Magnetic Resonance Imaging-Based Screening for Asymptomatic Brain Tumors: A Review

Catalytic Nanoparticles in Biomedical Applications: Exploiting Advanced Nanozymes for Therapeutics and Diagnostics

Catalytic nanoparticles (CNPs) as heterogeneous catalyst reveals superior activity due to their physio-chemical features, such as high surface-to-volume ratio and unique optical, electric, and magnetic properties. The CNPs, based on their physio-chemical nature, can either increase the reactive oxygen species (ROS) level for tumor and antibacterial therapy or eliminate the ROS for cytoprotection, anti-inflammation, and anti-aging. In addition, the catalytic activity of nanozymes can specifically trigger a specific reaction accompanied by the optical feature change, presenting the feasibility of biosensor and bioimaging applications. Undoubtedly, CNPs play a pivotal role in pushing the evolution of technologies in medical and clinical fields, and advanced strategies and nanomaterials rely on the input of chemical experts to develop. Herein, a systematic and comprehensive review of the challenges and recent development of CNPs for biomedical applications is presented from the viewpoint of advanced nanomaterial with unique catalytic activity and additional functions. Furthermore, the biosafety issue of applying biodegradable and non-biodegradable nanozymes and future perspectives are critically discussed to guide a promising direction in developing span-new nanozymes and more intelligent strategies for overcoming the current clinical limitations.

Immunological processes of enhancers and suppressors of long non-coding RNAs associated with brain tumors and inflammation

Immunological processes, such as inflammation, can both cause tumor suppression and cancer progression. Moreover, deregulated levels of long non-coding RNA (lncRNA) expression in the brain may cause inflammation and lead to the growth of tumors. Like other biological processes, the immune system's role in cancer is complicated, varies, and can help or hurt the cancer's maintenance. According to research, inflammation and brain cancer are correlated via several signaling pathways. A variety of lncRNAs have recently been revealed to influence cancer by modulating inflammatory pathways. As a result, lncRNAs have the potential to influence carcinogenesis, tumor formation, or tumor suppression via an increase or decrease in inflammation functions. Although the study and targeting of lncRNAs have made great progress in the treatment of cancer, there are definitely limitations and challenges. Using new technologies like nanocarriers and cell-penetrating peptides (CPPs) to target treatments without hurting healthy body tissues has shown to be very effective. In this review article, we have collected significantly related lncRNAs and their inhibitory or stimulating roles in inflammation and brain cancer for the first time. However, there are limitations, such as side effects and damage to normal tissues. With the advancement of new targeting technologies, these lncRNAs may be candidates for the specific targeting therapy of brain cancers by limiting inflammation or stimulating the immune system against them in the future.

A review on potential heterocycles for the treatment of glioblastoma targeting receptor tyrosine kinases

Glioblastoma, the most aggressive form of brain tumor, poses significant challenges in terms of treatment success and patient survival. Current treatment modalities for glioblastoma include radiation therapy, surgical intervention, and chemotherapy. Unfortunately, the median survival rate remains dishearteningly low at 12-15 months. One of the major obstacles in treating glioblastoma is the recurrence of tumors, making chemotherapy the primary approach for secondary glioma patients. However, the efficacy of drugs is hampered by the presence of the blood-brain barrier and multidrug resistance mechanisms. Consequently, considerable research efforts have been directed toward understanding the underlying signaling pathways involved in glioma and developing targeted drugs. To tackle glioma, numerous studies have examined kinase-downstream signaling pathways such as RAS-RAF-MEK-ERK-MPAK. By targeting specific signaling pathways, heterocyclic compounds have demonstrated efficacy in glioma therapeutics. Additionally, key kinases including phosphatidylinositol 3-kinase (PI3K), serine/threonine kinase, cytoplasmic tyrosine kinase (CTK), receptor tyrosine kinase (RTK) and lipid kinase (LK) have been considered for investigation. These pathways play crucial roles in drug effectiveness in glioma treatment. Heterocyclic compounds, encompassing pyrimidine, thiazole, quinazoline, imidazole, indole, acridone, triazine, and other derivatives, have shown promising results in targeting these pathways. As part of this review, we propose exploring novel structures with low toxicity and high potency for glioma treatment. The development of these compounds should strive to overcome multidrug resistance mechanisms and efficiently penetrate the blood-brain barrier. By optimizing the chemical properties and designing compounds with enhanced drug-like characteristics, we can maximize their therapeutic value and minimize adverse effects. Considering the complex nature of glioblastoma, these novel structures should be rigorously tested and evaluated for their efficacy and safety profiles.

