ArticleFrontiers in psychiatry2020
Neuroanatomical Features That Predict Response to Electroconvulsive Therapy Combined With Antipsychotics in Schizophrenia: A Magnetic Resonance Imaging Study Using Radiomics Strategy.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Structural and Functional MRI Brain Changes in Patients with Schizophrenia Following Electroconvulsive Therapy: A Systematic Review.Current neuropharmacology · 2022Pooled it
- The Impact of Clinical Characteristics of Patients with Mental Disorders on the Long-Term Effects of Electroconvulsive Therapy.Journal of clinical medicine · 2026Article
- Effects of electroconvulsive therapy on functional brain networks in patients with schizophrenia.BMC psychiatry · 2024Article
- Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients.Frontiers in psychiatry · 2024Article
- Radiomics; A Potential Next "Omics" in Psychiatric Disorders; An Introduction.Psychiatry investigation · 2023Article
- Robustness of radiomics to variations in segmentation methods in multimodal brain MRI.Scientific reports · 2022Article
- Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia.Frontiers in neuroscience · 2021Article
- Building the Precision Medicine for Mental Disorders via Radiomics/Machine Learning and Neuroimaging.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveNeuroimaging-based brain signatures may be informative in identifying patients with psychosis who will respond to antipsychotics. However, signatures that inform the electroconvulsive therapy (ECT) health care professional about the response likelihood remain unclear in psychosis with radiomics strategy. This study investigated whether brain structure-based signature in the prediction of ECT response in a sample of schizophrenia patients using radiomics approach.
methodsThis high-resolution structural magnetic resonance imaging study included 57 patients at baseline. After ECT combined with antipsychotics, 28 and 29 patients were classified as responders and non-responders. Features of gray matter were extracted and compared. The logistic regression model/support vector machine (LRM/SVM) analysis was used to explore the predictive performance.
resultsThe regularized multivariate LRM accurately discriminated responders from non-responders, with an accuracy of 90.91%. The structural features were further confirmed in the validating data set, resulting in an accuracy of 87.59%. The accuracy of the SVM in the training set was 90.91%, and the accuracy in the validation set was 91.78%.
conclusionOur results support the possible use of structural brain feature-based radiomics as a potential tool for predicting ECT response in patients with schizophrenia undergoing antipsychotics, paving the way for utilization of markers in psychosis.
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