Multimodal MRI Research Articles

Multimodal MRI-Based Radiomic Nomogram for the Early Differentiation of Recurrence and Pseudoprogression of High-Grade Glioma.

Objective To evaluate the diagnostic value of multimodal MRI radiomics based on T2-weighted fluid attenuated inversion recovery imaging (T2WI-FLAIR) combined with T1-weighted contrast enhanced imaging (T1WI-CE) in the early differentiation of high-grade glioma recurrence from pseudoprogression. Methods A total of one hundred eighteen patients with brain gliomas who were diagnosed from March 2014 to April 2020 were retrospectively analyzed. According to the clinical characteristics, the patients were randomly split into a training group (n = 83) and a test group (n = 35) at a 7 : 3 ratio. The region of interest (ROI) was delineated, and 2632 radiomic features were extracted. We used multiple logistic regression to establish a classification model, including the T1 model, T2 model, and T1 + T2 model, to differentiate recurrence from pseudoprogression. The diagnostic efficiency of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and accuracy (ACC) and by analyzing the calibration curve of the nomogram and decision curve. Results There were 75 cases of recurrence and 43 cases of pseudoprogression. The diagnostic efficacies of the multimodal MRI-based radiomic model were relatively high. The AUC values and ACC of the training group were 0.831 and 77.11%, respectively, and the AUC values and ACC of the test group were 0.829 and 88.57%, respectively. The calibration curve of the nomogram showed that the discrimination probability was consistent with the actual occurrence in the training group, and the discrimination probability was roughly the same as the actual occurrence in the test group. In the decision curve analysis, the T1 + T2 model showed greater overall net efficiency. Conclusion The multimodal MRI radiomic model has relatively high efficiency in the early differentiation of recurrence from pseudoprogression, and it could be helpful for clinicians in devising correct treatment plans so that patients can be treated in a timely and accurate manner.

Radiomic study on preoperative multi-modal magnetic resonance images identifies IDH-mutant TERT promoter-mutant gliomas.

Gliomas with comutations of isocitrate dehydrogenase (IDH) genes and telomerase reverse transcriptase (TERT) gene promoter (IDHmut pTERTmut) show distinct biological features and respond to first-line treatment differently in comparison with other gliomas. This study aimed to characterize the IDHmut pTERTmut gliomas in multimodal MRI using the radiomic method and establish a precise diagnostic model identifying this group of gliomas. A total of 140 patients with untreated primary gliomas were admitted between 2016 and 2020 to West China Hospital as a discovery cohort, including 22 IDHmut pTERTmut patients. Thirty-four additional cases from a different hospital were included in the study as an independent validation cohort. A total of 3654 radiomic features were extracted from the preoperative multimodal MRI images (T1c, FLAIR, and ADC maps) and filtered in a data-driven approach. The discovery cohort was split into training and test sets by a 4:1 ratio. A diagnostic model (multilayer perceptron classifier) for detecting the IDHmut pTERTmut gliomas was trained using an automatic machine-learning algorithm named tree-based pipeline optimization tool (TPOT). The most critical radiomic features in the model were identified and visualized. The model achieved an area under the receiver-operating curve (AUROC) of 0.971 (95% CI, 0.902-1.000), the sensitivity of 0.833 (95% CI, 0.333-1.000), and the specificity of 0.966 (95% CI, 0.931-1.000) in the test set. The area under the precision-recall curve (AUCPR) was 0.754 (95% CI, 0.572-0.833) and the F1 score was 0.833 (95% CI, 0.500-1.000). In the independent validation set, the model reached 0.952 AUROC, 0.714 sensitivity, 0.963 specificity, 0.841 AUCPR, and 0.769 F1 score. MR radiomic features of the IDHmut pTERTmut gliomas represented homogenous low-complexity texture in three modalities. An accurate diagnostic model was constructed for detecting IDHmut pTERTmut gliomas using multimodal radiomic features. The most important features were associated with the homogenous simple texture of IDHmut pTERTmut gliomas in MRI images transformed using Laplacian of Gaussian and wavelet filters.

