Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease.

Alzheimer Disease / diagnostic imaging Biomarkers Cognitive Dysfunction / diagnostic imaging Disease Progression Fluorodeoxyglucose F18 Humans Positron-Emission Tomography / methods
Alzheimer’s disease Cox model Mild cognitive impairment Radiomics Radiomics-based predictive model

Journal

European journal of nuclear medicine and molecular imaging
ISSN: 1619-7089
Titre abrégé: Eur J Nucl Med Mol Imaging
Pays: Germany
ID NLM: 101140988

Informations de publication

Date de publication:
06 2022
Historique:
received: 02 08 2021
accepted: 11 01 2022
pubmed: 16 1 2022
medline: 26 5 2022
entrez: 15 1 2022
Statut: ppublish

Résumé

Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.

Sections du résumé

BACKGROUND
Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data.
METHOD
FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments.
RESULTS
The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity.
CONCLUSION
The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.

Identifiants

DOI: 10.1007/s00259-022-05687-y PMID: 35032179
pubmed: 35032179
doi: 10.1007/s00259-022-05687-y
pii: 10.1007/s00259-022-05687-y
doi:

Substances chimiques

Biomarkers 0
Fluorodeoxyglucose F18 0Z5B2CJX4D

Types de publication

Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

2163-2173

Informations de copyright

© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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Auteurs

Jiehui Jiang (J)

Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China. jiangjiehui@shu.edu.cn.
ORCID: 0000-0003-4948-3683

Min Wang (M)

Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China.

Ian Alberts (I)

Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.

Xiaoming Sun (X)

Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China.

Taoran Li (T)

Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.

Axel Rominger (A)

Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.

Chuantao Zuo (C)

PET Center, Huashan Hospital, Fudan University, Shanghai, China. zuochuantao@fudan.edu.cn.
Human Phenome Institute, Fudan University, Shanghai, China. zuochuantao@fudan.edu.cn.

Ying Han (Y)

Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China. hanying@xwh.ccmu.edu.cn.
Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China. hanying@xwh.ccmu.edu.cn.
School of Biomedical Engineering, Hainan University, Haikou, China. hanying@xwh.ccmu.edu.cn.
National Clinical Research Center for Geriatric Disorders, Beijing, China. hanying@xwh.ccmu.edu.cn.

Kuangyu Shi (K)

Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.
Department of Informatics, Technische Universität München, Munich, Germany.

For The Alzheimer's Disease Neuroimaging Initiative (FTADN)

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questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Démence › Maladie d'Alzheimer › Using radiomics-based modelling to predict...
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questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Troubles de la cognition › Dysfonctionnement cognitif › Using radiomics-based modelling to predict...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Caractéristiques de la maladie › Évolution de la maladie › Using radiomics-based modelling to predict...
questionsmedicales.fr › Glucides › Désoses › Désoxyglucose › Fluorodésoxyglucose F18 › Using radiomics-based modelling to predict...
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questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Techniques de diagnostic radioisotopique › Scintigraphie › Tomoscintigraphie › Tomographie par émission de positons › Using radiomics-based modelling to predict...