Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN). MATERIAL AND METHOD: MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system's performance is evaluated by categorizing glioblastoma patients based on hypoxia. RESULTS: The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy. CONCLUSION: The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.

Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement?

Objectives: Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods. Materials & Methods: Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups. Results: Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods. Conclusion: The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.

Impaired white matter integrity in infants and young children with autism spectrum disorder: What evidence does diffusion tensor imaging provide?

BACKGROUND: Abnormal functional connections are associated with impaired white matter tract integrity in the brain. Diffusion tensor imaging (DTI) is a promising method for evaluating white matter integrity in infants and young children. This work aims to shed light on the location and nature of the decrease in white matter integrity. METHODS: Here, the results of 19 studies have been presented that investigated white matter integrity in infants and young children (6 months to 12 years) with autism using diffusion tensor imaging. RESULTS: In most of the reviewed studies, an increase in Fractional Anisotropy (FA) and a decrease in Radial Diffusivity (RD) were reported in Corpus Callosum (CC), Uncinate Fasciculus (UF), Cingulum (Cg), Inferior Longitudinal Fasciculus (ILF), and Superior Longitudinal Fasciculus (SLF), and in the Inferior Fronto-Occipital Fasciculus (IFOF) tract, a decrease in FA and an increase in RD were reported. CONCLUSION: In the reviewed articles, except for one study, the diffusion indices were different compared to the control group.

Comparison of machine learning methods for the detection of focal cortical dysplasia lesions: decision tree, support vector machine and artificial neural network.

BACKGROUND: Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem. Among the new techniques for FCD lesion diagnosis, classification techniques can be of great help in FCD patient's detection from healthy individuals. METHODS: MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated. RESULTS: Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 90%, 100% and 95.8%, respectively; it was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN. CONCLUSION: Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.

Early differentiation of neurodegenerative diseases using the novel QSM technique: what is the biomarker of each disorder?

During neurodegenerative diseases, the brain undergoes morphological and pathological changes; Iron deposits are one of the causes of pathological changes in the brain. The Quantitative susceptibility mapping (QSM) technique, a type of magnetic resonance (MR) image reconstruction, is one of the newest diagnostic methods for iron deposits to detect changes in magnetic susceptibility. Numerous research projects have been conducted in this field. The purpose of writing this review article is to identify the first deep brain nuclei that undergo magnetic susceptibility changes during neurodegenerative diseases such as Alzheimer's or Parkinson's disease. The purpose of this article is to identify the brain nuclei that are prone to iron deposition in any specific disorder. In addition to the mentioned purpose, this paper proposes the optimal scan parameters and appropriate algorithms of each QSM reconstruction step by reviewing the results of different articles. As a result, The QSM technique can identify nuclei exposed to iron deposition in various neurodegenerative diseases. Also, the selection of scan parameters is different based on the sequence and purpose; an example of the parameters is placed in the tables. The BET toolbox in FSL, Laplacian-based phase-unwrapping process, the V_SHARP algorithm, and morphology-enabled dipole inversion (MEDI) method are the most widely used algorithms in various stages of QSM reconstruction.

Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process?

The presence of iron is essential for many biological processes in the body. But sometimes, for various reasons, the amount of iron deposition in different areas of the brain increases, which leads to problems related to the nervous system. Quantitative susceptibility mapping (QSM) is one of the newest magnetic resonance imaging (MRI)-based methods for assessing iron accumulation in target areas. This Narrative Review article aims to evaluate the performance of QSM compared to other methods of assessing iron deposition in the clinical field. Based on the results, we introduced related basic definitions, some neurodegenerative diseases, methods of examining iron deposition in these diseases, and their advantages and disadvantages. This article states that the QSM method can be introduced as a new, reliable, and non-invasive technique for clinical evaluations.

Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

BACKGROUND AND OBJECTIVES: Focal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented. METHODS: Magnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency. RESULTS: Neural network evaluation metrics-sensitivity, specificity, and accuracy-were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively. CONCLUSION: Analyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.