Diagnosis Of Brain Tumors Research Articles

Retraction: Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm

Liu T, Yuan Z, Wu L, Badami B. Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm. Int J Imaging Syst Technol. 2021;31:1921–1935. https://doi.org/10.1002/ima.22559The above article, published online on 22 February 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief Dr. Mohamed Seghier, and Wiley Periodicals, LLC. The retraction has been agreed due to major overlap between this article and the following article by the same group of authors: Liu T, Yuan Z, Wu L, Badami B. An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm. Proc Inst Mech Eng H., J. Med. Eng 2021;235:459–469. https://doi.org/10.1177/0954411920987964. Following an investigation by the editorial office and Wiley, it was determined that the degree of overlap between these two articles constitutes a duplicate publication, which violates the journal's and Wiley's policies.

Brain tumor malignancy classification using improved VGG16 based on MRI images

This study introduces a method based on Magnetic Resonance Imaging (MRI) images and Convolutional Neural Networks (CNN) to categorize the malignancy of brain tumors. The major goal of this research is to develop an accurate and reliable model that aids in tumor early identification, leading to better patient outcomes. Specifically, first, the MRI images undergo preprocessing, which includes intensity normalization and random brightness and contrast adjustments for data augmentation. Next, the Visual Geometry Group (VGG16) model serves as a baseline, enhanced with flatten, dense, and dropout layers. Transfer learning is applied to leverage pre-trained features from VGG16, improving the generalization capability of model. A sizable collection of brain MRI pictures is used to train the model. The experimental outcomes on the MRI dataset illustrate the efficacy of the suggested model. The CNN-based approach achieves high accuracy in classifying brain tumor malignancy based on MRI images, indicating its potential in medical image analysis and its significance in early tumor detection and diagnosis. The proposed model serves as a valuable tool for healthcare professionals, aiding in well-informed decisions and providing an ideal medical approach for early detection of brain tumors. This research opens avenues for future work in medical imaging and deep learning, paving the way for improved treatment and diagnosis of brain tumors.

Abstract TP229: Stroke Rate and Associated Factors in Individuals With Primary Brain Gliomas and Brain Radiation Therapy: A Retrospective Cohort Study

Background/Objectives: The risk of stroke in individuals with brain gliomas may be increased due to several possible factors including chemotherapy, radiation-induced toxicity, and concomitant vascular risks. We report on the stroke rate and associated factors following brain tumor diagnosis and radiation therapy. Design: This is a retrospective cohort study, from a single tertiary academic brain tumor center, of patients with brain gliomas diagnosed between 2004-2021 who received brain radiation therapy during their treatment. Chi-square test was used to calculate the odds ratios. Results: Among a total of 912 patients with primary gliomas who received radiation therapy, stroke subsequently occurred in 93 patients (10.2%). Strokes were ischemic in 72.0% (N=67), intracerebral hemorrhage (ICH) in 25.8% (N=24), and subarachnoid hemorrhage in 2.2% (N=2). Individuals who sustained a stroke were 44.1% female and had a mean age of 54.2 years (range 21-84) at the time of stroke diagnosis. Of those who had a stroke, 64.5% (N=60) were also taking bevacizumab at the time of stroke. Median time from initiation of radiation therapy to stroke was 653 days (range 0-3788). Median radiation dose was 5940 cGy (range 1800-6000). The following factors were associated with a higher odd of having a stroke: radiation necrosis (OR 4.51; 95% CI 1.98-10.29, P=0.0003); hypertension (OR 1.82; 95% CI 1.17-2.82, P=0.008); hyperlipidemia (OR 5.50; 95% CI 3.45-8.78, P<0.0001); and diabetes (OR 2.19; 95% CI 1.25-3.84, P=0.007). Among stroke patients, 80.6% had strokes on the same side as radiation therapy. Those who were taking bevacizumab were more likely to have an ICH (31.7% vs. 15.1%, OR 2.60, 95% CI 0.87-7.77, P=0.09) as compared to other stroke types. Conclusions: Individuals with primary brain gliomas undergoing radiation therapy who were subsequently diagnosed with a stroke had higher rates of comorbid vascular risk factors and radiation necrosis.

Characterisation of paediatric brain tumours by their MRS metabolite profiles.

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

Brain tumor grade classification using the ConvNext architecture.

