Tissue-type mapping of gliomas

Brain tumor classification for MRI images using dual-discriminator conditional generative adversarial network

This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using Dual-Discriminator Conditional Generative Adversarial Network (DDCGAN) for MRI images. The proposed system is implemented in the MATLAB programming language. In this study, images of the brain are taken from a dataset and processed to remove noise and enhance image quality. The brain pictures are taken from Brats MRI image dataset. The images are preprocessed using Structural interval gradient filtering to remove noises and improve the quality of the image. The preprocessing outcomes are given to feature extraction. The features are extracted by Empirical wavelet transform (EWT) and the extracted features are given to the Dual-discriminator conditional generative adversarial network (DDCGAN) for recognizing the brain tumor, which classifies the brain images into glioma, meningioma, pituitary gland, and normal. Then, the weight parameter of DDCGAN is optimized by utilizing Border Collie Optimization (BCO), which is a met a heuristic approach to handle the real world optimization issues. It maximizes the detection accurateness and reduced computational time. Implemented in MATLAB, the experimental results demonstrate that the proposed system achieves a high sensitivity of 99.58%. The BCO-DDCGAN-MRI-BTC method outperforms existing techniques in terms of precision and sensitivity when compared to methods like Kernel Basis SVM (KSVM-HHO-BTC), Joint Training of Two-Channel Deep Neural Network (JT-TCDNN-BTC), and YOLOv2 including Convolutional Neural Network (YOLOv2-CNN-BTC). The research findings indicate that the proposed method enhances the accuracy of brain tumor classification while reducing computational time and errors.

Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification

This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a SAGAN optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time.The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging. The accuracy acquired for the brain tumor identification from the proposed method is 99.29%. The proposed BTC-SAGAN-CHA-MRI technique achieves 18.29%, 14.09% and 7.34% higher accuracy and 67.92%,54.04%, and 59.08% less Computation Time when analyzed to the existing models, like Brain tumor diagnosis utilizing deep learning convolutional neural network with transfer learning approach (BTC-KNN-SVM-MRI); M3BTCNet: multi model brain tumor categorization under metaheuristic deep neural network features optimization (BTC-CNN-DEMFOA-MRI), and efficient method depending upon hierarchical deep learning neural network classifier for brain tumour categorization (BTC-Hie DNN-MRI) respectively.

Brain tumour classification using MRI images based on lenet with golden teacher learning optimization

Brain tumour (BT) is a dangerous neurological disorder produced by abnormal cell growth within the skull or brain. Nowadays, the death rate of people with BT is linearly growing. The finding of tumours at an early stage is crucial for giving treatment to patients, which improves the survival rate of patients. Hence, the BT classification (BTC) is done in this research using magnetic resonance imaging (MRI) images. In this research, the input MRI image is pre-processed using a non-local means (NLM) filter that denoises the input image. For attaining the effective classified result, the tumour area from the MRI image is segmented by the SegNet model. Furthermore, the BTC is accomplished by the LeNet model whose weight is optimized by the Golden Teacher Learning Optimization Algorithm (GTLO) such that the classified output produced by the LeNet model is Gliomas, Meningiomas, and Pituitary tumours. The experimental outcome displays that the GTLO-LeNet achieved an Accuracy of 0.896, Negative Predictive value (NPV) of 0.907, Positive Predictive value (PPV) of 0.821, True Negative Rate (TNR) of 0.880, and True Positive Rate (TPR) of 0.888.

Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI brain tumor classification

Magnetic resonance imaging (MRI) is a powerful tool for tumor diagnosis in human brain. Here, the MRI images are considered to detect the brain tumor and classify the regions as meningioma, glioma, pituitary and normal types. Numerous existing methods regarding brain tumor detection were suggested previously, but none of the methods accurately categorizes the brain tumor and consumes more computation period. To address these problems, an Evolutionary Gravitational Neocognitron Neural Network optimized with Marine Predators Algorithm is proposed in this article for MRI Brain Tumor Classification (EGNNN-VGG16-MPA-MRI-BTC). Initially, the brain MRI pictures are collected under Brats MRI image dataset. By using Savitzky-Golay Denoising approach, these images are pre-processed. The features are extracted utilizing visual geometry group network (VGG16). By utilizing VGG16, the features, like Grey level features, Haralick Texture features are extracted. These extracted features are given to EGNNN classifier, which categorizes the brain tumor as glioma, meningioma, pituitary gland and normal. Batch Normalization (BN) layer of EGNNN is eliminated and included with VGG16 layer. Marine Predators Optimization Algorithm (MPA) optimizes the weight parameters of EGNNN. The simulation is activated in MATLAB. Finally, the EGNNN-VGG16-MPA-MRI-BTC method attains 38.98%, 46.74%, 23.27% higher accuracy, 24.24%, 37.82%, 13.92% higher precision, 26.94%, 47.04%, 38.94% higher sensitivity compared with the existing AlexNet-SVM-MRI-BTC, RESNET-SGD-MRI-BTC and MobileNet-V2-MRI-BTC models respectively.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model

Background

The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation.

Methods

One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans.

Results

The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans.

Conclusions

Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study

In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired.

PURPOSE

To test whether MV grids can be classified with models trained with SV.

METHODS

Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score.

RESULTS

The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us.

DISCUSSION

The reasons for failure in the classification of the MV test set were related to the presence of artifacts.

A novel approach for neural networks based diagnosis and grading of stroke in tumor-affected brain MRIs

Recognition and diagnosis of stroke from magnetic resonance Image (MRIs) are significant for medical procedures in therapeutic standards. The primary goal of this scheme is the discovery of stroke in tumour locale in brain tissues influenced image. The probability of stroke is categorized on brain tumour influenced images into mild, moderate, or serious cases. The mild and moderate phases of stroke are recognized as "Ahead of schedule" findings and serious cases are distinguished as "Advance" determination. The proposed Glioblastoma brain tumour recognition strategy used the Multifaceted Brain Tumour Image Segmentation test open-access dataset for evaluating the presentation. The brain images are classified utilizing the Deep Neural Networks classification algorithm as normal and abnormal images. The tumour region is segmented from the identified set of abnormal images using the normalized graph cut algorithm. The stroke likelihood is identified using the Deep Neural Networks by analysing the proximity of tumour section in brain matters. The proposed stroke analysis framework accurately groups 10 images as "Right on time" stroke probability images and accomplishes 90% order rate. The proposed stroke prediction framework effectively characterizes images as "Advance" stroke probability images and accomplishes 90% characterization rate.

Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images

AIM

To evaluate 1.5 T magnetic resonance imaging (MRI) brain images with denoising procedures using deep learning-based reconstruction (dDLR) relative to the original 1.5 and 3 T images.

MATERIALS AND METHODS

Eleven volunteers underwent MRI at 3 and 1.5 T. Two-dimensional fast spin-echo T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR) imaging and diffusion-weighted imaging (DWI) sequences were performed. The dDLR method was applied to the 1.5 T data (dDLR-1.5 T), then the image quality of the dDLR-1.5 T data relative to the original 1.5 T and 3 T data was qualitatively and quantitatively assessed based on the structure similarity (SSIM) index; the signal-to-noise ratios (SNRs) of the grey matter (GM) and white matter (WM); and the contrast-to-noise ratios (CNRs) between the GM and WM (CNRgm-wm) and between the striatum (ST) and WM (CNRst-wm).

RESULTS

The perceived image quality, and SNRs and CNRs were significantly higher for the dDLR-1.5 T images versus the 1.5 T images for all sequences and almost comparable or even superior to those of the 3 T images. For DWI, the SNRs and CNRst-wm were significantly higher for the dDLR-1.5 T images versus the 3 T images.

CONCLUSION

The dDLR technique improved the image quality of 1.5 T brain MRI images. With respect to qualitative and quantitative measurements, the denoised 1.5 T brain images were almost equivalent or even superior to the 3 T brain images.

Automated brain tumour segmentation from multi-modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method

BACKGROUND

Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection systems need to be developed to decrease physicians' workload and improve the accuracy of segmentation.

METHODS

This paper proposes a level set method constrained by an intuitive artificial intelligence-based approach to perform brain tumour segmentation. By studying 3D brain tumour images, a new segmentation technique based on the Modified Particle Swarm Optimisation (MPSO), Darwin Particle Swarm Optimisation (DPSO), and Fractional Order Darwinian Particle Swarm Optimisation (FODPSO) algorithms were developed.

