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

Zhao, Kun; Ding, Yanhui; Han, Ying; Fan, Yong; Alexander-Bloch, Aaron; Han, Tong; Jin, Depeng; Liu, Bing; Lu, Jie; Song, Chengyuan; Wang, Pan; Wang, Dawei; Wang, Qing; Xu, Kaibin; Yang, Hongwei; Yao, Hongxiang; Zheng, Yuanjie; Yu, Chunshui; Zhou, Bo; Zhang, Xinqing; Zhou, Yuying; Jiang, Tianzi; Zhang, Xi; Liu, Yong

doi:10.1016/j.scib.2020.04.003

Cited by 82 publications

(90 citation statements)

References 66 publications

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“…Radiomics has been widely used in clinical oncology studies (Ji et al, 2020;Zhao K. et al, 2020); the medical images can be converted into feature sets that can be used to characterize tumor characteristics by a series of algorithms (Lambin et al, 2012;Ji et al, 2020). Radiomics has now been widely used in the study of neuropsychological diseases, their diagnosis, and their neurological mechanism (Sun et al, 2018;Huang K. et al, 2019;Mo et al, 2019;Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson’s disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

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Section: Introductionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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“…Numerous studies emphasized the importance of radiomics (Gillies et al, 2016), and also took it as a bridge between imaging and personalized medicine (Lambin et al, 2017). More importantly, radiomics also improve the power of the precision of diagnosis, treatment, and prognosis of the tumor (Aerts et al, 2014, Coroller et al, 2015, Huang et al, 2018, and diagnosis of AD (Sorensen et al, 2016, Sorensen et al, 2017, Feng et al, 2018, Zhao et al, 2020. Hereby, the radiomics is a method that extracts a large number of features of the brain region with high reproducibility (Li et al, 2019) that provide a better measure to facilitate better characteristics of the brain region.…”

Section: Discussionmentioning

confidence: 99%

“…The structure of the brain region was controlled by gene expression (Liu et al, 2004, Warden andMayfield, 2017), and the variations in intensity or pattern of the brain region can be reflected by the radiomics feature (Wang et al, 2019), these results further speculated the gene expression can be reflected with the R2SN. In brief, R2SN has a genetic basis, and those findings provided the potential possibility to estimate the risk of the gene by using R2SN and also provided certain evidence for genetic disease research, such as Alzheimer's disease (Zhao et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…All features were described in the study by Aerts and colleagues (Aerts et al, 2014) and implemented as in-house MATLAB scripts (https://github.com/YongLiulab/). The definitions and detailed descriptions of the radiomics features can be found in previous publications (Aerts et al, 2014, Feng et al, 2018, Zhao et al, 2020.…”

Section: Data Preprocessing and Radiomics Feature Extractionmentioning

confidence: 99%

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Regional radiomics similarity networks (R2SN) in the human brain: reproducibility, small-world and biological basis

Zhao

Zheng

Che

et al. 2020

Preprint

Self Cite

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BackgroundStructural covariance network (SCN) has been applied successfully to structural magnetic resonance imaging (MRI) study. However, most SCNs were constructed by the unitary marker, which was insensitive for the different disease phases. The aim of this study is to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis.MethodsRegional radiomics similarity network (R2SN) was constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject. We further assessed the small-world property of R2SN using the graph theory method, as well as the reproducibility in the different datasets and the reliability with test-retest analysis. The relationship between the R2SN and inter-regional co-expression of gene enriched was also explored, as well as the relationship with general intelligence.ResultsThe R2SN can be replicated in different datasets, also regardless of using different feature subsets. The R2SN showed high reliability with the test-retest analysis (ICC>0.7). Besides, the small-word property (σ>2) and the high correlation with the gene expression (R=0.24, P<0.001) and the general intelligence was found by R2SN.ConclusionR2SN provides a novel, reliable, and biologically plausible method to understand human morphological covariance based on structural MRI.Impact StatementImaging biomarkers are the cornerstone of modern radiology, and the development of valid biomarkers is crucial for optimizing individualized prediction in neurological disorders like AD. Thus, the development of the data mining method from neuroimaging is crucial for adding the biomarkers of disease. This study confirmed that R2SN provides a novel, robust and biologically plausible model and a new perspective for understanding the human brain, therefore. Thus, the R2SN has great promise in further study.

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“…In the ROI-based methods, the anatomic volume and thickness of predefined regions [19]- [24] are applied for AD diagnosis. Recently, the texture, shape and deep features of the hippocampal region are extracted by convolutional neural network (CNN) [25]- [27] to enhance the classification performance. However, the ROIs must be defined in advance and these regions may not cover all biomarkers or span multiple ROI, resulting in the loss of discriminant features.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Landmarks and DAG Network Learning for Alzheimer’s Disease Diagnosis

et al. 2020

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

Cited by 82 publications

References 66 publications

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Regional radiomics similarity networks (R2SN) in the human brain: reproducibility, small-world and biological basis

Anatomical Landmarks and DAG Network Learning for Alzheimer’s Disease Diagnosis

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