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In Frontiers in neuroscience ; h5-index 72.0

Background : Emerging 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.

Methods : A 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.

Results : Using 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%.

Conclusion : Our 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.

Cui Long-Biao, Zhang Ya-Juan, Lu Hong-Liang, Liu Lin, Zhang Hai-Jun, Fu Yu-Fei, Wu Xu-Sha, Xu Yong-Qiang, Li Xiao-Sa, Qiao Yu-Ting, Qin Wei, Yin Hong, Cao Feng

2021

diagnosis, machine learning, radiomics, schizophrenia, thalamus, treatment