RESULTS

The introduced technique was verified according to the MICCAI RASTS 2013 database for high-grade glioma patients. The three algorithms were evaluated using different performance measures: accuracy, sensitivity, specificity, and Dice similarity coefficient to prove the performance and robustness of our 3D segmentation technique.

CONCLUSION

The result is that the MPSO algorithm consistently outperforms the DPSO and FO DPSO.

Convolutional neural networks to predict brain tumor grades and Alzheimer’s disease with MR spectroscopic imaging data

Purpose

To evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer’s disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions.

Methods

A shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a classification layer that outputs class predictions for the given brain voxel.

Results

Our CNN method separated glioma grades 3 and 4 and identified Alzheimer’s disease patients using MRSI and complementary MRI data with high MCC score (Area Under the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness over other popular methods as Partial Least Squares or Support Vector Machines. Also, our method automatically identified the spectral regions most important in the diagnosis process and we show that these are in good agreement with existing biomarkers from the literature.

Conclusion

Shallow CNNs models integrating image and spectral features improved quantitative and exploration and diagnosis of brain diseases for research and clinical purposes. Software is available at https://bitbucket.org/TeslaH2O/cnn_mrsi.

3D asymmetric expectation-maximization attention network for brain tumor segmentation

Automatic brain tumor segmentation on MRI is a prerequisite to provide a quantitative and intuitive assistance for clinical diagnosis and treatment. Meanwhile, 3D deep neural network related brain tumor segmentation models have demonstrated considerable accuracy improvement over corresponding 2D methodologies. However, 3D brain tumor segmentation models generally suffer from high computation cost. Motivated by a recently proposed 3D dilated multi-fiber network (DMF-Net) architecture that pays more attention to reduction of computation cost, we present in this work a novel encoder-decoder neural network, ie a 3D asymmetric expectation-maximization attention network (AEMA-Net), to automatically segment brain tumors. We modify DMF-Net by introducing an asymmetric convolution block into a multi-fiber unit and a dilated multi-fiber unit to capture more powerful deep features for the brain tumor segmentation. In addition, AEMA-Net further incorporates an expectation-maximization attention (EMA) module into the DMF-Net by embedding the EMA block in the third stage of skip connection, which focuses on capturing the long-range dependence of context. We extensively evaluate AEMA-Net on three MRI brain tumor segmentation benchmarks of BraTS 2018, 2019 and 2020 datasets. Experimental results demonstrate that AEMA-Net outperforms both 3D U-Net and DMF-Net, and it achieves competitive performance compared with the state-of-the-art brain tumor segmentation methods.

A whole-process interpretable and multi-modal deep reinforcement learning for diagnosis and analysis of Alzheimer's disease∗

Objective. Alzheimer's disease (AD), a common disease of the elderly with unknown etiology, has been adversely affecting many people, especially with the aging of the population and the younger trend of this disease. Current artificial intelligence (AI) methods based on individual information or magnetic resonance imaging (MRI) can solve the problem of diagnostic sensitivity and specificity, but still face the challenges of interpretability and clinical feasibility. In this study, we propose an interpretable multimodal deep reinforcement learning model for inferring pathological features and the diagnosis of AD.Approach. First, for better clinical feasibility, the compressed-sensing MRI image is reconstructed using an interpretable deep reinforcement learning model. Then, the reconstructed MRI is input into the full convolution neural network to generate a pixel-level disease probability risk map (DPM) of the whole brain for AD. The DPM of important brain regions and individual information are then input into the attention-based fully deep neural network to obtain the diagnosis results and analyze the biomarkers. We used 1349 multi-center samples to construct and test the model.Main results.Finally, the model obtained 99.6% ± 0.2%, 97.9% ± 0.2%, and 96.1% ± 0.3% area under curve in ADNI, AIBL and NACC, respectively. The model also provides an effective analysis of multimodal pathology, predicts the imaging biomarkers in MRI and the weight of each individual item of information. In this study, a deep reinforcement learning model was designed, which can not only accurately diagnose AD, but analyze potential biomarkers.Significance. In this study, a deep reinforcement learning model was designed. The model builds a bridge between clinical practice and AI diagnosis and provides a viewpoint for the interpretability of AI technology.

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images

Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.

Brain Hemorrhage Classification in CT Scan Images Using Minimalist Machine Learning

Over time, a myriad of applications have been generated for pattern classification algorithms. Several case studies include parametric classifiers such as the Multi-Layer Perceptron (MLP) classifier, which is one of the most widely used today. Others use non-parametric classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Adaboost, and Random Forest (RF). However, there is still little work directed toward a new trend in Artificial Intelligence (AI), which is known as eXplainable Artificial Intelligence (X-AI). This new trend seeks to make Machine Learning (ML) algorithms increasingly simple and easy to understand for users. Therefore, following this new wave of knowledge, in this work, the authors develop a new pattern classification methodology, based on the implementation of the novel Minimalist Machine Learning (MML) paradigm and a higher relevance attribute selection algorithm, which we call dMeans. We examine and compare the performance of this methodology with MLP, NB, KNN, SVM, Adaboost, and RF classifiers to perform the task of classification of Computed Tomography (CT) brain images. These grayscale images have an area of 128 × 128 pixels, and there are two classes available in the dataset: CT without Hemorrhage and CT with Intra-Ventricular Hemorrhage (IVH), which were classified using the Leave-One-Out Cross-Validation method. Most of the models tested by Leave-One-Out Cross-Validation performed between 50% and 75% accuracy, while sensitivity and sensitivity ranged between 58% and 86%. The experiments performed using our methodology matched the best classifier observed with 86.50% accuracy, and they outperformed all state-of-the-art algorithms in specificity with 91.60%. This performance is achieved hand in hand with simple and practical methods, which go hand in hand with this trend of generating easily explainable algorithms.

Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network

Multiple sclerosis (MS) is a chronic and autoimmune disease that forms lesions in the central nervous system. Quantitative analysis of these lesions has proved to be very useful in clinical trials for therapies and assessing disease prognosis. However, the efficacy of these quantitative analyses greatly depends on how accurately the MS lesions have been identified and segmented in brain MRI. This is usually carried out by radiologists who label 3D MR images slice by slice using commonly available segmentation tools. However, such manual practices are time consuming and error prone. To circumvent this problem, several automatic segmentation techniques have been investigated in recent years. In this paper, we propose a new framework for automatic brain lesion segmentation that employs a novel convolutional neural network (CNN) architecture. In order to segment lesions of different sizes, we have to pick a specific filter or size 3 × 3 or 5 × 5. Sometimes, it is hard to decide which filter will work better to get the best results. Google Net has solved this problem by introducing an inception module. An inception module uses 3 × 3, 5 × 5, 1 × 1 and max pooling filters in parallel fashion. Results show that incorporating inception modules in a CNN has improved the performance of the network in the segmentation of MS lesions. We compared the results of the proposed CNN architecture for two loss functions: binary cross entropy (BCE) and structural similarity index measure (SSIM) using the publicly available ISBI-2015 challenge dataset. A score of 93.81 which is higher than the human rater with BCE loss function is achieved.

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.

Metabolic NMR mapping with microgram tissue biopsy

This study explores the potential of profiling a microgram-scale soft tissue biopsy by NMR spectroscopy. The important elements of high resolution and high sensitivity for the spectral data are achieved through a unique probe, HR-μMAS, which allowed comprehensive profiling to be performed on microgram tissue for the first time under MAS conditions. Thorough spatially resolved metabolic maps were acquired across a coronal brain slice of rat C6 gliomas, which rendered the delineation of the tumor lesion. The results present a unique ex vivo NMR possibility to analyze tissue pathology that cannot be fully explored by the conventional approach, HR-MAS and in vivo MRS. Aside from the capability of analyzing a small localized region to track its specific metabolism, it could also offer the possibility to carry out longitudinal investigations on live animals due to the feasibility of minimally invasive tissue excision.