Age, Motion, Medical, and Psychiatric Associations With Incidental Findings in Brain MRI

Importance

Few investigations have evaluated rates of brain-based magnetic resonance imaging (MRI) incidental findings (IFs) in large lifespan samples, their stability over time, or their associations with health outcomes.

Objectives

To examine rates of brain-based IFs across the lifespan, their persistence, and their associations with phenotypic indicators of behavior, cognition, and health; to compare quantified motion with radiologist-reported motion and evaluate its associations with IF rates; and to explore IF consistency across multiple visits.

Design, Setting, and Participants

This cross-sectional study included participants from the Nathan Kline Institute-Rockland Sample (NKI-RS), a lifespan community-ascertained sample, and the Healthy Brain Network (HBN), a cross-sectional community self-referred pediatric sample focused on mental health and learning disorders. The NKI-RS enrolled participants (ages 6-85 years) between March 2012 and March 2020 and had longitudinal participants followed up for as long as 4 years. The HBN enrolled participants (ages 5-21 years) between August 2015 and October 2021. Clinical neuroradiology MRI reports were coded for radiologist-reported motion as well as presence, type, and clinical urgency (category 1, no abnormal findings; 2, no referral recommended; 3, consider referral; and 4, immediate referral) of IFs. MRI reports were coded from June to October 2021. Data were analyzed from November 2021 to February 2023.

Main Outcomes and Measures

Rates and type of IFs by demographic characteristics, health phenotyping, and motion artifacts; longitudinal stability of IFs; and Euler number in projecting radiologist-reported motion.

Results

A total of 1300 NKI-RS participants (781 [60.1%] female; mean [SD] age, 38.9 [21.8] years) and 2772 HBN participants (976 [35.2%] female; mean [SD] age, 10.0 [3.5] years) had health phenotyping and neuroradiology-reviewed MRI scans. IFs were common, with 284 of 2956 children (9.6%) and 608 of 1107 adults (54.9%) having IFs, but rarely of clinical concern (category 1: NKI-RS, 619 [47.6%]; HBN, 2561 [92.4%]; category 2: NKI-RS, 647 [49.8%]; HBN, 178 [6.4%]; category 3: NKI-RS, 79 [6.1%]; HBN, 30 [1.1%]; category 4: NKI-RS: 12 [0.9%]; HBN, 6 [0.2%]). Overall, 46 children (1.6%) and 79 adults (7.1%) required referral for their IFs. IF frequency increased with age. Elevated blood pressure and BMI were associated with increased T2 hyperintensities and age-related cortical atrophy. Radiologist-reported motion aligned with Euler-quantified motion, but neither were associated with IF rates.

Conclusions and Relevance

In this cross-sectional study, IFs were common, particularly with increasing age, although rarely clinically significant. While T2 hyperintensity and age-related cortical atrophy were associated with BMI and blood pressure, IFs were not associated with other behavioral, cognitive, and health phenotyping. Motion may not limit clinical IF detection.

Brain imaging with portable low-field MRI

The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k-space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.

A computer-aided diagnosis system for brain tumors based on artificial intelligence algorithms

The choice of treatment and prognosis evaluation depend on the accurate early diagnosis of brain tumors. Many brain tumors go undiagnosed or are overlooked by clinicians as a result of the challenges associated with manually evaluating magnetic resonance imaging (MRI) images in clinical practice. In this study, we built a computer-aided diagnosis (CAD) system for glioma detection, grading, segmentation, and knowledge discovery based on artificial intelligence algorithms. Neuroimages are specifically represented using a type of visual feature known as the histogram of gradients (HOG). Then, through a two-level classification framework, the HOG features are employed to distinguish between healthy controls and patients, or between different glioma grades. This CAD system also offers tumor visualization using a semi-automatic segmentation tool for better patient management and treatment monitoring. Finally, a knowledge base is created to offer additional advice for the diagnosis of brain tumors. Based on our proposed two-level classification framework, we train models for glioma detection and grading, achieving area under curve (AUC) of 0.921 and 0.806, respectively. Different from other systems, we integrate these diagnostic tools with a web-based interface, which provides the flexibility for system deployment.

Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans

Refined hybrid convolutional neural networks are proposed in this work for classifying brain tumor classes based on MRI scans. A dataset of 2880 T1-weighted contrast-enhanced MRI brain scans are used. The dataset contains three main classes of brain tumors: gliomas, meningiomas, and pituitary tumors, as well as a class of no tumors. Firstly, two pre-trained, fine-tuned convolutional neural networks, GoogleNet and AlexNet, were used for classification process, with validation and classification accuracy being 91.5% and 90.21%, respectively. Then, to improving the performance of the fine-tuning AlexNet, two hybrid networks (AlexNet-SVM and AlexNet-KNN) were applied. These hybrid networks achieved 96.9% and 98.6% validation and accuracy, respectively. Thus, the hybrid network AlexNet-KNN was shown to be able to apply the classification process of the present data with high accuracy. After exporting these networks, a selected dataset was employed for testing process, yielding accuracies of 88%, 85%, 95%, and 97% for the fine-tuned GoogleNet, the fine-tuned AlexNet, AlexNet-SVM, and AlexNet-KNN, respectively. The proposed system would help for automatic detection and classification of the brain tumor from the MRI scans and safe the time for the clinical diagnosis.

Temporal radiographic and histological study of necrosis development in a mouse glioblastoma model

Tumor necrosis is a poor prognostic marker in glioblastoma (GBM) and a variety of other solid cancers. Accumulating evidence supports that necrosis could facilitate tumor progression and resistance to therapeutics. GBM necrosis is typically first detected by magnetic resonance imaging (MRI), after prominent necrosis has already formed. Therefore, radiological appearances of early necrosis formation and the temporal-spatial development of necrosis alongside tumor progression remain poorly understood. This knowledge gap leads to a lack of reliable radiographic diagnostic/prognostic markers in early GBM progression to detect necrosis. Recently, we reported an orthotopic xenograft GBM murine model driven by hyperactivation of the Hippo pathway transcriptional coactivator with PDZ-binding motif (TAZ) which recapitulates the extent of GBM necrosis seen among patients. In this study, we utilized this model to perform a temporal radiographic and histological study of necrosis development. We observed tumor tissue actively undergoing necrosis first appears more brightly enhancing in the early stages of progression in comparison to the rest of the tumor tissue. Later stages of tumor progression lead to loss of enhancement and unenhancing signals in the necrotic central portion of tumors on T1-weighted post-contrast MRI. This central unenhancing portion coincides with the radiographic and clinical definition of necrosis among GBM patients. Moreover, as necrosis evolves, two relatively more contrast-enhancing rims are observed in relationship to the solid enhancing tumor surrounding the central necrosis in the later stages. The outer more prominently enhancing rim at the tumor border probably represents the infiltrating tumor edge, and the inner enhancing rim at the peri-necrotic region may represent locally infiltrating immune cells. The associated inflammation at the peri-necrotic region was further confirmed by immunohistochemical study of the temporal development of tumor necrosis. Neutrophils appear to be the predominant immune cell population in this region as necrosis evolves. This study shows central, brightly enhancing areas associated with inflammation in the tumor microenvironment may represent an early indication of necrosis development in GBM.

Delivering the Promise of Gene Therapy with Nanomedicines in Treating Central Nervous System Diseases

Central Nervous System (CNS) diseases, such as Alzheimer's diseases (AD), Parkinson's Diseases (PD), brain tumors, Huntington's disease (HD), and stroke, still remain difficult to treat by the conventional molecular drugs. In recent years, various gene therapies have come into the spotlight as versatile therapeutics providing the potential to prevent and treat these diseases. Despite the significant progress that has undoubtedly been achieved in terms of the design and modification of genetic modulators with desired potency and minimized unwanted immune responses, the efficient and safe in vivo delivery of gene therapies still poses major translational challenges. Various non-viral nanomedicines have been recently explored to circumvent this limitation. In this review, an overview of gene therapies for CNS diseases is provided and describes recent advances in the development of nanomedicines, including their unique characteristics, chemical modifications, bioconjugations, and the specific applications that those nanomedicines are harnessed to deliver gene therapies.

