ArticleTranslational psychiatry2021
An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Multiclassifier Radiomics Analysis of Ultrasound for Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma in Children.International journal of medical sciences · 2023Trial
- Hippocampal subfield radiomics improves identification of in-hospital aggressive behavior in schizophrenia: an interpretable machine-learning study.BMC psychiatry · 2026Article
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- Unveiling state-specific neural dynamics in migraine with and without depressive symptom: a hidden Markov model and interpretable machine learning approach.The journal of headache and pain · 2026Article
- Review
- Noninvasive evaluation of treatment-related protein expression status in advanced gastric cancer using dynamic nomograms with dual-energy CT: a multicenter study.European radiology · 2026Article
- Multimodal neuroimaging alterations in schizophrenia model rats and the modulatory effects of risperidone: a pilot study.Frontiers in psychiatry · 2026Article
- Sureness of classification of breast cancers as pure ductal carcinomaJournal of medical imaging (Bellingham, Wash.) · 2025Article
- Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models.Scientific reports · 2024Article
- Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.Scientific reports · 2024Article
- Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter.Frontiers in aging neuroscience · 2024Article
- A Framework for Interpretability in Machine Learning for Medical Imaging.IEEE access : practical innovations, open solutions · 2024Article
- Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.Scientific reports · 2023Article
- Article
- Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype.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
- An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas.Radiation oncology (London, England) · 2022Article
- CaCell death & disease · 2022Article
- Role of Radiomics-Based Baseline PET/CT Imaging in Lymphoma: Diagnosis, Prognosis, and Response Assessment.Molecular imaging and biology · 2022Review
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Radiomic features of the CC subregions were extracted from T1-weighted, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) images (N = 1605). Following feature selection, various combinations of classifiers were trained, and Bayesian optimization was adopted in the best performing classifier. Discrimination, calibration, and clinical utility of the model were assessed. An online calculator was constructed to offer the probability of having schizophrenia. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. We identified 30 radiomic features to differentiate participants with schizophrenia from HCs. The Bayesian optimized model achieved the highest performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.81-0.98), 80.0, 83.3, and 76.9%, respectively, in the test set. The final model offers clinical probability in an online calculator. The model explanation by SHAP suggested that second-order features from the posterior CC were highly associated with the risk of schizophrenia. The multiparametric radiomics model focusing on the CC shows its robustness for the diagnosis of schizophrenia. Radiomic features could be a potential source of biomarkers that support the biomarker-based diagnosis of schizophrenia and improve the understanding of its neurobiology.
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