ReviewMedComm2024
Machine learning-based radiomics in neurodegenerative and cerebrovascular disease.
Review in MedComm, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Neuroimaging and machine learning in eating disorders: a systematic review.Eating and weight disorders : EWD · 2025Pooled it
- Radiomics and ischemic stroke research: bibliometric insights and visual trends (2004-2024).Frontiers in neurology · 2025Pooled it
- Retinal and Choroidal Vascular Metrics Predict Cerebral Atrophy and Cognitive Impairment in Noninfarcted Large Artery Stenosis.Investigative ophthalmology & visual science · 2026Article
- Multiscale characterization and classification of Alzheimer's disease via integration of brain fingerprint radiomics and graph‑theoretical network metrics.Neuroradiology · 2026Article
- Exploratory Associations Between Multimodal MRI-Derived Features and Neurological Symptoms in Wolfram Syndrome: A Spanish Cohort Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Impact of Different Energy Levels of Virtual Monoenergetic Reconstructions on Radiomic Features Stability in Organic Phantom Imaging Using Photon-Counting CT.Tomography (Ann Arbor, Mich.) · 2026Article
- Radiomics-enhancedEuropean radiology experimental · 2026Article
- Molecular and multimodal biomarkers in Moyamoya disease: from pathogenic mechanisms to clinical translation.European journal of medical research · 2026Review
- Construction of an interpretable prediction model for poor functional outcome in conservatively managed basal ganglia hemorrhage based on CT radiomics and multiple machine learning algorithms.Frontiers in neurology · 2026Article
- Interpretable machine learning model based on multimodal MRI radiomics for Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2026Article
- Machine learning-derived biomarker cutoffs for Alzheimer's disease: Validation and application in preclinical and prodromal phases.iScience · 2025Article
- Predicting mild cognitive impairment in patients with Parkinson's disease by integrating striatal MRI radiomics with clinical features.BMC medical imaging · 2025Article
- Novel multi-task learning for Alzheimer's stage classification using hippocampal MRI segmentation, feature fusion, and nomogram modeling.European journal of medical research · 2025Article
- Developments in MRI radiomics research for vascular cognitive impairment.Insights into imaging · 2025Review
- Development of a Diagnostic Prediction Model for Post-Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol.Brain and behavior · 2025Article
- Comparison of Radiomics and conventional SUVr methods for Alzheimer's disease classification using AV45 PET imaging.Frontiers in neurology · 2025Article
- Multimodal radiomics of cerebellar subregions for machine learning-driven Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2025Article
- Advancements in multi-omics research to address challenges in Alzheimer's disease: a systems biology approach utilizing molecular biomarkers and innovative strategies.Frontiers in aging neuroscience · 2025Review
- Research progress of artificial intelligence in moyamoya disease.Frontiers in neurology · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cognitive impairments, which can be caused by neurodegenerative and cerebrovascular disease, represent a growing global health crisis with far-reaching implications for individuals, families, healthcare systems, and economies worldwide. Notably, neurodegenerative-induced cognitive impairment often presents a different pattern and severity compared to cerebrovascular-induced cognitive impairment. With the development of computational technology, machine learning techniques have developed rapidly, which offers a powerful tool in radiomic analysis, allowing a more comprehensive model that can handle high-dimensional, multivariate data compared to the traditional approach. Such models allow the prediction of the disease development, as well as accurately classify disease from overlapping symptoms, therefore facilitating clinical decision making. This review will focus on the application of machine learning-based radiomics on cognitive impairment caused by neurogenerative and cerebrovascular disease. Within the neurodegenerative category, this review primarily focuses on Alzheimer's disease, while also covering other conditions such as Parkinson's disease, Lewy body dementia, and Huntington's disease. In the cerebrovascular category, we concentrate on poststroke cognitive impairment, including ischemic and hemorrhagic stroke, with additional attention given to small vessel disease and moyamoya disease. We also review the specific challenges and limitations when applying machine learning radiomics, and provide our suggestion to overcome those limitations towards the end, and discuss what could be done for future clinical use.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.