Multimodal MRI of myalgic encephalomyelitis/chronic fatigue syndrome: A cross-sectional neuroimaging study toward its neuropathophysiology and diagnosis.

IntroductionMyalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), is a debilitating illness affecting up to 24 million people worldwide but concerningly there is no known mechanism for ME/CFS and no objective test for diagnosis. A series of our neuroimaging findings in ME/CFS, including functional MRI (fMRI) signal characteristics and structural changes in brain regions particularly sensitive to hypoxia, has informed the hypothesis that abnormal neurovascular coupling (NVC) may be the neurobiological origin of ME/CFS. NVC is a critical process for normal brain function, in which glutamate from an active neuron stimulates Ca2+ influx in adjacent neurons and astrocytes. In turn, increased Ca2+ concentrations in both astrocytes and neurons trigger the synthesis of vascular dilator factors to increase local blood flow assuring activated neurons are supplied with their energy needs.This study investigates NVC using multimodal MRIs: (1) hemodynamic response function (HRF) that represents regional brain blood flow changes in response to neural activities and will be modeled from a cognitive task fMRI; (2) respiration response function (RRF) represents autoregulation of regional blood flow due to carbon dioxide and will be modeled from breath-holding fMRI; (3) neural activity associated glutamate changes will be modeled from a cognitive task functional magnetic resonance spectroscopy. We also aim to develop a neuromarker for ME/CFS diagnosis by integrating the multimodal MRIs with a deep machine learning framework.Methods and analysisThis cross-sectional study will recruit 288 participants (91 ME/CFS, 61 individuals with chronic fatigue, 91 healthy controls with sedentary lifestyles, 45 fibromyalgia). The ME/CFS will be diagnosed by consensus diagnosis made by two clinicians using the Canadian Consensus Criteria 2003. Symptoms, vital signs, and activity measures will be collected alongside multimodal MRI.The HRF, RRF, and glutamate changes will be compared among four groups using one-way analysis of covariance (ANCOVA). Equivalent non-parametric methods will be used for measures that do not exhibit a normal distribution. The activity measure, body mass index, sex, age, depression, and anxiety will be included as covariates for all statistical analyses with the false discovery rate used to correct for multiple comparisons.The data will be randomly divided into a training (N = 188) and a validation (N = 100) group. Each MRI measure will be entered as input for a least absolute shrinkage and selection operator—regularized principal components regression to generate a brain pattern of distributed clusters that predict disease severity. The identified brain pattern will be integrated using multimodal deep Boltzmann machines as a neuromarker for predicting ME/CFS fatigue conditions. The receiver operating characteristic curve of the identified neuromarker will be determined using data from the validation group.Ethics and study registryThis study was reviewed and approved by University of the Sunshine Coast University Ethics committee (A191288) and has been registered with The Australian New Zealand Clinical Trials Registry (ACTRN12622001095752).Dissemination of resultsThe results will be disseminated through peer reviewed scientific manuscripts and conferences and to patients through social media and active engagement with ME/CFS associations.

Diffusion-weighted imaging hyperintensities during the chronic stage of intracerebral hemorrhage with surgery: A new clinical situation or post-surgery artifact?

