Table 1.
Study characteristics.
| First author | Year | Title | Group | Number of cases | Tool | Task | Source |
|---|---|---|---|---|---|---|---|
| Alongi et al. | 2022 | Radiomics Analysis of Brain [ (18)F]FDG PET/CT to Predict Alzheimer's Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis. | AD-related | 43 | [18F]FDG PET | Prediction | local |
| Ciarmiello et al. | 2022 | Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET. | AD-related | 328 | [18]F Florbetaben (FBB) PET | Classification | ADNIa |
| Jiang et al. | 2022 | Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease. | AD-related | 884 | [18F]FDG PET | Classification and Prediction | ADNI |
| Sheng et al | 2022 | Cross-Cultural Longitudinal Study on Cognitive Decline (CLoCODE) for Subjective Cognitive Decline in China and Germany: A Protocol for Study Design. | AD-related | 479 | [18]F-AV-45 (florbetapir) PET | Classification and Correlation | ADNI |
| Yang et al. | 2022 | Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer's disease: an exploratory radiomic analysis study. | AD-related | 471 | [18F]FDG PET | Prediction and Correlation | local, ADNI |
| Ding et al. | 2021 | Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study. | AD-related | 1078 | [18]F-AV-45 (florbetapir) PET | Classification | local, ADNI |
| Huang et al | 2021 | Radiogenomics of Alzheimer's disease: exploring gene related metabolic imaging markers. | AD-related | 389 | [18F]FDG PET | Prediction | multicenter |
| Li et al. | 2019 | Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. | AD-related | 466 | [18F]FDG PET | Prediction | local, ADNI |
| Zhou et al. | 2019 | Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease. | AD-related | 263 | [18F]FDG PET | Prediction | ADNI |
| Comte et al. | 2022 | Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT. | PD-related | 443 | [18F]FDOPA PET | Classification | local |
| Salmanpour et al. | 2022 | Longitudinal clustering analysis and prediction of Parkinson's disease progression using radiomics and hybrid machine learning. | PD-related | 143 | DAT-SPECT (123I-Ioflupane) | Classification | local |
| Shiiba et al. | 2022 | Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson's disease. | PD-related | 413 | DAT-SPECT (123I-Ioflupane) | Prediction | PPMIb |
| Hu et al. | 2021 | Multivariate radiomics models based on (18)F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy. | PD-related | 90 | [18F]FDG PET/CT | Prediction and Correlation | local |
| Salmanpour et al. | 2021 | Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning. | PD-related | 464 | DAT-SPECT (123I-Ioflupane) | Clustering | PPMI |
| Tang et al. | 2019 | Artificial Neural Network-Based Prediction of Outcome in Parkinson's Disease Patients Using DaTscan SPECT Imaging Features. | PD-related | 69 | DAT-SPECT (123I-Ioflupane) | Clustering and Prediction | PPMI |
| Wu et al. | 2019 | Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls. | PD-related | 230 | [18F]FDG PET/CT | Classification | local |
| Rahmim et al. | 2017 | Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images. | PD-related | 64 | DAT-SPECT (123I-Ioflupane) | Prediction | PPMI |
| Rahmim et al. | 2016 | Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments | PD-related | 141 | DAT-SPECT (123I-Ioflupane) | Classification | multicenter |
a
The Alzheimer's Disease Neuroimaging Initiative (ADNI) database, www.adni.loni.usc.edu.
b
The Parkinson's Progression Markers Initiative (PPMI) database, www.ppmi-info.org.