Radiomic Features of Hippocampal Subregions in Alzheimer's Disease and Amnestic Mild Cognitive Impairment

Correlation of [18F]florbetaben textural features and age of onset of Alzheimer's disease: a principal components analysis approach

BACKGROUND

When Alzheimer's disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text]F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). We conducted [[Formula: see text]F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-[Formula: see text] ([Formula: see text]) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student's t-test.

RESULTS

We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [[Formula: see text]F]florbetaben uptake than LOAD patients in those four brain regions.

CONCLUSIONS

Early-onset AD implies a very irregular pattern of [Formula: see text] deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.

Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.

Comprehensive classification models based on amygdala radiomic features for Alzheimer's disease and mild cognitive impairment

The amygdala is an important part of the medial temporal lobe and plays a pivotal role in the emotional and cognitive function. The aim of this study was to build and validate comprehensive classification models based on amygdala radiomic features for Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). For the amygdala, 3360 radiomic features were extracted from 97 AD patients, 53 aMCI patients and 45 normal controls (NCs) on the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images. We used maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) to select the features. Multivariable logistic regression analysis was performed to build three classification models (AD-NC group, AD-aMCI group, and aMCI-NC group). Finally, internal validation was assessed. After two steps of feature selection, there were 5 radiomic features remained in the AD-NC group, 16 features remained in the AD-aMCI group and the aMCI-NC group, respectively. The proposed logistic classification analysis based on amygdala radiomic features achieves an accuracy of 0.90 and an area under the ROC curve (AUC) of 0.93 for AD vs. NC classification, an accuracy of 0.81 and an AUC of 0.84 for AD vs. aMCI classification, and an accuracy of 0.75 and an AUC of 0.80 for aMCI vs. NC classification. Amygdala radiomic features might be early biomarkers for detecting microstructural brain tissue changes during the AD and aMCI course. Logistic classification analysis demonstrated the promising classification performances for clinical applications among AD, aMCI and NC groups.

Radiomics-based classification models for HBV-related cirrhotic patients with covert hepatic encephalopathy

INTRODUCTION

The significant abnormalities of precuneus (PC), which are associated with brain dysfunction, have been identified in cirrhotic patients with covert hepatic encephalopathy (CHE). The present study aimed to apply radiomics analysis to identify the significant radiomic features in PC and their subregions, combine with clinical risk factors, then build and evaluate the classification models for CHE diagnosis.

METHODS

106 HBV-related cirrhotic patients (54 had current CHE and 52 had non-CHE) underwent the three-dimensional T1-weighted imaging. For each participant, PC and their subregions were segmented and extracted a large number of radiomic features and then identified the features with significant discriminative power as the radiomics signature. The logistic regression analysis was employed to develop and evaluate the classification models, which are constructed using the radiomics signature and clinical risk factors.

RESULTS

The classification model (R-C model) achieved best diagnostic performance, which incorporated radiomics signature (4 radiomic features from right PC), venous blood ammonia, and the Child-Pugh stage. And the area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, and accuracy values were 0.926, 1.000, 0.765, and 0.848, in the testing set. Application of the radiomics nomogram in the testing set still showed a good predictive accuracy.

CONCLUSIONS

This study presented the radiomic features of the right PC, as a potential image marker of CHE. The radiomics nomogram that incorporates the radiomics signature and clinical risk factors may facilitate the individualized prediction of CHE.

Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

BACKGROUND

Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients.

OBJECTIVE

To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles.

METHODS

A total of 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated.

RESULTS

The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set.

CONCLUSION

Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.

Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study

Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into "converters" and "nonconverters" according to individuals' future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer's Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7-95.9% and 87.1-90.8% in the validation set and 81.9-89.1% and 83.2-83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-β deposition and predicted future cognitive decline (areas under the curve 0.649-0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.

Magnetic Resonance Texture Analysis in Alzheimer's disease

Texture analysis is an emerging field that allows mathematical detection of changes in MRI signals that are not visible among image pixels. Alzheimer's disease, a progressive neurodegenerative disease, is the most common cause of dementia. Recently, multiple texture analysis studies in patients with Alzheimer's disease have been performed. This review summarizes the main contributors to Alzheimer's disease-associated cognitive decline, presents a brief overview of texture analysis, followed by review of various MR imaging texture analysis applications in Alzheimer's disease. We also discuss the current challenges for widespread clinical utilization. MR texture analysis could potentially be applied to develop neuroimaging biomarkers for use in Alzheimer's disease clinical trials and diagnosis.

Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer's disease

BACKGROUND AND PURPOSE

Texture analysis of magnetic resonance imaging (MRI) brain scans have been proposed as a promising tool in the early diagnosis of Alzheimer's disease (AD), but its biological correlates remain unknown. In this study, we examined the relationship between MRI texture features and AD pathology.

METHODS

The study included 150 participants who had a 3.0T T1-weighted image, amyloid-β positron emission tomography (PET), and tau PET within 3 months of each other. In each of six brain regions (hippocampus, precuneus, and entorhinal, middle temporal, posterior cingulate and superior frontal cortices), linear regression analyses adjusting for age and sex was performed to examine the effects of regional amyloid-β and tau burden on regional texture features. We also compared neuroimaging measures based on pathological severity using ANOVA.

RESULTS

In all regions, tau burden (p < 0.05), but not amyloid-β burden, were associated with a certain texture feature that varied with the region's cytoarchitecture. Specifically, autocorrelation and cluster shade were associated with tau burden in allocortical and periallocortical regions, whereas entropy and contrast were associated with tau burden in neocortical regions. Mean signal intensity of each region did not show any associations with AD pathology. The values of the region-specific textures also varied across groups of varying pathological severity.

CONCLUSIONS

Our results suggest that textures of T1-weighted MRI reflect changes in the brain that are associated with regional tau burden and the local cytoarchitecture. This study provides insight into how MRI texture can be used for detection of microstructural changes in AD.

Dysfunctional Architecture Underlies White Matter Hyperintensities with and without Cognitive Impairment

BACKGROUND

White matter hyperintensities (WMH) are common in older adults and are associated with cognitive decline. However, little is known about the functional changes underlying cognitive decline in WMH subjects.

OBJECTIVES

To investigate whole-brain functional connectivity (FC) underpinnings of cognitive decline in WMH subjects using univariate and multivariate analyses.

METHODS

Twenty-three WMH subjects with mild cognitive impairment (WMH-MCI), 43 WMH subjects with no cognitive impairment (WMH-nCI), and 55 healthy controls underwent resting-state functional MRI scans. Whole-brain FC was calculated using the fine-grained human Brainnetome Atlas, followed by performance of between-group comparisons and FC-cognition correlation analysis. A multivariate analysis using support vector machine (SVM) was performed to classify WMH-MCI and WMH-nCI subjects based on FC.

RESULTS

Both the WMH-MCI and WMH-nCI subjects exhibited characteristic impaired FC patterns. Markedly reduced FC involving subcortical nuclei and cortical hub regions of cognitive networks, especially the cingulate cortex, was identified in the WMH-MCI patients. In the WMH-MCI group, several connections involving the cingulate cortex were associated with cognitive decline. The exploratory mediation analyses indicated that FC alterations could partially explain the association between WMH and cognition. Furthermore, an SVM classifier based on FC distinguished WMH-MCI and WMH-nCI subjects with 78.8% accuracy. Connections that contributed most to the classification showed a similar distribution as the connections identified in the univariate analysis.

CONCLUSIONS

This study provides a new window into the pathophysiology of cognitive impairment in WMH subjects and offer a novel and potential approach for early detection of the cognitive impairment in WMH subjects at the individual level.

Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Roadmap for Moving Forward

Objective

To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use.

Materials and Methods

PubMed MEDLINE and EMBASE were searched using the terms ‘cognitive impairment’ or ‘Alzheimer’ or ‘dementia’ and ‘radiomic’ or ‘texture’ or ‘radiogenomic’ for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science.

Results

The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence.

Conclusion

The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.

Use of radiomic features and support vector machine to discriminate subjective cognitive decline and healthy controls

Subjective cognitive decline (SCD) is a high-risk preclinical stage in the progress of Alzheimer's disease (AD). Its timely diagnosis is of great significance for older adults. Though multi-parameter magnetic resonance imaging (MPMRI) is a noninvasive neuroimaging technique to detect SCD, the lack of biomarkers and computed aided diagnosis (CAD) tools is a major concern for its application. Radiomics, a high-dimensional imaging feature extraction method, has been widely used for identifying biomarkers and developing CAD tools in oncological studies. Therefore, in this study, we aimed to investigate whether the radiomic approach could be used for the diagnosis of SCD. In the proposed radiomic approach, we mainly performed four steps: image preprocessing, feature extraction and screening, and classification. The dataset from Xuanwu Hospital, Beijing, China, was used in this study, including 105 healthy controls (HC) and 130 SCD subjects. All subjects were divided into one training & validation group and one test group. We extracted 30128 radiomic features from MPMRI of each subject. The t-test, autocorrelation, and Fisher score were performed for feature selection, and we deployed the support vector machine (SVM) for classification. The above process was performed 100 times with 5-fold cross-validation. The results showed that the accuracy, sensitivity, and specificity of classification was 89.03%±5.37%, 85.44%±9.28% and 91.97%±6.38% in the validation set and 84.70%±4.68%, 86.98%±10.49% and 82.59%±7.07% in the test set. In conclusion, this study has shown that the radiomic approach could be used to discriminate SCD and HC with high accuracy and sensitivity effectively.

Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics

BACKGROUND

Accurately diagnosing schizophrenia is still challenging due to the lack of validated biomarkers. Here, we aimed to investigate whether radiomic features in bilateral hippocampal subfields from magnetic resonance images (MRIs) can differentiate patients with schizophrenia from healthy controls (HCs).

METHODS

A total of 152 participants with MRI (86 schizophrenia and 66 HCs) were allocated to training (n = 106) and test (n = 46) sets. Radiomic features (n = 642) from the bilateral hippocampal subfields processed with automatic segmentation techniques were extracted from T1-weighted MRIs. After feature selection, various combinations of classifiers (logistic regression, extra-trees, AdaBoost, XGBoost, or support vector machine) and subsampling were trained. The performance of the classifier was validated in the test set by determining the area under the curve (AUC). Furthermore, the association between selected radiomic features and clinical symptoms in schizophrenia was assessed.

RESULTS

Thirty radiomic features were identified to differentiate participants with schizophrenia from HCs. In the training set, the AUC exhibited poor to good performance (range: 0.683-0.861). The best performing radiomics model in the test set was achieved by the mutual information feature selection and logistic regression with an AUC, accuracy, sensitivity, and specificity of 0.821 (95% confidence interval 0.681-0.961), 82.1%, 76.9%, and 70%, respectively. Greater maximum values in the left cornu ammonis 1-3 subfield were associated with a higher severity of positive symptoms and general psychopathology in participants with schizophrenia.

CONCLUSION

Radiomic features from hippocampal subfields may be useful biomarkers for identifying schizophrenia.

Normative Baseline for Radiomics in Brain MRI: Evaluating the Robustness, Regional Variations, and Reproducibility on FLAIR Images

BACKGROUND

Radiomics in neuroimaging has gained momentum as a noninvasive prediction tool not only to differentiate between types of brain tumors, but also to create phenotypic signatures in neurological and neuropsychiatric disorders. However, there is currently little understating about the robustness and reproducibility of radiomic features in a baseline normative population.

PURPOSE

To investigate the intra- and interscanner reproducibility, spatial robustness, and sensitivity of radiomics on fluid attenuation inversion recovery (FLAIR) images, which are widely used in neuro-oncology investigations.

STUDY TYPE

Retrospective.

POPULATION

Three separate datasets of healthy controls: 1) 87 subjects (age range 12-64 years), 2) intrascanner three timepoints, four subjects, and 3) interscanner, eight subjects at three different sites.

FIELD STRENGTH/SEQUENCE

T₂ -weighted FLAIR at 1.5T and 3.0T.

ASSESSMENT

Spatial variance across lobes, and their relation with age/gender, intra- and inter-scanner reproducibility (with and without site harmonization) of radiomics.

STATISTICAL TESTS

Analysis of variance (ANOVA), interclass correlation (ICC), coefficient of variation (CoV), Bland-Altman analysis.

RESULTS

Analysis of data revealed no differences between genders; however, multiple radiomic features were highly associated with age (P < 0.05). Spatial variability was also evaluated where only 29.04% gray matter and 38.7% white matter features demonstrated an ICC >0.5. Furthermore, the results demonstrated intra-scanner reliability (ICC >0.5); however, inter-scanner reproducibility was poor, with ICC < 0.5 for 82% gray matter and 78.5% white matter features. The inter-scanner reliability improved (ICC < 0.5 for 39.67% gray matter and 38% white matter features) using site-harmonization techniques.

DATA CONCLUSION

These findings suggest that, accounting for age, spatial locations in radiomics-based analysis and use of intersite radiomics harmonization is crucial before interpreting these features for pathological inference. Level of Evidence 3. Technical Efficacy Stage 1. J. MAGN. RESON. IMAGING 2021;53:394-407.

Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD-associated functional brain alterations using one of the world's largest resting-state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta-analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default-mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid-β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave-one-site-out cross-validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini-Mental State Examination scores: 0.56, p < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer's disease: diagnosis, longitudinal progress and biological basis

Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation remains unclear. The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging (MRI) markers for AD. Multivariate classifier-based support vector machine (SVM) analysis provided individual-level predictions for distinguishing AD patients (n = 261) from normal controls (NCs; n = 231) with an accuracy of 88.21% and intersite cross-validation. Further analyses of a large, independent the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings. In MCI groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up. These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI, and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus. The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.

Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD.

Methods: T2-weighted images from 69 PD patients and 69 age- and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group.

Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986–0.9833) and 0.7732 (95% CI: 0.6292–0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066–0.9469) and 0.7143 (95% CI: 0.5540–0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05).

Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.

Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls

Background

Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD.

Methods

In this retrospective multicohort study, we collected ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times.

Results

Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively.

Conclusions

This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

OBJECTIVE

Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS.

METHODS

Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS.

RESULTS

The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance. Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration.

SIGNIFICANCE

Machine learning with quantitative clinical and radiomics features is shown to improve HS detection. HS-related structural alterations were similar in the MRI-positive and MRI-negative HS patient groups, indicating that misdiagnosis originates mainly from empirical interpretation. The cortical folding complexity of the temporal pole is a potentially valuable feature for exploring the nature of HS.

Robustness of radiomic features in magnetic resonance imaging: review and a phantom study

Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image. These imaging biomarkers can aid in the generation of prediction models aimed to further personalized medicine. However, the generalizability of the model is dependent on the robustness of these features. The purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance imaging. Additionally, a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions (signal to noise ratio, region of interest delineation, voxel size change and normalization methods) using intraclass correlation coefficients. The features extracted in this phantom study include first order, shape, gray level cooccurrence matrix and gray level run length matrix. Many features are found to be non-robust to changing parameters. Feature robustness assessment prior to feature selection, especially in the case of combining multi-institutional data, may be warranted. Further investigation is needed in this area of research.

ASAF: altered spontaneous activity fingerprinting in Alzheimer's disease based on multisite fMRI

Several monocentric studies have noted alterations in spontaneous brain activity in Alzheimer's disease (AD), although there is no consensus on the altered amplitude of low-frequency fluctuations in AD patients. The main aim of the present study was to identify a reliable and reproducible abnormal brain activity pattern in AD. The amplitude of local brain activity (AM), which can provide fast mapping of spontaneous brain activity across the whole brain, was evaluated based on multisite rs-fMRI data for 688 subjects (215 normal controls (NCs), 221 amnestic mild cognitive impairment (aMCI) 252 AD). Two-sample t-tests were used to detect group differences between AD patients and NCs from the same site. Differences in the AM maps were statistically analyzed via the Stouffer's meta-analysis. Consistent regions of lower spontaneous brain activity in the default mode network and increased activity in the bilateral hippocampus/parahippocampus, thalamus, caudate nucleus, orbital part of the middle frontal gyrus and left fusiform were observed in the AD patients compared with those in NCs. Significant correlations (P < 0.05, Bonferroni corrected) between the normalized amplitude index and Mini-Mental State Examination scores were found in the identified brain regions, which indicates that the altered brain activity was associated with cognitive decline in the patients. Multivariate analysis and leave-one-site-out cross-validation led to a 78.49% prediction accuracy for single-patient classification. The altered activity patterns of the identified brain regions were largely correlated with the FDG-PET results from another independent study. These results emphasized the impaired brain activity to provide a robust and reproducible imaging signature of AD.

Characterization of Alzheimer conditions in MR images using volumetric and sagittal brainstem texture features

BACKGROUND AND OBJECTIVE

Brainstem analysis in Magnetic Resonance Images is essential to detect Alzheimer's condition in the preclinical stages. In this work, an attempt has been made to segment the brainstem in sagittal (2D) and volumetric (3D) images and evaluate texture changes to differentiate Alzheimer's disease (AD) stages.

METHOD

The images obtained from a public access database are spatial normalized, skull stripped and contrast enhanced. Morphological Reconstruction based Fast and Robust Fuzzy 'C' Means technique is used to cluster the brain tissue in preprocessed images into three groups namely cerebrospinal fluid, grey matter and white matter. Brainstem is segmented from the white matter tissue using connected component labelling. Texture features from volumetric and sagittal brainstem slices are extracted and its statistical significance is evaluated.

RESULTS

Results show that the proposed approach is able to segment the brainstem from all the considered images. Variation in texture is observed to be less than 2% among sagittal brainstem slices. Additionally, midsagittal and volumetric features are correlated, suggesting that midsagittal brainstem structure gives an estimate of brainstem volume. Texture features extracted from midsagittal slice shows significant variation (p < 0.05) and is able to differentiate AD classes.

CONCLUSION

Midsagittal brainstem texture features are able to capture the changes occurring in the early stages of disease condition. As the distinction of AD in preclinical stage is complex and clinically significant, this approach could be useful for early diagnosis of the disease.

Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease

Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and ¹⁸F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell’s C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.