Background and objectiveDiffusion-weighted imaging (DWI) hyperintensities were occasionally seen at previous hematoma in patients several months after intracerebral hemorrhage with surgery. Whether they are newly occurred clinical situations or post-surgery changes is unknown. This study aims to investigate the prevalence and possible mechanisms for this phenomenon.MethodsWe retrospectively reviewed the MRI database for intracerebral hemorrhage with surgery after 3 months of disease onset in our hospital. We also prospectively performed repeated multimodal MRI scans for two patients at the chronic stage after surgery for intracerebral hemorrhage.ResultsWe found that 14 out of 23 patients (60.9%) had DWI hyperintensities at the site of previous hematoma 3 months after intracerebral hemorrhage with surgery. All the DWI lesions were hyperintense on T1- and T2-weighted imaging, most of which appeared long and narrow in shape. The DWI lesions were usually located adjacent to the thin wall of the previous hematoma cavity close to the lateral ventricle. They were more associated with the basal ganglia hemorrhage than with the lobar hemorrhage (P = 0.02) and were more frequently seen for those with intraventricular hemorrhage than without (P = 0.02). Prospectively repeated MRI exams of two patients revealed unchanged DWI hyperintensity during the 18- and 2-month follow-up, respectively.ConclusionThe DWI lesions at previous hematoma were commonly seen in patients after surgery for intracerebral hemorrhage at the chronic stage which would persist for years. We hypothesized a possible mechanism by which extracellular methemoglobin “islands” are formed with delayed or no absorption by macrophages from adjacent thin residual brain tissue. Unnecessary further examinations and treatment would be avoided by realizing this imaging phenomenon.

An Open MRI Dataset For Multiscale Neuroscience

Multimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from (i) resting-state functional imaging, (ii) diffusion tractography, (iii) microstructure covariance analysis, and (iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and function. MICA-MICs is available on the Canadian Open Neuroscience Platform data portal (https://portal.conp.ca) and the Open Science Framework (https://osf.io/j532r/).

Risk of recurrence after chemoradiotherapy identified by multimodal MRI and 18F-FDG-PET/CT in locally advanced cervical cancer

Background and purposeMRI, applying dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) sequences, and 18F-fluorodeoxyglucose (18F-FDG) PET/CT provide information about tumor aggressiveness that is unexploited in treatment of locally advanced cervical cancer (LACC). We investigated the potential of a multimodal combination of imaging parameters for classifying patients according to their risk of recurrence. Materials and methodsEighty-two LACC patients with diagnostic MRI and FDG-PET/CT, treated with chemoradiotherapy, were collected. Thirty-eight patients with MRI only were included for validation of MRI results. Endpoints were survival (disease-free, cancer-specific, overall) and tumor control (local, locoregional, distant). Ktrans, reflecting vascular function, apparent diffusion coefficient (ADC), reflecting cellularity, and standardized uptake value (SUV), reflecting glucose uptake, were extracted from DCE-MR, DW-MR and FDG-PET images, respectively. By applying an oxygen consumption and supply-based method, ADC and Ktrans parametric maps were voxel-wise combined into hypoxia images that were used to determine hypoxic fraction (HF). ResultsHF showed a stronger association with outcome than the single modality parameters. This association was confirmed in the validation cohort. Low HF identified low-risk patients with 95% precision. Based on the 50th SUV-percentile (SUV50), patients with high HF were divided into an intermediate- and high-risk group with high and low SUV50, respectively. This defined a multimodality biomarker, HF/SUV50. HF/SUV50 increased the precision of detecting high-risk patients from 41% (HF alone) to 57% and showed prognostic significance in multivariable analysis for all endpoints. ConclusionMultimodal combination of MR- and FDG-PET/CT-images improves classification of LACC patients compared to single modality images and clinical factors.

Brain tumor segmentation in multimodal MRI via pixel-level and feature-level image fusion.

