H3K27M mutation in diffuse midline gliomas (DMGs) on MRI is desirable. The purpose of this study is to elaborate conventional MRI (cMRI) features of H3K27M-mutant DMGs and identify features that could discriminate them from wild-type (WT) DMGs.

METHODS

CMRI features of 123 patients with DMG were evaluated conforming to the institutional research protocols. Multimodality MRI was performed on 1.5 or 3.0 Tesla MR Scanners with imaging protocol, including T1-weighted (w), T2w, fluid-attenuated inversion recovery, diffusion-weighted, susceptibility-weighted, and postcontrast T1w sequences. Pertinent cMRI features were annotated along the lines of Visually AcceSAble Rembrandt Images features, and Intra Tumoral Susceptibility Signal score (ITSS) was evaluated. R software was used for statistical analysis.

RESULTS

Sixty-one DMGs were H3K27M-mutant (mutant DMGs). The patients in the H3K27M-mutant DMG group were younger compared to the WT-DMG group (mean age 24.13 ± 13.13 years vs. 35.79±18.74 years) (p = 0.016). The two groups differed on five cMRI features--(1) enhancement quality (p = 0.032), (2) thickness of enhancing margin (p = 0.05), (3) proportion of edema (p = 0.002), (4) definition of noncontrast-enhancing tumor (NCET) margin (p = 0.001), and (5) cortical invasion (p = 0.037). The mutant DMGs showed greater enhancement and greater thickness of enhancing margin, while the WT DMGs exhibited significantly larger edema proportion with poorly defined NCET margins and cortical invasion. ITSS was not significantly different among the groups.

CONCLUSION

CMRI features like enhancement quality, the thickness of the enhancing margin, proportion of edema, definition of NCET margin, and cortical invasion can discriminate between the H3K27M-mutant and WT DMGs.

Challenges and opportunities for artificial intelligence in oncological imaging

Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qualitative in nature with some relatively crude size-based measurements of tumours for assessment of disease progression or treatment response; however, it is increasingly understood that there may be significantly more information about oncological disease that can be obtained from imaging that is not currently utilized. Artificial intelligence (AI) has the potential to harness quantitative techniques to improve oncological imaging. These may include improving the efficiency or accuracy of traditional roles of imaging such as diagnosis or detection. These may also include new roles for imaging such as risk-stratifying patients for different types of therapy or determining biological tumour subtypes. This review article outlines several major areas in oncological imaging where there may be opportunities for AI technology. These include (1) screening and detection of cancer, (2) diagnosis and risk stratification, (3) tumour segmentation, (4) precision oncology, and (5) predicting prognosis and assessing treatment response. This review will also address some of the potential barriers to AI research in oncological imaging.

Salvage Boron Neutron Capture Therapy for Malignant Brain Tumor Patients in Compliance with Emergency and Compassionate Use: Evaluation of 34 Cases in Taiwan

Although boron neutron capture therapy (BNCT) is a promising treatment option for malignant brain tumors, the optimal BNCT parameters for patients with immediately life-threatening, end-stage brain tumors remain unclear. We performed BNCT on 34 patients with life-threatening, end-stage brain tumors and analyzed the relationship between survival outcomes and BNCT parameters. Before BNCT, MRI and 18F-BPA-PET analyses were conducted to identify the tumor location/distribution and the tumor-to-normal tissue uptake ratio (T/N ratio) of 18F-BPA. No severe adverse events were observed (grade ≥ 3). The objective response rate and disease control rate were 50.0% and 85.3%, respectively. The mean overall survival (OS), cancer-specific survival (CSS), and relapse-free survival (RFS) times were 7.25, 7.80, and 4.18 months, respectively. Remarkably, the mean OS, CSS, and RFS of patients who achieved a complete response were 17.66, 22.5, and 7.50 months, respectively. Kaplan-Meier analysis identified the optimal BNCT parameters and tumor characteristics of these patients, including a T/N ratio ≥ 4, tumor volume < 20 mL, mean tumor dose ≥ 25 Gy-E, MIB-1 ≤ 40, and a lower recursive partitioning analysis (RPA) class. In conclusion, for malignant brain tumor patients who have exhausted all available treatment options and who are in an immediately life-threatening condition, BNCT may be considered as a therapeutic approach to prolong survival.

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.

The potential of metformin as an antineoplastic in brain tumors: A systematic review

Brain tumors are challenging to handle and cause severe mortality and morbidity. The primary therapy for brain tumors, a combination of radiotherapy, chemotherapy (i.e temozolomide), and corticosteroids, is considered inadequate to improve patients' clinical conditions and associated with many adverse effects. There is an urgent need for new compounds or repurposing of existing therapies, which could improve brain tumor patients' prognosis. Metformin, commonly used for type 2 diabetes medication, has been examined for its protective action in cancer, reducing cancer risk and cancer-related mortality. However, its effect on cancer is still in rigorous debate. This study examines recent studies on the effects of metformin in primary brain tumor patients through systematic reviews. The literature search was performed on PubMed, ScienceDirect, and SpringerLink databases for articles published between 2013 and 2020. We selected clinical studies comparing the therapeutic outcomes of brain tumor therapy with and without metformin. The clinical benefits of the drug were assessed through the overall survival (OS) and progression-free survival (PFS) of brain tumor patients. Those studies demonstrated that the combination of metformin with temozolomide given post-radiotherapy resulted in better OS and PFS. Nonetheless, the efficacy and safety of metformin need further clinical testing in the wider population.

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma

BACKGROUND

Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age.

METHODS

In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers.

RESULTS

Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification.

CONCLUSIONS

We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.

m6A Modifications Play Crucial Roles in Glial Cell Development and Brain Tumorigenesis

RNA methylation is a reversible post-transcriptional modification to RNA and has a significant impact on numerous biological processes. N⁶-methyladenosine (m⁶A) is known as one of the most common types of eukaryotic mRNA methylation modifications, and exists in a wide variety of organisms, including viruses, yeast, plants, mice, and humans. Widespread and dynamic m⁶A methylation is identified in distinct developmental stages in the brain, and controls development of neural stem cells and their differentiation into neurons, glial cells such as oligodendrocytes and astrocytes. Here we summarize recent advances in our understanding of RNA methylation regulation in brain development, neurogenesis, gliogenesis, and its dysregulation in brain tumors. This review will highlight biological roles of RNA methylation in development and function of neurons and glial cells, and provide insights into brain tumor formation, and diagnostic and treatment strategies.

Adult Primary Brain Neoplasm, Including 2016 World Health Organization Classification

In 2016, the World Health Organization (WHO) central nervous system (CNS) classification scheme incorporated molecular parameters in addition to traditional microscopic features for the first time. Molecular markers add a level of objectivity that was previously missing for tumor categories heavily dependent on microscopic observation for pathologic diagnosis. This article provides a brief discussion of the major 2016 updates to the WHO CNS classification scheme and reviews typical MR imaging findings of adult primary CNS neoplasms, including diffuse infiltrating gliomas, ependymal tumors, neuronal/glioneuronal tumors, pineal gland tumors, meningiomas, nerve sheath tumors, solitary fibrous tumors, and lymphoma.