Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them

Multi Stage Classification and Segmentation of Brain Tumor Images Based on Statistical Feature Extraction Technique

Automatic classification of brain images has a censorious act in calm down the burden of manual characterize and developing power of brain tumor diagnosis. In this paper, Stanchion Vector Machine (SVM) method has been employed to perform classification of brain tumor images into their variety and grades. Chiefly the target is on four brain tumor categories-Normal, Glioma, Meningioma, Metastasis and the four grades of Astrocytomas, which is a conventional section of Glioma. We consult segmentation of glioma tumors, which have a large deviation in size, pattern and appearance inheritance. In this paper images are enlarged and normalized to same range in a pre-functioning stride.The enlarged images are then segmented positioned on their intensities applying 3D super-voxels. This effort analyze the SVM classifier applying variance statistical feature set the final analysis shows that for brain tumor categories and grades classification. The analyses are repeated for variance SVM categories, kernel categories and gamma points of kernel section. Analysis on the misclassification is implemented for each feature set applying specificity and sensitivity measures. At the end of this effort, we inferred that the Statistical feature Extraction(SFE) method is classifying the brain tumor categories satisfactorily but comparatively lacks in tumor grade classification. Classifying the brain tumorcan collection their material in the cloud, the cloud create it attainable to admissionourmaterialin distinction to anywhere at any time

Supervised Partial Volume Effect Unmixing for Brain Tumor Characterization using Multi-voxel MR Spectroscopic Imaging

A major challenge faced by multi-voxel Magnetic Resonance Spectroscopy (MV-MRS) imaging is partial volume effect (PVE), where signals from two or more tissue types may be mixed within a voxel. This problem arises due to the low resolution data acquisition, where the size of a voxel is kept relatively large to improve the signal to noise ratio. We propose a novel supervised Signal Mixture Model (SMM), which characterizes the MV-MRS signal into normal, low grade (infiltrative) and high grade (necrotic) brain tissue types, while accounting for in-type variation. An optimization problem is solved based on differential equations, to unmix the tissue by estimating mixture coefficients corresponding to each tissue type at each voxel. This enables visualization of probability heatmaps, useful for characterizing heterogeneous tumors. Experimental results show an overall accuracy of 91.67% and 88.89% for classifying tumors into either low or high grade against histopathology, and demonstrate the method's potential for non-invasive computer-aided diagnosis

TEXTURE CLASSIFICATION USING WEIGHTED PROBABILISTIC NEURAL NETWORKS

Texture classification is basically the problem of classifying pixels in an image according to their textural cues. This is different from conventional image segmentation as the texture is characterized using both the gray value for a given pixel and gray-level pattern in the neighborhood surrounding the pixel. In this project, the novel temporal updating approach is developed for weighted probabilistic neural network (WPNN) classifiers that can be used to classify the textures. This is done by utilizing the temporal contextual information and adjusting the WPNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the WPNN updated to the last frame while at the same time, a prediction using PNN is also based on the classification results of previous frame. The result of both the PNN and WPNN are then compared. Compared to the PNN, WPNN includes weighting factors between pattern layers and summation layer of the PNN. Performance of this approach is compared with model based and feature based methods in terms of signal to noise ratio and classification rate

Intravoxel water diffusion heterogeneity imaging of human high-grade gliomas

This study aimed to determine the potential value of intravoxel water diffusion heterogeneity imaging for brain tumor characterization and evaluation of high-grade gliomas, by comparing an established heterogeneity index ( Α value) measured in human high-grade gliomas to those of normal appearing white and grey matter landmarks. Twenty patients with high-grade gliomas prospectively underwent diffusion-weighted magnetic resonance imaging using multiple b-values. The stretched-exponential model was used to generate Α and distributed diffusion coefficient (DDC) maps. The Α values and DDCs of the tumor and contralateral anatomic landmarks were measured in each patient. Differences between Α values of tumors and landmark tissues were assessed using paired t- tests. Correlation between tumor Α and tumor DDC was assessed using Pearson's correlation coefficient. Mean Α of tumors was significantly lower than that of contralateral frontal white matter ( p  = 0.0249), basal ganglia ( p  < 0.0001), cortical grey matter ( p  < 0.0001), and centrum semiovale ( p  = 0.0497). Correlation between tumor Α and tumor DDC was strongly negative (Pearson correlation coefficient, −0.8493; p  < 0.0001). The heterogeneity index Α of human high-grade gliomas is significantly different from those of normal brain structures, which potentially offers a new method for evaluating brain tumors. The observed negative correlation between tumor Α and tumor DDC requires further investigation. Copyright © 2009 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65045/1/1441_ftp.pd

Primary Level Classification of Brain Tumor using PCA and PNNÂ

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Adding Contextual Information to Intrusion Detection Systems Using Fuzzy Cognitive Maps

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The experimental results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections

Comparison of apparent diffusion coefficients and distributed diffusion coefficients in high-grade gliomas