Brain tumor segmentation in multimodal MRI volumes is of great significance to disease diagnosis, treatment planning, survival prediction and other relevant tasks. However, most existing brain tumor segmentation methods fail to make sufficient use of multimodal information. The most common way is to simply stack the original multimodal images or their low-level features as the model input, and many methods treat each modality data with equal importance to a given segmentation target. In this paper, we introduce multimodal image fusion technique including both pixel-level fusion and feature-level fusion for brain tumor segmentation, aiming to achieve more sufficient and finer utilization of multimodal information. At the pixel level, we present a convolutional network named PIF-Net for 3D MR image fusion to enrich the input modalities of the segmentation model. The fused modalities can strengthen the association among different types of pathological information captured by multiple source modalities, leading to a modality enhancement effect. At the feature level, we design an attention-based modality selection feature fusion (MSFF) module for multimodal feature refinement to address the difference among multiple modalities for a given segmentation target. A two-stage brain tumor segmentation framework is accordingly proposed based on the above components and the popular V-Net model. Experiments are conducted on the BraTS 2019 and BraTS 2020 benchmarks. The results demonstrate that the proposed components on both pixel-level and feature-level fusion can effectively improve the segmentation accuracy of brain tumors.

Advanced MRI imaging of nerve roots in lumbar radiculopathy due to discoradicular conflict: DWI, DTI, and T2 mapping with clinical and neurophysiological correlations.

To evaluate the lumbar nerve root alterations in patients with lumbar disc herniation sciatica using advanced multimodality MRI sequences and the correlations with clinical and neurophysiological findings. We prospectively evaluated 45 patients suffering from unilateral lumbar radiculopathy due to disco radicular conflict. All patients underwent MRI examinations using a standard MRI protocol and additional advanced MRI sequences (DWI, DTI, and T2 mapping sequences). Relative metrics of ADC, FA, and T2 relaxation times were recorded by placing ROIs at the pre-, foraminal, and post-foraminal level, either at the affected side or the contralateral side, used as control. All patients were also submitted to electromyography testing, recording the spontaneous activity, voluntary activity, F wave amplitude, latency, and motor evoked potentials (MEP) amplitude and latency, both at the level of the tibialis anterior and the gastrocnemius. Clinical features (diseases duration, pain, sensitivity, strength, osteotendinous reflexes) were also recorded. Among clinical features, we found a positive correlation of pain intensity with ADC values of the lumbar nerve roots. The presence of spontaneous activity was correlated with lower ADC values of the affected lumbar nerve root. F wave and MEP latency were correlated with decreased FA values at the foraminal level and increased values at the post-foraminal level. The same neurophysiological measures correlated positively with pre-foraminal T2 mapping values and negatively with post-foraminal T2 mapping values. Increased T2 mapping values at the foraminal level were correlated with disease duration. Evaluation of lumbar nerve roots using advanced MRI sequences may provide useful clinical information in patients with lumbar radiculopathy, potentially indicating active inflammation/myelinic damage (DTI, T2 mapping) and axonal damage/chronicity (DWI).

Frontostriatal and limbic contributions to cognitive decline in Parkinson's disease.

The circuitry underlying heterogenous cognitive profiles in Parkinson's disease (PD) remains unclear. The purpose of this study is to investigate whether structural changes in frontostriatal and limbic pathways contribute to different cognitive trajectories in PD. We obtained clinical and multimodal MRI data from 120 control and 122 PD subjects without dementia or severe motor disability. T1/T2-weighted images estimated volume, and diffusion imaging evaluated fractional anisotropy (FA) of frontostriatal (striatum and frontostriatal white matter [FSWM]) and limbic (hippocampus and fornix) structures. Montreal Cognitive Assessment (MoCA) gauged total and domain-specific (attention/executive and memory) cognitive function. Linear mixed-effects models were used to compare MRI and cognitive progression over 4.5years between controls and PD and evaluate associations between baseline MRI and cognitive changes in PD. At baseline, control and PD groups were comparable, except PD participants had smaller striatal volume (p<0.001). Longitudinally, PD showed faster decline in hippocampal volume, FSWM FA, and fornix FA (ps<.016), but not striatal volume (p=.218). Total and domain-specific MoCA scores declined faster in PD (ps<.030). In PD, lower baseline hippocampal volume (p=.005) and fornix FA (p=.032), but not striatal volume (p=.662) or FSWM FA (p=.143), were associated with faster total MoCA decline. Baseline frontostriatal metrics of striatal volume and FSWM FA were associated with faster attention/executive decline (p<.038), whereas lower baseline hippocampal volume was associated with faster memory decline (p=.005). In PD, frontostriatal structural metrics are associated with attention/executive tasks, whereas limbic changes correlated with faster global cognitive decline, particularly in memory tasks.

