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Zhou L, Yang W, Liu Y, Li J, Zhao M, Liu G, Zhang J. Correlations between cognitive reserve, gray matter, and cerebrospinal fluid volume in healthy elders and mild cognitive impairment patients. Front Neurol 2024; 15:1355546. [PMID: 38497043 PMCID: PMC10941649 DOI: 10.3389/fneur.2024.1355546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Objective To explore the effect of cognitive reserve (CR) on brain volume and cerebrospinal fluid (CSF) in patients with mild cognitive impairment (MCI) and healthy elders (HE). Methods 31 HE and 50 MCI patients were collected in this study to obtain structural MRI, cognitive function, and composite CR scores. Educational attainment, leisure time, and working activity ratings from two groups were used to generate cognitive reserve index questionnaire (CRIq) scores. The different volumes of brain regions and CSF were obtained using uAI research portal in both groups, which were taken as the regions of interest (ROI), the correlation analysis between ROIs and CRIq scores were conducted. Results The scores of CRIq, CRIq-leisure time, and CRIq-education in HE group were significantly higher than patients in MCI group, and the montreal cognitive assessment (MoCA) and minimum mental state examination (MMSE) scores were positively correlated with the CRIq, CRIq-education in both groups, and were positively correlated with CRIq-leisure time in MCI group. The scores of auditory verbal learning test (AVLT) and verbal fluency test (VFT) were also positively correlated with CRIq, CRIq-leisure time, and CRIq-education in MCI group, but the score of AVLT was only positively correlated with CRIq in HE group. Moreover, in MCI group, the volume of the right middle cingulate cortex and the right parahippocampal gyrus were negatively correlated with the CRIq, and the volume of CSF, peripheral CSF, and third ventricle were positively correlated with the CRIq-leisure time score. The result of mediation analysis suggested that right parahippocampal gryus mediated the main effect of the relationship between CRIq and MoCA score in MCI group. Conclusion People with higher CR show better levels of cognitive function, and MCI patients with higher CR showed more severe volume atrophy of the right middle cingulate cortex and the right parahippocampal gyrus, but more CSF at a given level of global cognition.
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Affiliation(s)
- Liang Zhou
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wenxia Yang
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Yang Liu
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jiachen Li
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Mengmeng Zhao
- Shanghai United Imaging Intelligence, Shanghai, China
| | - Guangyao Liu
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
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Li C, Hui D, Wu F, Xia Y, Shi F, Yang M, Zhang J, Peng C, Feng J, Li C. Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI. Front Med (Lausanne) 2024; 10:1303501. [PMID: 38249966 PMCID: PMC10797132 DOI: 10.3389/fmed.2023.1303501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/08/2023] [Indexed: 01/23/2024] Open
Abstract
Background Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs). Methods 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models. Results Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.
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Affiliation(s)
- Chang Li
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Dongming Hui
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Chongqing, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China
| | - Mingguang Yang
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Jinrui Zhang
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chao Peng
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Junbang Feng
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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Shahidi R, Baradaran M, Asgarzadeh A, Bagherieh S, Tajabadi Z, Farhadi A, Korani SS, Khalafi M, Shobeiri P, Sadeghsalehi H, Shafieioun A, Yazdanifar MA, Singhal A, Sotoudeh H. Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis. Aging Clin Exp Res 2023; 35:2333-2348. [PMID: 37801265 DOI: 10.1007/s40520-023-02565-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/13/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. METHODS AND MATERIALS A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. RESULTS We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. CONCLUSION MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.
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Affiliation(s)
- Ramin Shahidi
- School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Mansoureh Baradaran
- Department of Radiology, Imam Ali Hospital, North Khorasan University of Medical Science, Bojnurd, Iran
| | - Ali Asgarzadeh
- Students Research Committee, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Sara Bagherieh
- Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zohreh Tajabadi
- Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Akram Farhadi
- Faculty of Health, Bushehr University of Medical Sciences, Bushehr, Iran
| | | | - Mohammad Khalafi
- Department of Radiology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Hamidreza Sadeghsalehi
- Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University Of Medical Sciences, Tehran, Iran
| | - Arezoo Shafieioun
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Aparna Singhal
- Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Alabama, USA
| | - Houman Sotoudeh
- Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Alabama, USA.
- O'Neal Comprehensive Cancer Center, UAB, The University of Alabama at Birmingham, JTN 333, 619 19th St S, Birmingham, AL, 35294, USA.
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Bevilacqua R, Barbarossa F, Fantechi L, Fornarelli D, Paci E, Bolognini S, Giammarchi C, Lattanzio F, Paciaroni L, Riccardi GR, Pelliccioni G, Biscetti L, Maranesi E. Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer's Disease: A Systematic Review of Studies in the Field. J Clin Med 2023; 12:5432. [PMID: 37629474 PMCID: PMC10455452 DOI: 10.3390/jcm12165432] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/17/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023] Open
Abstract
The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer's disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer's disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer's disease patients-both at the dementia and pre-dementia phases of the disease-from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer's disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer's.
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Affiliation(s)
- Roberta Bevilacqua
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Federico Barbarossa
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Lorenzo Fantechi
- Unit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, Italy; (L.F.); (D.F.)
| | - Daniela Fornarelli
- Unit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, Italy; (L.F.); (D.F.)
| | - Enrico Paci
- Unit of Radiology, IRCCS INRCA, 60127 Ancona, Italy;
| | - Silvia Bolognini
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Cinzia Giammarchi
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Fabrizia Lattanzio
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Lucia Paciaroni
- Unit of Neurology, IRCCS INRCA, 60127 Ancona, Italy; (L.P.); (G.P.)
| | | | | | | | - Elvira Maranesi
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
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Wu J, Xia Y, Wang X, Wei Y, Liu A, Innanje A, Zheng M, Chen L, Shi J, Wang L, Zhan Y, Zhou XS, Xue Z, Shi F, Shen D. uRP: An integrated research platform for one-stop analysis of medical images. FRONTIERS IN RADIOLOGY 2023; 3:1153784. [PMID: 37492386 PMCID: PMC10365282 DOI: 10.3389/fradi.2023.1153784] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/31/2023] [Indexed: 07/27/2023]
Abstract
Introduction Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible. Methods We present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable. Results and Discussion The uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.
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Affiliation(s)
- Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xuechun Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Aie Liu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Arun Innanje
- Department of Research and Development, United Imaging Intelligence Co., Ltd., Cambridge, MA, United States
| | - Meng Zheng
- Department of Research and Development, United Imaging Intelligence Co., Ltd., Cambridge, MA, United States
| | - Lei Chen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jing Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Liye Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yiqiang Zhan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Zhong Xue
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- Shanghai Clinical Research and Trial Center, Shanghai, China
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Zhou K, Piao S, Liu X, Luo X, Chen H, Xiang R, Geng D. A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction. Front Aging Neurosci 2023; 14:1073909. [PMID: 36726800 PMCID: PMC9884698 DOI: 10.3389/fnagi.2022.1073909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning. Methods In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve. Results Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification. Discussion The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.
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Affiliation(s)
- Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Rui Xiang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China,*Correspondence: Daoying Geng,
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