Brain tumor grade is an important aspect of brain tumor diagnosis and helps to plan for treatment. Traditional methods of diagnosis, including biopsy and manual examination of medical images, are either invasive or may result in inaccurate diagnoses. This study proposes a brain tumor grade classification technique using a modern convolutional neural network (CNN) architecture called ConvNext that inputs magnetic resonance imaging (MRI) data. Deep learning-based techniques are replacing invasive procedures for consistent, accurate, and non-invasive diagnosis of brain tumors. A well-known challenge of using deep learning architectures in medical imaging is data scarcity. Modern-day architectures have huge trainable parameters and require massive datasets to achieve the desired accuracy and avoid overfitting. Therefore, transfer learning is popular among researchers using medical imaging data. Recently, transformer-based architectures have surpassed CNNs for image data. However, recently proposed CNNs have achieved superior accuracy by introducing some tweaks inspired by vision transformers. This study proposed a technique to extract features from the ConvNext architecture and feed these features to a fully connected neural network for final classification. The proposed study achieved state-of-the-art performance on the BraTS 2019 dataset using pre-trained ConvNext. The best accuracy of 99.5% was achieved when three MRI sequences were input as three channels of the pre-trained CNN. The study demonstrated the efficacy of the representations learned by a modern CNN architecture, which has a higher inductive bias for the image data than vision transformers for brain tumor grade classification.

Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model

Brain cancer deaths are significantly increased in all categories of aged persons due to the abnormal growth of brain tumor tissues in the brain. The death rate can be controlled by accurate early stage brain tumor diagnosis. The detection and classification of brain tumors play a crucial role in early diagnosis and treatment planning. Brain tumor detection and classification have become challenging and time-consuming for domain-specific radiologists and pathologists in medical image analysis. So, automatic detection and classification are essential to reduce the time of diagnosis. In recent years, machine learning classifiers have played an essential role in automatically classifying brain tumors. In this research, an approach based on an improved fuzzy factor fuzzy local information C means (IFF-FLICM) segmentation and hybrid modified harmony search and sine cosine algorithm (MHS-SCA) optimized extreme learning machine (ELM) is proposed for brain tumor detection and classification. The IFF-FLICM algorithm is utilized to accurately segment the brain’s magnetic resonance (MR) images to identify the tumor regions. The Mexican hat wavelet transform is employed for feature extraction from the segmented images. The extracted features from the segmented regions are fed into the MHS-SCA-ELM classifier for classification. The MHS-SCA is proposed to optimize the weights of the ELM model to improve the classification performance. Five distinct multimodal and unimodal benchmark functions are considered for optimization to demonstrate the robustness of the proposed MHS-SCA optimization technique. The image Dataset-255 is considered for this study. The quality measures such as SSIM and PSNR are considered for segmentation. The proposed IFF-FLICM segmentation achieved a peak signal-to-noise ratio (PSNR) of 37.24 dB and a structural similarity index (SSIM) of 0.9823. The proposed MHS-SCA-based ELM model achieved a sensitivity, specificity, and accuracy of 98.78%, 99.23%, and 99.12%. The classification performance results of the proposed MHS-SCA-ELM model are compared with MHS-ELM, SCA-ELM, and PSO-ELM models, and the comparison results are presented.

A Novel Ensemble Model for Brain Tumor Diagnosis

The spread of brain tumors resulted in numerous deaths, and cancer patients are still being treated. Four novel models are introduced and compared in this study. The best one is the PIEnsemble model, which was created to correctly identify and classify MRI images for brain tumor classification. The PIEnsemble primarily combines three deep learning techniques, ResNet Model, GoogleNet Model, and Inception Model, integrated with two dimensionality reduction techniques, PCA and ICA, which are used for feature extraction with a combination of Linear, Batch1Norm1D, and ReLU layers. Simulations in a series on two benchmarks datasets were run to show the improved effectiveness of the PIEnsemble model. The experimental results highlighted the improvements to the PIEnsemble classifier structure, which has the highest classification accuracy ratio and superior performance to other methods, with a 98.25% accuracy for Dataset 1 and a 98.75% accuracy for Dataset 2.

Current and future therapy for BRAF-mutant pediatric glioma

Current and future therapy for BRAF-mutant pediatric glioma Professor Peter J. Houghton from the Greehey Children’s Cancer Research Institute discusses new approaches to pediatric cancer treatment specifically for BRAF-mutant pediatric glioma. Low-grade glioma (LGG) is the most frequently diagnosed brain tumor in children, and although five and ten-year survival is quite high, this does not reveal the toxicity of current therapies. Conventional treatment includes intensive chemo-radiation therapy that can lead to cognitive decline, malignant transformation, and other life-debilitating or -threatening sequelae. (1) The 15-year incidence of adverse outcomes such as blindness, hearing loss, obesity, and hormonal imbalance is 18%, 22%, 53%, and approximately 25%, respectively. Among survivors assessed for intellectual function, 34% had an intelligence quotient (IQ) below average. Treatment-resistant progressive disease is the most common cause of death. (2)

Structure-changeable luminescent Eu(III) complex as a human cancer grade probing system for brain tumor diagnosis.

Accurate determination of human tumor malignancy is important for choosing efficient and safe therapies. Bioimaging technologies based on luminescent molecules are widely used to localize and distinguish active tumor cells. Here, we report a human cancer grade probing system (GPS) using a water-soluble and structure-changeable Eu(III) complex for the continuous detection of early human brain tumors of different malignancy grades. Time-dependent emission spectra of the Eu(III) complexes in various types of tumor cells were recorded. The radiative rate constants (kr), which depend on the geometry of the Eu(III) complex, were calculated from the emission spectra. The tendency of the kr values to vary depended on the tumor cells at different malignancy grades. Between T = 0 and T = 3h of invasion, the kr values exhibited an increase of 4% in NHA/TS (benign grade II gliomas), 7% in NHA/TSR (malignant grade III gliomas), and 27% in NHA/TSRA (malignant grade IV gliomas). Tumor cells with high-grade malignancy exhibited a rapid upward trend in kr values. The cancer GPS employs Eu(III) emissions to provide a new diagnostic method for determining human brain tumor malignancy.

Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.

The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).

Magnetic resonance imaging in the diagnosis of glial brain tumors in the light of a new classification of central nervous system tumors

Glial tumors of the brain is still to be a pressing problem in oncology. They are usually characterized by rapid growth rates, invasiveness, frequent recurrence, and a poor prognosis. This literature review was made with the aim of introducing the diagnostic capabilities of MRI in recognizing glial tumors. It includes 44 sources written for the period from 2005 to 2023.

An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor

A brain tumor is an unnatural expansion of brain cells that can’t be stopped, making it one of the deadliest diseases of the nervous system. The brain tumor segmentation for its earlier diagnosis is a difficult task in the field of medical image analysis. Earlier, segmenting brain tumors was done manually by radiologists but that requires a lot of time and effort. Inspite of this, in the manual segmentation there was possibility of making mistakes due to human intervention. It has been proved that deep learning models can outperform human experts for the diagnosis of brain tumor in MRI images. These algorithms employ a huge number of MRI scans to learn the difficult patterns of brain tumors to segment them automatically and accurately. Here, an encoder-decoder based architecture with deep convolutional neural network is proposed for semantic segmentation of brain tumor in MRI images. The proposed method focuses on the image downsampling in the encoder part. For this, an intelligent LinkNet-34 model with EfficientNetB7 encoder based semantic segmentation model is proposed. The performance of LinkNet-34 model is compared with other three models namely FPN, U-Net, and PSPNet. Further, the performance of EfficientNetB7 used as encoder in LinkNet-34 model has been compared with three encoders namely ResNet34, MobileNet_V2, and ResNet50. After that, the proposed model is optimized using three different optimizers such as RMSProp, Adamax and Adam. The LinkNet-34 model has outperformed with EfficientNetB7 encoder using Adamax optimizer with the value of jaccard index as 0.89 and dice coefficient as 0.915.

HoloBrain: 3D low-cost mobile augmented reality rendering of brain tumour using the GVF snake model segmentation

ABSTRACT Augmented reality (AR) is a dynamic field intersecting various disciplines, with applications spanning medicine and diagnostics. Our focus on advancing medical imaging has led to groundbreaking progress in brain tumor diagnosis, fostering optimism in treatment. In this pursuit, we introduce HoloBrain, an accessible AR application designed to transform medical education. Utilizing smartphones, HoloBrain enables automatic segmentation, AR visualization, and 3D interaction without the need for head-mounted devices. Emphasizing practicality over hyper-realism, our innovation allows medical students to seamlessly integrate a virtual brain tumor model into their surroundings. This immersive approach facilitates exploration of the 3D representation of an afflicted brain, enhancing educational experiences. Tests in a controlled environment using a phantom head yielded promising results, with our solution demonstrating average errors of 2 mm to 4.5 mm on smartphone displays. These outcomes highlight the precision and effectiveness of our contribution, underlining its potential in transforming medical education and training. HoloBrain represents a step forward in making AR accessible and impactful for aspiring medical professionals, offering a valuable tool for enhancing their understanding of complex medical scenarios.

