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Bao YW, Wang ZJ, Shea YF, Chiu PKC, Kwan JS, Chan FHW, Mak HKF. Combined quantitative amyloid-β PET and structural MRI features improve Alzheimer's Disease classification in random forest model - A multicenter study. Acad Radiol 2024:S1076-6332(24)00426-4. [PMID: 39003227 DOI: 10.1016/j.acra.2024.06.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/18/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
RATIONALE AND OBJECTIVES Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD. MATERIAL AND METHODS We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aβ features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification. RESULTS The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aβ PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aβ PET features contributed more to differentiating AD from others. CONCLUSION Our study demonstrated the discriminative ability of Aβ PET features in differentiating AD from OHC and MCI. A combination of Aβ PET and structural MRI features can improve the RF model performance.
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Affiliation(s)
- Yi-Wen Bao
- Department of Medical Imaging Center, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China (Y-W.B.)
| | - Zuo-Jun Wang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China (Z-J.W., H.K-F.M.)
| | - Yat-Fung Shea
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Patrick Ka-Chun Chiu
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Joseph Sk Kwan
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Felix Hon-Wai Chan
- Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China (Z-J.W., H.K-F.M.).
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Chen Z, Bi S, Shan Y, Cui B, Yang H, Qi Z, Zhao Z, Han Y, Yan S, Lu J. Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using 18F-FDG PET/MRI Radiomics. CNS Neurosci Ther 2024; 30:e14539. [PMID: 38031997 PMCID: PMC11017421 DOI: 10.1111/cns.14539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/18/2023] [Accepted: 11/10/2023] [Indexed: 12/01/2023] Open
Abstract
PURPOSE This study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer's disease (AD). METHODS A total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F-fluorodeoxyglucose (18F-FDG) PET, 3D arterial spin labeling (ASL), and high-resolution T1-weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad-Score) of logistic regression models were evaluated from 5-fold cross-validation. RESULTS The hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single-modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad-Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests. CONCLUSION Multiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.
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Affiliation(s)
- Zhigeng Chen
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Sheng Bi
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Yi Shan
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Bixiao Cui
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Hongwei Yang
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Zhigang Qi
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Zhilian Zhao
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Ying Han
- Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Shaozhen Yan
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
- Key Laboratory of Neurodegenerative DiseasesMinistry of EducationBeijingChina
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De A, Mishra TK, Saraf S, Tripathy B, Reddy SS. A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction. Curr Alzheimer Res 2024; 20:845-861. [PMID: 38468529 DOI: 10.2174/0115672050301514240307071217] [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: 01/13/2024] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.
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Affiliation(s)
- Arka De
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Tusar Kanti Mishra
- Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Sameeksha Saraf
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Balakrushna Tripathy
- School of Information Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Shiva Shankar Reddy
- Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India
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Shan Y, Yan SZ, Wang Z, Cui BX, Yang HW, Yuan JM, Yin YY, Shi F, Lu J. Impact of brain segmentation methods on regional metabolism quantification in 18F-FDG PET/MR analysis. EJNMMI Res 2023; 13:79. [PMID: 37668814 PMCID: PMC10480127 DOI: 10.1186/s13550-023-01028-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Accurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and MRI through an integrated PET/MR system presents an opportunity to leverage the benefits of precisely aligned structural MRI and molecular PET images in both spatial and temporal dimensions. However, in many clinical workflows, PET studies are often performed without the aid of individually matched structural MRI scans, primarily for the sake of convenience in the data collection and brain segmentation possesses. Currently, two commonly employed segmentation strategies for brain PET analysis are distinguished: methods with or without MRI registration and methods employing either atlas-based or individual-based algorithms. Moreover, the development of artificial intelligence (AI)-assisted methods for predicting brain segmentation holds promise but requires further validation of their efficiency and accuracy for clinical applications. This study aims to compare and evaluate the correlations, consistencies, and differences among the above-mentioned brain segmentation strategies in quantification of brain metabolism in 18F-FDG PET/MR analysis. RESULTS Strong correlations were observed among all methods (r = 0.932 to 0.999, P < 0.001). The variances attributable to subject and brain region were higher than those caused by segmentation methods (P < 0.001). However, intraclass correlation coefficient (ICC)s between methods with or without MRI registration ranged from 0.924 to 0.975, while ICCs between methods with atlas- or individual-based algorithms ranged from 0.741 to 0.879. Brain regions exhibiting significant standardized uptake values (SUV) differences due to segmentation methods were the basal ganglia nuclei (maximum to 11.50 ± 4.67%), and various cerebral cortexes in temporal and occipital regions (maximum to 18.03 ± 5.52%). The AI-based method demonstrated high correlation (r = 0.998 and 0.999, P < 0.001) and ICC (0.998 and 0.997) with FreeSurfer, substantially reducing the time from 8.13 h to 57 s on per subject. CONCLUSIONS Different segmentation methods may have impact on the calculation of brain metabolism in basal ganglia nuclei and specific cerebral cortexes. The AI-based approach offers improved efficiency and is recommended for its enhanced performance.
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Affiliation(s)
- Yi Shan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Shao-Zhen Yan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China
| | - Bi-Xiao Cui
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Hong-Wei Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Jian-Min Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China
| | - Ya-Yan Yin
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200030, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, #45 Changchunjie, Xicheng District, Beijing, 100053, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China.
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Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R. An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease. Front Aging Neurosci 2023; 15:1238065. [PMID: 37719873 PMCID: PMC10501457 DOI: 10.3389/fnagi.2023.1238065] [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: 06/10/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023] Open
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Silvano Quarto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
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Shang Q, Zhang Q, Liu X, Zhu L. Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3144035. [PMID: 35572832 PMCID: PMC9106502 DOI: 10.1155/2022/3144035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/06/2022] [Accepted: 04/15/2022] [Indexed: 11/17/2022]
Abstract
This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer's disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer's disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group (P < 0.01). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group (P < 0.001). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score (R 2 = 0.1702, 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; P < 0.001). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model (P < 0.01). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model (P < 0.01). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.
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Affiliation(s)
- Qun Shang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Qi Zhang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Xiao Liu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Lingchen Zhu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
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