ArticleFrontiers in neuroscience2021
Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- Toward precision medicine: can neuroimaging prospectively predict early treatment outcomes in schizophrenia spectrum disorders? A systematic review and meta-analysis.European archives of psychiatry and clinical neuroscience · 2026Review
- Radiomics-based feature exploration and auxiliary diagnostic model construction for gaming disorder.Scientific reports · 2026Article
- Brain Volume Alterations and Cognitive Functions in Patients with Common Variable Immunodeficiency: Evaluating the Impact of Autoimmunity.Journal of clinical medicine · 2026Article
- Explainable artificial intelligence (XAI) in schizophrenia: enhancing transparency in clinical decision-making.Frontiers in systems biology · 2026Review
- Radiomic Analysis of Striatal [Molecular imaging and biology · 2025Article
- Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review.Brain sciences · 2024Article
- Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients.Frontiers in psychiatry · 2024Article
- Machine learning methods to predict outcomes of pharmacological treatment in psychosis.Translational psychiatry · 2023Review
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 2023Article
- Incidence and factors associated of early non-response in first-treatment and drug-naïve patients with schizophrenia: a real-world study.Frontiers in psychiatry · 2023Article
- Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.Translational psychiatry · 2022Article
- Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.Frontiers in aging neuroscience · 2022Article
- A Comprehensive Evaluation of Radiomic Features in Normal Brain Magnetic Resonance Imaging: Investigating Robustness and Region Variations.Journal of medical physicsArticle
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEmerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at developing and validating a method of disease identification and prediction of treatment response by multi-dimensional thalamic features derived from magnetic resonance imaging in schizophrenia patients using radiomics approaches.
methodsA total of 390 subjects, including patients with schizophrenia and healthy controls, participated in this study, among which 109 out of 191 patients had clinical characteristics of early outcome (61 responders and 48 non-responders). Thalamus-based radiomics features were extracted and selected. The diagnostic and predictive capacity of multi-dimensional thalamic features was evaluated using radiomics approach.
resultsUsing radiomics features, the classifier accurately discriminated patients from healthy controls, with an accuracy of 68%. The features were further confirmed in prediction and random forest of treatment response, with an accuracy of 75%.
conclusionOur study demonstrates a radiomics approach by multiple thalamic features to identify schizophrenia and predict early treatment response. Thalamus-based classification could be promising to apply in schizophrenia definition and treatment selection.
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