Peritumor Edema Serves as an Independent Predictive Factor of Recurrence Patterns and Recurrence-Free Survival for High-Grade Glioma

Objective. This study is aimed at analyzing the factors affecting the recurrence patterns and recurrence-free survival (RFS) of high-grade gliomas (HGG). Methods. Eligible patients admitted to the Affiliated Hospital of Xuzhou Medical University were selected. Subsequently, the effects of some clinical data including age, gender, WHO pathological grades, tumor site, tumor size, clinical treatments, and peritumoral edema (PTE) area and molecular markers (Ki-67, MGMT, IDH-1, and p53) on HGG patients’ recurrence patterns and RFS were analyzed. Results. A total number of 77 patients were enrolled into this study. After analyzing all the cases, it was determined that tumor size and tumor site had a significant influence on the recurrent patterns of HGG, and PTE was an independent predict factor of recurrence patterns. Specifically, when the PTE was mild (<1 cm), the recurrence pattern tended to be local; in contrast, HGG was more likely to progress to marginal recurrence and distant recurrence. Furthermore, age and PTE were significantly associated with RFS; the median RFS of the population with $\text{PTE}<1\text{cm}$ (23.60 months) was obviously longer than the population with $\text{PTE}\ge 1\text{cm}$ (5.00 months). Conclusions. PTE is an independent predictor of recurrence patterns and RFS for HGG. Therefore, preoperative identification of PTE in HGG patients is crucially important, which is helpful to accurately estimate the recurrence pattern and RFS.

The Rapid Development of Glioblastoma: A Report of Two Cases

Diffuse astrocytic gliomas and their most common and aggressive representation, glioblastoma (GBM), which as per the 2021 World Health Organization (WHO) guidelines is an isocitrate dehydrogenase (IDH) wildtype without alteration in histone 3 and has glomeruloid vascular proliferation, tumor necrosis, telomerase reverse transcriptase (TERT) promoter mutation, epidermal growth factor receptor (EGFR) gene amplification, or +7/−10 chromosome copy-number changes, are fast-growing tumors with a dismal patient prognosis. Herein, we present cases of a 63-year-old male who, despite no evidence of tumor growth, developed a 6-cm tumor, histologically verified as GBM, WHO CNS grade 4, within eight months, and a 74-year-old female in whom a 1.5-cm tumor grew to 43 mm within 28 days, once again histologically confirmed as GBM, WHO CNS grade 4. Other studies using previous WHO guidelines and including up to 106 cases have shown that these tumors have a daily growth rate of 1.4% and can double their size in a period varying from two weeks to 49.6 days. These growth rates further underline the need for extensive surgical resection as disease progression is rapid, with studies reporting that resection of more than 85% of the tumor volume determined on neuroradiology improves survival compared to biopsy or limited resection and resection of more than 98% of the tumor volume statistically improves patient survival.

Reclassification of Glioblastoma Multiforme According to the 2021 World Health Organization Classification of Central Nervous System Tumors: A Single Institution Report and Practical Significance

Introduction The 2021 World Health Organization (WHO) classification of tumors of the central nervous system (CNS) has introduced significant changes to tumor taxonomy. One of the most significant changes in the isolation of isocitrate dehydrogenase (IDH) mutant forms of glioblastoma multiforme (GBM) into separate entities, as well as no longer allowing for entries to be classified as not otherwise specified (NOS). As a result, this entity now includes only the most aggressive adult-type tumors. As such, established prognostic factors no longer apply, as they now form the criteria of different disease entries or have been established based on a mixed cohort. Herein, we aimed to reclassify glioblastoma cases diagnosed per the 2016 WHO tumors of the CNS classification into the 2021 WHO tumors of the CNS classification and establish a patient survival pattern based on age, gender, tumor location, and size as well as tumor O-6-methylguanine-DNA methyltransferase (MGMT) mutation. Materials and methods A retrospective, non-clinical approach was utilized. Biopsy specimens of adults diagnosed with GBM, WHO grade 4, NOS in the period February 2018-February 2021 were reevaluated. The data regarding the patient's gender and age were withdrawn from the medical documentation. Immunohistochemistry was performed with mouse monoclonal anti-IDH R132H and rabbit polyclonal anti-MGMT. Radiology data on tumor location and size were pulled from the radiology repository. Data were statistically analyzed for significance, using Kaplan-Meier survival analysis, with a 95% confidence interval and p<0.05 defined as significant. Results A total of 58 cases fit the set criteria, with eight of them (13.7%) harboring an IDH R132H mutation and were hence reclassified as diffuse astrocytoma IDH-mutant, WHO CNS grade 4. The cases that retained their GBM classification included n=28 males and n=22 females, a male to female ratio of 1.27:1, and a mean age of 65.3 years (range 43-86 years). The MGMT mutational status revealed a total of n=17 positive cases (35%), while the remaining cases were negative. No hemispheric predilection could be established. Lobar predilection was as follows: temporal (37.78%), parietal (28.89%), frontal (24.44%), and occipital (8.89%). The mean tumor size measured on neuroradiology across the cohort was 50.51 mm (range 20-76 mm). The median survival across cases was 255.96 days (8.41 months), with a range of 18-1150 days (0.59-37.78 months). No statistical correlation could be established between patient survival and gender, hemispheric location, lobar location, and tumor size. A significant difference in survival was established only when comparing the 41-50 age groups to the 71-80 and 81-90 age groups and MGMT positive versus negative tumors (p=0.0001). Conclusion From a practical standpoint, the changes implemented in the new classification of CNS tumors define GBM as the most aggressive adult type of tumor. Based on their significantly more favorable prognosis, the reclassification of IDH mutant forms of astrocytomas has had little epidemiological impact on this relatively common malignancy but has significantly underlined the dismal prognosis. The changes have also led to MGMT promoter methylation status being the only significant prognostic factor for patient survival in clinical use, based on its prediction for response to temozolomide therapy in this nosological unit clinically presenting when it has already reached immense size.

The clinical characteristics and outcomes of incidentally discovered glioblastoma

OBJECTIVE

With an increase in the number of imaging examinations and the development of imaging technology, a small number of glioblastomas (GBMs) are identified by incidental radiological images. These incidentally discovered glioblastomas (iGBMs) are rare, and their clinical features are not well understood. Here, we investigated the clinical characteristics and outcomes of iGBM.

METHODS

Data of newly diagnosed iGBM patients who were treated at our institution between August 2005 and October 2019 were reviewed. An iGBM was defined as a GBM without a focal sign, discovered on radiological images obtained for reasons unrelated to the tumor. Kaplan-Meier analysis was performed to calculate progression-free survival (PFS) and overall survival (OS).

RESULTS

Of 315 patients with newly diagnosed GBM, four (1.3%) were classified as having iGBM. Health screening was the most common reason for tumor discovery (75.0%). The preoperative Karnofsky performance status score was 100 in three patients. Tumors were found on the right side in three cases. The mean volume of preoperative enhanced tumor lesion was 16.8 cm³. The median duration from confirmation of an enhanced lesion to surgery was 13.5 days. In all cases, either total (100%) or subtotal (95-99%) resections were achieved. The median PFS and OS were 10.5 and 20.0 months, respectively.

CONCLUSIONS

The iGBMs were often small and in the right non-eloquent area, and the patients had good performance status. We found that timely therapeutic intervention provided iGBM patients with favorable outcomes. This report suggests that early detection of GBM may lead to a better prognosis.

