ArticleDiagnostics (Basel, Switzerland)2022
Radiomics Analysis of Brain [
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Stage-stratified benefits of AI-radiomics PET in early Alzheimer's disease: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- A real-world decision tree model based on ¹⁸F-FDG PET/CT Z scores and Mini-Mental State Examination for differentiating mild cognitive impairment from clinically diagnosed Alzheimer disease.Annals of nuclear medicine · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Predictive value ofFrontiers in oncology · 2026Article
- Correlation and Interchangeability of Amyloid, Tau, and Glucose Metabolism PET in Mild Cognitive Impairment and Alzheimer: A Review.Brain sciences · 2025Review
- Preoperative prediction of pituitary neuroendocrine tumor consistency based on multiparametric MRI radiomics: a multicenter study.BMC cancer · 2025Article
- Automated segmentation of brain metastases in magnetic resonance imaging using deep learning in radiotherapy.Scientific reports · 2025Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Advanced finite segmentation model with hybrid classifier learning for high-precision brain tumor delineation in PET imaging.Scientific reports · 2025Article
- Looking into Abnormal Co-Expressions of Tau and TDP-43 in the Realm of Mixed Dementia Types: A Double-Punch Scenario.Brain sciences · 2025Review
- Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phaseScientific reports · 2025Article
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
- Comparison of Radiomics and conventional SUVr methods for Alzheimer's disease classification using AV45 PET imaging.Frontiers in neurology · 2025Article
- From pixels to prognosis: radiomics and AI in Alzheimer's disease management.Frontiers in neurology · 2025Review
- 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
- Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease usingCNS neuroscience & therapeutics · 2024Article
- Article
- Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?Diagnostics (Basel, Switzerland) · 2023Article
- Based on Tau PET Radiomics Analysis for the Classification of Alzheimer's Disease and Mild Cognitive Impairment.Brain sciences · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly in-vivo diagnosis of Alzheimer's disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [
methodsFrom July 2016 to September 2017, 43 patients underwent PET/CT scans with FDG and Florbetaben brain PET/CT and at least 24 months of clinical/instrumental follow-up. Patients were retrospectively evaluated by a multidisciplinary team (MDT = Neurologist, Psychologist, Radiologist, Nuclear Medicine Physician, Laboratory Clinic) at the G. Giglio Institute in Cefalù, Italy. Starting from the cerebral segmentations applied by SPM on the main cortical macro-areas of each patient, Pyradiomics was used for the feature extraction process; subsequently, an innovative descriptive-inferential mixed sequential approach and a machine learning algorithm (i.e., discriminant analysis) were used to obtain the best diagnostic performance in prediction of amyloid deposition and the final diagnosis of AD.
resultsA total of 11 radiomics features significantly predictive of cortical beta-amyloid deposition (
conclusionsThe results obtained in this preliminary study support advanced segmentation of cortical areas typically involved in early AD on FDG PET/CT brain images, and radiomics analysis for the identification of specific high-order features to predict Amyloid deposition and final diagnosis of AD.
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