ArticleEuropean journal of nuclear medicine and molecular imaging2022
Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 43 citations in OpenAlex.
- [Progress on prognostic assessment methods for mild cognitive impairment].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026Review
- Integration of glymphatic system function and hippocampal radiomics for diagnosis and conversion prediction of Alzheimer's disease.BMC medical imaging · 2026Article
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Early-stage diagnosis of HIV-associated neurocognitive disorders via multiple learning models based on resting-state functional magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2025Article
- Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.Alzheimer's research & therapy · 2025Article
- Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network.Diagnostics (Basel, Switzerland) · 2025Article
- The application of radiomics in the diagnosis and evaluation of cognitive impairment related to neurological diseases.Frontiers in neuroscience · 2025Review
- Comparison of Radiomics and conventional SUVr methods for Alzheimer's disease classification using AV45 PET imaging.Frontiers in neurology · 2025Article
- Correlation Between Cognitive Impairment and Lenticulostriate Arteries: A Clinical and Radiomics Analysis.Journal of imaging informatics in medicine · 2024Article
- Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.Brain sciences · 2024Article
- Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- Association study of brain structure-function coupling and glymphatic system function in patients with mild cognitive impairment due to Alzheimer's disease.Frontiers in neuroscience · 2024Article
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.Academic radiology · 2023Review
- Radiomics insight into the neurodegenerative "Frontiers in nuclear medicine · 2023Review
- Unlocking the enigma: unraveling multiple cognitive dysfunction linked to glymphatic impairment in early Alzheimer's disease.Frontiers in neuroscience · 2023Article
- Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening.Brain sciences · 2022Article
- Radiomics Analysis of Brain [Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting 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.
methodFDG 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.
resultsThe 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.
conclusionThe preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.
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