Brain metastases: the role of clinical imaging

Imaging of brain metastases (BMs) has advanced greatly over the past decade. In this review, we discuss the main challenges that BMs pose in clinical practice and describe the role of imaging.Firstly, we describe the increased incidence of BMs of different primary tumours and the rationale for screening. A challenge lies in selecting the right patients for screening: not all cancer patients develop BMs in their disease course.Secondly, we discuss the imaging techniques to detect BMs. A three-dimensional (3D) T1W MRI sequence is the golden standard for BM detection, but additional anatomical (susceptibility weighted imaging, diffusion weighted imaging), functional (perfusion MRI) and metabolic (MR spectroscopy, positron emission tomography) information can help to differentiate BMs from other intracranial aetiologies.Thirdly, we describe the role of imaging before, during and after treatment of BMs. For surgical resection, imaging is used to select surgical patients, but also to assist intraoperatively (neuronavigation, fluorescence-guided surgery, ultrasound). For treatment planning of stereotactic radiosurgery, MRI is combined with CT. For surveillance after both local and systemic therapies, conventional MRI is used. However, advanced imaging is increasingly performed to distinguish true tumour progression from pseudoprogression.FInally, future perspectives are discussed, including radiomics, new biomarkers, new endogenous contrast agents and theranostics.

Prevalence of incidental meningiomas and gliomas on MRI: a meta-analysis and meta-regression analysis

BACKGROUND

The chance of incidentally detecting brain tumors is increasing as the utilization of magnetic resonance imaging (MRI) becomes more prevalent. In this background, knowledge is accumulating in relation to the prediction of their clinical sequence. However, their prevalence-especially the prevalence of glioma-has not been adequately investigated according to age, sex, and region.

METHOD

We systematically reviewed the articles according to the PRISMA statement and calculated the prevalence of meningiomas and diffuse gliomas in adults using a generalized linear mixed model. Specifically, the differences related to age, sex, and region were investigated.

RESULTS

The pooled prevalence of incidental meningiomas in MRI studies was 0.52% (95% confidence interval (CI) [0.34-0.78]) in 37,697 individuals from 36 studies. A meta-regression analysis showed that the prevalence was significantly higher in elderly individuals, women, and individuals outside Asia; this remained statistically significant in the multivariate meta-regression analysis. The prevalence reached to 3% at 90 years of age. In contrast, the prevalence of gliomas in 30,918 individuals from 18 studies was 0.064% (95%CI [0.040 - 0.104]). The meta-regression analysis did not show a significant relationship between the prevalence and age, male sex, or region. The prevalence of histologically confirmed glioma was 0.026% (95%CI [0.013-0.052]).

CONCLUSIONS

Most of meningiomas, especially those in elderlies, remained asymptomatic, and their prevalence increased with age. However, the prevalence of incidental gliomas was much lower and did not increase with age. The number of gliomas that developed and the number that reached a symptomatic stage appeared to be balanced.

Disability-Adjusted Life Years and Mortality Rate Attributed to Brain and Central Nervous System Cancer in the Middle East and North Africa Countries

BACKGROUND

Primary brain tumors are among the main causes of death. This study aimed to determine the epidemiological features of the brain and central nervous system cancer in the Middle East and North Africa (MENA) region.

METHODS

In this study, data of the Global Burden of Disease (GBD) study were used to estimate the incidence, prevalence, deaths, disability-adjusted life years (DALYs), and mortality in 21 countries in the MENA region from 1990 to 2019 based on age and sex. The percentage of the changes of epidemiologic indicators was calculated between 1990 and 2019.

RESULTS

Palestine and Turkey had the highest rate of brain and central nervous system cancer in 2019. Saudi Arabia, Oman, Iraq, and Lebanon had the highest percentage of incidence rate changes from 1990 to 2019. The prevalence of brain and central nervous system cancer in the MENA region was increased from 7.51 (95% CI: 4.95-11.01) in 1990 to 16.45 (95% CI: 10.83-19.54) in 2019 (percentage of changes = 54.35%). The standardized age mortality rate in the MENA region was increased by 2.7% in 2019 compared to that in 1990. The rate of standardized age of DALY per 100,000 individuals in the MENA region decreased from 135.09 (95% CI: 92.57-199.92) in 1990 to 128.34 (95% CI: 87.81-151.3) in 2019.

CONCLUSION

The incidence rate, prevalence, and standardized age mortality (per 100,000) had increased significantly in the MENA region in 2019 compared to those in 1990. Focusing on the diversity of the estimates of such indices in different countries of MENA can lead to the identification of important risk factors for brain cancer in future studies.

Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.