Multi-modality MRI for Alzheimer's disease detection using deep learning.

Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human body in vivo and non-invasively. It identifies the microstructure of white matter (WM) connectivity by estimating the movement of water molecules at each voxel. This makes possible the identification of the damage to WM integrity caused by Alzheimer's disease (AD) at its early stage, called mild cognitive impairment (MCI). Furthermore, the brain's gray matter (GM) atrophy characterizes the main structural changes in AD, which can be sensitively detected by structural MRI (sMRI) modality. In this research, we aimed to classify the Alzheimer's diseases stages by developing a novel multi-modality MRI (DTI and sMRI) fusion strategy to detect WM alterations and GM atrophy in AD patients. The latter is based on a 2-dimensional deep convolutional neural network (CNN) features extractor and a support vector machine (SVM) classifier. The fusion framework consists of merging features extracted from DTI scalar metrics [(fractional anisotropy (FA) and mean diffusivity (MD)], and GM using 2D-CNN and feeding them to SVM to classify AD versus cognitively normal (CN), AD versus MCI, and MCI versus CN. Our novel multimodal AD method demonstrates a superior performance with an accuracy of 99.79%, 99.6%, and 97.00% for AD/CN, AD/MCI, and MCI/CN respectively.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease.

To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction. 77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen. The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Nested U-Net with Enhanced Attention Gate and Compound Loss for Semantic Segmentation of Brain Tumor from Multimodal MRI

Nested U-Net with Enhanced Attention Gate and Compound Loss for Semantic Segmentation of Brain Tumor from Multimodal MRI

Capturing brain‐cognition relationship: Integrating task‐based fMRI across tasks markedly boosts prediction and test‐retest reliability

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n = 873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r = 0.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=∼.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r = 0.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r = 0.56) while still providing excellent test-retest reliability (ICC=∼.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.

Multimodal MRI in Leigh syndrome due to m.13513G > A.

Multimodal MRI in Leigh syndrome due to m.13513G > A.

Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury

In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R2 = 0.26 and R2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R2 = 0.49 and R2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.

Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network

Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.

3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images.

Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. Due to the development of modern technology, it is very valuable to use deep learning (DL) and multimodal MRI images to study brain tumor segmentation. In order to solve the problems of low efficiency and low accuracy of brain tumor segmentation, this paper proposes DL to conduct research on multimodal MRI image segmentation, aiming to make accurate diagnosis and treatment for doctors. In addition, this paper constructs an automatic diagnosis system for brain tumors, uses GLCM and discrete wavelet transform (DWT) to extract features from MRI images, and then uses a convolutional neural network (CNN) for final diagnosis; finally, through four. The comparison of the results between the two algorithms proves that the CNN algorithm has the better processing power and higher efficiency.

Multicenter clinical radiomics-integrated model based on [18F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas.