Brain Tumor Detection with Deep Learning Methods’ Classifier Optimization Using Medical Images

It is known that, with the development of artificial intelligence science in recent years, it has started to be used in all areas of life. Due to the increase in diseases that threaten human life, such as epidemics and cancer, more attention has been paid to research in this field. Especially in the field of biomedical image processing, very successful results have been obtained in recent years with the use of deep learning methods. For this study, MR images are utilized to diagnose brain tumors. To assist doctors and radiologists in automatic brain tumor diagnosis and to overcome the need for manual diagnosis, a brain MR image automated classification system is being developed. The data used in the study are open access data obtained from the Kaggle library. This paper presents a novel approach for classifying brain MR images utilizing a dataset of 7022 MR images. To give an unbiased evaluation of the dataset, it is divided into a 40% test and 60% training set. Respectively, VGG, ResNet, DenseNet and SqueezeNet architectures are trained and used for feature extraction from brain MRI images. In order to classify the extracted features, machine learning methods (Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Decision Tree, Linear Regression Analysis) are applied first, then an ensemble learning method is applied and the best validation method is selected. In addition, parameter optimization is applied to the trained CNN algorithms. In order to develop the proposed methods, the Python software program was used in the training and testing phases of the models, and the classification success rates were mutually evaluated. Among the results found, it can see that the ResNet architecture reached 100% accuracy. The data obtained as a result of the study were compared with the results of similar studies. In conclusion, the techniques and methods applied highlight their effectiveness in accurately classifying brain MRI images and their potential to improve diagnostic capabilities.

Effects of autolysis and prolonged formalin fixation on histomorphology and immunohistochemistry of normal canine brain tissue: an experimental study.

CNS tumor diagnosis in dogs often relies on immunohistochemistry (IHC) given similar histologic features among tumors. Most CNS tissue samples encountered by diagnostic pathologists are collected during autopsy, and postmortem specimens can be susceptible to autolysis and prolonged formalin fixation, both of which have the potential to influence IHC results and interpretation. Here we evaluated the effects of experimentally controlled autolysis induced by delayed tissue fixation (sections of brain held for 2, 4, 8, 12, 24, 48, and 72 h in 0.9% NaCl at either room temperature or 37°C prior to fixation) as well as the effects of prolonged formalin fixation times (1 wk, 1 mo, 2 mo) on a panel of 8 IHC markers (CNPase, GFAP, Iba1, OLIG2, PGP9.5, MAP2, NeuN, synaptophysin) relevant to brain tumor diagnosis. Prolonged fixation of up to 2 mo had no detrimental effect on any immunomarker except NeuN, which had reduced immunolabeling intensity. Delayed fixation led to autolytic changes as expected, on a gradient of severity corresponding to increased time in saline prior to fixation. Several immunomarkers should be used with caution (CNPase, OLIG2) or avoided entirely (MAP2, NeuN) in markedly autolyzed brain and brain tumor tissues. Our results suggest that autolysis has minimal effect on most immunomarkers, but that advanced autolysis may cause a loss of specificity for GFAP, MAP2, and PGP9.5, a loss of intensity of CNPase and OLIG2, and loss of labeling with MAP2 and NeuN. Prolonged fixation affected only NeuN, with mildly decreased intensity.

Diagnostic Accuracy of Intraoperative Brain Smear: A Meta-Analysis of Studies from Resource-Limited Settings

BackgroundIntraoperative brain smear is an easy, rapid, and cost-effective technique for immediate diagnosis of brain tumors. Earlier studies have gauged its application on a limited number of samples, but its diagnostic accuracy especially in low-resource settings, where its practice would be extremely helpful is still undetermined. ObjectiveTo investigate the diagnostic accuracy of intraoperative brain smear in resource-limited settings for diagnosis of brain tumors. MethodsA systematic search was conducted on PubMed, Google Scholar, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Embase for all articles utilizing intraoperative brain smears that were extracted. Studies from low- and middle-income countries (LMIC) with test performance characteristics were selected and subsequent values were summarized using a hierarchical summary receiver operating characteristic (ROC) curve via STATA and pooled using a random-effects model on MetaDiSc 2.0. ResultsTwelve studies consisting of 1124 patients were identified. Six studies included both adult and pediatric population groups, while four investigated adults and two included pediatric patients. The pooled diagnostic odds ratio was calculated to be 212.52 (CI: [104.27 - 433.13]) of Bivariable pooled specificity and sensitivity were 92% (CI: [86%-96%]) and 96% (CI: [93%-98%]), respectively. ConclusionOur study shows that intraoperative brain smear is not only an accurate and sensitive diagnostic modality in resource-rich settings, but it is also equally useful in resource-limited settings, making it an ideal method for rapid diagnosis.