Purpose: To compare apparent diffusion coefficients (ADCs) with distributed diffusion coefficients (DDCs) in high-grade gliomas. Materials and Methods: Twenty patients with high-grade gliomas prospectively underwent diffusion-weighted MRI. Traditional ADC maps were created using b-values of 0 and 1000 s/mm 2 . In addition, DDC maps were created by applying the stretched-exponential model using b-values of 0, 1000, 2000, and 4000 s/mm 2 . Whole-tumor ADCs and DDCs (in 10 −3 mm 2 /s) were measured and analyzed with a paired t-test, Pearson's correlation coefficient, and the Bland-Altman method. Results: Tumor ADCs (1.14 ± 0.26) were significantly lower ( P = 0.0001) than DDCs (1.64 ± 0.71). Tumor ADCs and DDCs were strongly correlated ( R = 0.9716; P < 0.0001), but mean bias ± limits of agreement between tumor ADCs and DDCs was −0.50 ± 0.90. There was a clear trend toward greater discordance between ADC and DDC at high ADC values. Conclusion: Under the assumption that the stretched-exponential model provides a more accurate estimate of the average diffusion rate than the mono-exponential model, our results suggest that for a little diffusion attenuation the mono-exponential fit works rather well for quantifying diffusion in high-grade gliomas, whereas it works less well for a greater degree of diffusion attenuation. J. Magn. Reson. Imaging 2010;31:531–537. © 2010 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69184/1/22070_ftp.pd

Multivariate Multi-scaled Student Distributions : brain tumor characterization from multiparametric MRI

International audienceBrain tumor characterization is very useful for patients treatment, but it can be time-consuming for medical experts. Furthermore, the reference method to characterize tissues is biopsy which is a local and invasive technique. Because of this, there is a huge interest for automatic and non-invasive approaches in order to characterize tumor. In this study we use a statistical model-based method to classify multiparametric MRI of brain rat tumors, which allows data quality control with atypical observations detection, and may provide a dictionary of tumor signatures. A previous study, [1], used a Gaussian mixture model to characterize pixels inside tumors. With this model, the observations are gathered into classes resulting from Gaussian distributions. However, this model is sensitive to outliers which degrade the relevance of the obtained groups. And inside a tumor, there could be a huge variability and so a lot of outliers. To account for this biological variability, we propose to use generalized Student distributions : the multivariate multi-scaled Student distributions (MMSD, [2]). The MMSD distribution extends the standard multivariate Student distribution by using the Gaussian scale mixture representation of Student distributions. This representation allows us to introduce multi-dimensional weights, which control different tail thickness of the distribution for each dimension, and provide a way to detect outlier data. In this way, we obtain a finer regulation of the influence of atypical data on the groups shapes, and so a greater flexibility of the clustering model. We use an Expectation-Maximization algorithm (EM) to adjust a MMSD mixture on brain tumor MRI. The number of classes inside the mixture is selected by minimizing the Bayesian information criterion (BIC). Our sample consists of healthy rats (n=8) and 4 groups of rats bearing a brain tumor model (n=8 per group), and 5 quantitative MRI parameter maps for each rat. We adjust a MMSD mixture on the healthy sample to detect tumor area in the tumor sample through the multi-dimensional weights. Then we characterize the tumor areas with another MMSD mixture and build a tumor dictionary which discriminates the 4 tumor

Multivariate Multi-scaled Student Distributions : brain tumor characterization from multiparametric MRI

International audienceBrain tumor characterization is very useful for patients treatment, but it can be time-consuming for medical experts. Furthermore, the reference method to characterize tissues is biopsy which is a local and invasive technique. Because of this, there is a huge interest for automatic and non-invasive approaches in order to characterize tumor. In this study we use a statistical model-based method to classify multiparametric MRI of brain rat tumors, which allows data quality control with atypical observations detection, and may provide a dictionary of tumor signatures. A previous study, [1], used a Gaussian mixture model to characterize pixels inside tumors. With this model, the observations are gathered into classes resulting from Gaussian distributions. However, this model is sensitive to outliers which degrade the relevance of the obtained groups. And inside a tumor, there could be a huge variability and so a lot of outliers. To account for this biological variability, we propose to use generalized Student distributions : the multivariate multi-scaled Student distributions (MMSD, [2]). The MMSD distribution extends the standard multivariate Student distribution by using the Gaussian scale mixture representation of Student distributions. This representation allows us to introduce multi-dimensional weights, which control different tail thickness of the distribution for each dimension, and provide a way to detect outlier data. In this way, we obtain a finer regulation of the influence of atypical data on the groups shapes, and so a greater flexibility of the clustering model. We use an Expectation-Maximization algorithm (EM) to adjust a MMSD mixture on brain tumor MRI. The number of classes inside the mixture is selected by minimizing the Bayesian information criterion (BIC). Our sample consists of healthy rats (n=8) and 4 groups of rats bearing a brain tumor model (n=8 per group), and 5 quantitative MRI parameter maps for each rat. We adjust a MMSD mixture on the healthy sample to detect tumor area in the tumor sample through the multi-dimensional weights. Then we characterize the tumor areas with another MMSD mixture and build a tumor dictionary which discriminates the 4 tumor