To develop a clinical radiomics-integrated model based on 18 F-fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and multi-modal MRI for predicting alpha thalassemia/mental retardation X-linked (ATRX) mutation status of IDH-mutant lower-grade gliomas (LGGs). One hundred and two patients (47 ATRX mutant-type, 55 ATRX wild-type) diagnosed with IDH-mutant LGGs (CNS WHO grades 1 and 2) were retrospectively enrolled. A total of 5540 radiomics features were extracted from structural MR (sMR) images (contrast-enhanced T1-weighted imaging, CE-T1WI; T2-weighted imaging, and T2WI), functional MR (fMR) images (apparent diffusion coefficient, ADC; cerebral blood volume, CBV), and metabolic PET images ([18F]FDG PET). The random forest algorithm was used to establish a clinical radiomics-integrated model, integrating the optimal multi-modal radiomics model with three clinical parameters. The predictive effectiveness of the models was evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA). The optimal multi-modal model incorporated sMR (CE-T1WI), fMR (ADC), and metabolic ([18F]FDG) images ([18F]FDG PET+ADC+ CE-T1WI) with the area under curves (AUCs) in the training and test groups of 0.971 and 0.962, respectively. The clinical radiomics-integrated model, incorporating [18F]FDG PET+ADC+CE-T1WI, three clinical parameters (KPS, SFSD, and ATGR), showed the best predictive effectiveness in the training and test groups (0.987 and 0.975, respectively). The clinical radiomics-integrated model with metabolic, structural, and functional information based on [18F]FDG PET and multi-modal MRI achieved promising performance for predicting the ATRX mutation status of IDH-mutant LGGs. • The clinical radiomics-integrated model based on [18F]FDG PET and multi-modal MRI achieved promising performance for predicting ATRX mutation status in LGGs. • The study investigated the value of multicenter clinical radiomics-integrated model based on [18F]FDG PET and multi-modal MRI in LGGs regarding ATRX mutation status prediction. • The integrated model provided structural, functional, and metabolic information simultaneously and demonstrated with satisfactory calibration and discrimination in the training and test groups (0.987 and 0.975, respectively).

Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision

Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of tumors.

Association of Microglial Activation With Spontaneous ARIA-E and CSF Levels of Anti-Aβ Autoantibodies.

Background and ObjectivesAmyloid-related imaging abnormalities suggestive of vasogenic edema or sulcal effusion (ARIA-E) are the most common adverse events complicating Alzheimer disease (AD) immunotherapy with anti–β-amyloid (Aβ) monoclonal antibodies. ARIA-E can also occur spontaneously in cerebral amyloid angiopathy–related inflammation (CAA-ri), a rare autoimmune encephalopathy associated with increased CSF levels of anti-Aβ autoantibodies. Although the pathophysiologic mechanisms of ARIA-E remain to be fully elucidated, experimental evidence from ex vivo studies suggests that gantenerumab and aducanumab enable microglial activation. However, the in vivo evidence for a direct association between neuroinflammation and ARIA-E in patients with high CSF anti-Aβ (auto)antibody levels has never been demonstrated.MethodsThe spatial distribution and temporal variations of microglial activation associated with levels of anti-Aβ autoantibodies at (sub)acute presentation of ARIA-E and after corticosteroid therapy were evaluated in a longitudinal case series of patients with CAA-ri, the spontaneous variant of the iatrogenic ARIA-E reported in Aβ-lowering immunotherapy with monoclonal antibodies. Multimodal and multiparametric MRI was used for CAA and ARIA-E severity quantification, according to validated scoring system; CSF testing for anti-Aβ autoantibodies and AD biomarkers; 11C-PK11195 PET for activated microglia.ResultsAt (sub)acute presentation, we found focal peaks of microglial activation having a greater spatial colocalization with ARIA-E compared with chronic age-related white matter change imaging abnormalities. The severity of ARIA-E and the magnitude of the associated microglial activation were greater in patients having AD and severe CAA concomitant disease compared with patients having CAA only. CSF anti-Aβ autoantibodies at presentation were high in all patients and markedly decreased at posttreatment follow-up, in parallel with clinical resolution of acute symptoms, reduced ARIA-E severity, and reduced microglial activation.DiscussionOur findings extend the current notion of ARIA-E by providing the first in vivo 11C-PK11195 PET evidence for an association between microglial activation and the magnitude and severity of ARIA-E in patients with increased CSF concentration of anti-Aβ autoantibodies and comorbid AD and CAA disease. Our results highlight CSF testing for anti-Aβ autoantibodies as a promising diagnostic, prognostic, and therapy response biomarker to help guide future treatment and management decisions in real clinical practice and clinical trials.