RanMerFormer: Randomized vision transformer with token merging for brain tumor classification

Brains are the control center of the nervous system in human bodies, and brain tumor is one of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most effective way to brain tumors early detection in clinical diagnoses due to its superior imaging quality for soft tissues. Manual analysis of brain MRI is error-prone which depends on empirical experience and the fatigue state of the radiologists to a large extent. Computer-aided diagnosis (CAD) systems are becoming more and more impactful because they can provide accurate prediction results based on medical images with advanced techniques from computer vision. Therefore, a novel CAD method for brain tumor classification named RanMerFormer is presented in this paper. A pre-trained vision transformer is used as the backbone model. Then, a merging mechanism is proposed to remove the redundant tokens in the vision transformer, which improves computing efficiency substantially. Finally, a randomized vector functional-link serves as the head in the proposed RanMerFormer, which can be trained swiftly. All the simulation results are obtained from two public benchmark datasets, which reveal that the proposed RanMerFormer can achieve state-of-the-art performance for brain tumor classification. The trained RanMerFormer can be applied in real-world scenarios to assist in brain tumor diagnosis.

Brain Tumor Detection Using a Deep CNN Model

The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This underscores the need for an autonomous model for brain tumor diagnosis. Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. The proposed model is built upon the state‐of‐the‐art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. Through training and testing, the pretrained CNN model achieves a precision and classification accuracy rate of 96%, and the loss is 1%. Moreover, it achieves an average precision, recall, and F1‐score of 98.7%, 97.44%, and 98.06%, respectively. These evaluation metric values demonstrate the effectiveness of the proposed solution.

Accurate brain pharmacokinetic parametric imaging using the blood input function extracted from the cavernous sinus.

Brain pharmacokinetic parametric imaging based on dynamic positron emission tomography (PET) scan is valuable in the diagnosis of brain tumor and neurodegenerative diseases. For short-axis PET system, standard blood input function (BIF) of the descending aorta is not acquirable during the dynamic brain scan. BIF extracted from the intracerebral vascular is inaccurate, making the brain parametric imaging task challenging. This study introduces a novel technique tailored for brain pharmacokinetic parameter imaging in short-axis PET in which the head BIF (hBIF) is acquired from the cavernous sinus. The proposed method optimizes the hBIF within the Patlak model via data fitting, curve correction and Patlak graphical model rewriting. The proposed method was built and evaluated using dynamic PET datasets of 67 patients acquired by uEXPLORER PET/CT, among which 64 datasets were used for data fitting and model construction, and 3 were used for method testing; using cross-validation, a total of 15 patient datasets were finally used to test the model. The performance of the new method was evaluated via visual inspection, root-mean-square error (RMSE) measurements and VOI-based accuracy analysis using linear regression and Person's correlation coefficients (PCC). Compared to directly using the cavernous sinus BIF directly for parameter imaging, the new method achieves higher accuracy in parametric analysis, including the generation of Patlak plots closer to the standard plots, better visual effects and lower RMSE values in the Ki (P = 0.0012) and V (P = 0.0042) images. VOI-based analysis shows regression lines with slopes closer to 1 (P = 0.0019 for Ki ) and smaller intercepts (P = 0.0085 for V). The proposed method is capable of achieving accurate brain pharmacokinetic parametric imaging using cavernous sinus BIF with short-axis PET scan. This may facilitate the application of this imaging technology in the clinical diagnosis of brain diseases.

MicroRNAs as Biomarkers of Brain Tumor.

Brain tumors have been deadly cancers for years, and in most cases they are difficult to diagnose in their early stages. For this reason, researchers need to develop low-cost, sensitive methods for examining cancer biomarkers. Such biomarkers include microRNA. MicroRNA expression in various body fluids shows a high correlation with cancer. A number of studies have demonstrated changes in microRNA expression in cerebrospinal fluid and blood samples from patients with brain tumors. New biomarkers such as microRNAs may help diagnose brain tumors at the very beginning of the disease, enabling early treatment and increasing the chances of survival. This review describes the diagnostic role of microRNAs and the prospects for their use as biomarkers in patients with brain tumors.