Peripheral inflammation is associated with micro-structural and functional connectivity changes in depression-related brain networks

Inflammation is associated with depressive symptoms and innate immune mechanisms are likely causal in some cases of major depression. Systemic inflammation also perturbs brain function and microstructure, though how these are related remains unclear. We recruited N = 46 healthy controls, and N = 83 depressed cases stratified by CRP (> 3 mg/L: N = 33; < 3 mg/L: N = 50). All completed clinical assessment, venous blood sampling for C-reactive protein (CRP) assay, and brain magnetic resonance imaging (MRI). Micro-structural MRI parameters including proton density (PD), a measure of tissue water content, were measured at 360 cortical and 16 subcortical regions. Resting-state fMRI time series were correlated to estimate functional connectivity between individual regions, as well as the sum of connectivity (weighted degree) of each region. Multiple tests for regional analysis were controlled by the false discovery rate (FDR = 5%). We found that CRP was significantly associated with PD in precuneus, posterior cingulate cortex (pC/pCC) and medial prefrontal cortex (mPFC); and with functional connectivity between pC/pCC, mPFC and hippocampus. Depression was associated with reduced weighted degree of pC/pCC, mPFC, and other nodes of the default mode network (DMN). Thus CRP-related increases in proton density-a plausible marker of extracellular oedema-and changes in functional connectivity were anatomically co-localised with DMN nodes that also demonstrated significantly reduced hubness in depression. We suggest that effects of peripheral inflammation on DMN node micro-structure and connectivity may mediate inflammatory effects on depression.

Main genetic differences in high-grade gliomas may present different MR imaging and MR spectroscopy correlates

OBJECTIVE

To assess whether the main genetic differences observed in high-grade gliomas (HGG) will present different MR imaging and MR spectroscopy correlates that could be used to better characterize lesions in the clinical setting.

METHODS

Seventy-nine patients with histologically confirmed HGG were recruited. Immunohistochemistry analyses for isocitrate dehydrogenase gene 1 (IDH1), alpha thalassemia mental retardation X-linked gene (ATRX), Ki-67, and p53 protein expression were performed. Tumour radiological features were examined on MR images. Metabolic profile and infiltrative pattern were assessed with MR spectroscopy. MR features were analysed to identify imaging-molecular associations. The Kaplan-Meier method and the Cox regression model were used to identify survival prognostic factors.

RESULTS

In total, 17.7% of the lesions were IDH1-mutated, 8.9% presented ATRX-mutated, 70.9% presented p53 unexpressed, and 22.8% had Ki-67 > 5%. IDH1 wild-type tumours had higher levels of mobile lipids (p = 0.001). The tumour-infiltrative pattern was higher in HGG with unexpressed p53 (p = 0.009). Mutated ATRX tumours presented higher levels of glutamate and glutamine (Glx) (p = 0.001). An association was observed between Glx tumour levels (p = 0.038) and Ki-67 expression (p = 0.008) with the infiltrative pattern. Survival analyses identified IDH1 status, age, and tumour choline levels as independent predictors of prognostic significance.

CONCLUSIONS

Our results suggest that IDH1-wt tumours are more necrotic than IDH1-mut. And that the presence of an infiltrative pattern in HGG is associated with loss of p53 expression, Ki-67 index, and Glx levels. Finally, tumour choline levels could be used as a predictive factor in survival in addition to the IDH1 status to provide a more accurate prediction of survival in HGG patients.

KEY POINTS

• IDH1-wt tumours present higher levels of mobile lipids than IDH1-mut. • Mutated ATRX tumours exhibit higher levels of glutamate and glutamine. • Loss of p53 expression, Ki-67 expression, and glutamate and glutamine levels may contribute to the presence of an infiltrative pattern in HGG.

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features

PURPOSE

The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features.

METHOD

Fluid-attenuated inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1WI_CE ) MRI of 90 patients diagnosed with glioblastoma were segmented using a semiautomatic algorithm and an interactive segmentation with two different raters. We analyzed the robustness of 108 radiomic features from five categories (intensity histogram, gray-level co-occurrence matrix, gray-level size-zone matrix (GLSZM), edge maps, and shape) using intra-class correlation coefficient (ICC) and Bland and Altman analysis.

RESULTS

Our results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SD_diffs ) ≤ 30%. Features computed from the histogram and co-occurrence matrices were found to be the most robust (ICC ≥ 0.8 and SD_diffs  ≤ 30% for most features in these groups). Features from GLSZM were shown to have mixed robustness. Edge, shape, and GLSZM features were the most impacted by the choice of segmentation method with the interactive method resulting in more robust features than the semiautomatic method. Finally, features computed from T1WI_CE and FLAIR images were found to have similar robustness when computed with the interactive segmentation method.

CONCLUSION

Semiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.