Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia

Frontiers in Neuroscience

Hai-Jun Zhang¹  Long-Biao Cui²  Yu-Ting Qiao³  Xiao-Sa Li³  Yong-Qiang Xu⁴  Hong Yin⁴  Yu-Fei Fu⁴  Xu-Sha Wu⁴  Ya-Juan Zhang⁵  Hong-Liang Lu⁵  Lin Liu⁶  Wei Qin⁷  Feng Cao⁸ 

[1] Department of Clinical Aerospace Medicine, School of Aerospace Medicine, Fourth Military Medical University, Xi’an, China;Department of Clinical Psychology, Fourth Military Medical University, Xi’an, China;Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China;Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China;Military Medical Psychology School, Fourth Military Medical University, Xi’an, China;Peking University Sixth Hospital/Institute of Mental Health and Key Laboratory of Mental Health, Peking University, Beijing, China;School of Life Sciences and Technology, Xidian University, Xi’an, China;The Second Medical Center, Chinese PLA General Hospital, Beijing, China;

关键词: schizophrenia;    thalamus;    radiomics;    machine learning;    diagnosis;    treatment;   

DOI  :  10.3389/fnins.2021.682777

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown