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

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

doi:10.3389/fnins.2021.682777

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.

Highlights

Driven by the need for precision medicine, a quest for accurate diagnosis and treatment was recently noted in the management of schizophrenia
As the neuroanatomical and neurochemical theories implicated in the pathophysiology of schizophrenia, the notion of emphasizing psychopathological processes mediated by the thalamus (Parnaudeau et al, 2018) should be paralleled by identifying patients and predicting treatment response via multi-dimensional thalamic features
We performed comparison between subjects on two scanners, e.g., patients/healthy controls on Siemens scanner and General Electric (GE) scanner, and no significant difference was found between two scanners by t-tests

Summary

Introduction

Driven by the need for precision medicine, a quest for accurate diagnosis and treatment was recently noted in the management of schizophrenia. Disrupted coactivation within resting-state networks analysis has been observed in the thalamus (Cui et al, 2017a). Both functional and structural imaging findings support dysconnectivity of the thalamus and cerebellum (Liu et al, 2011). 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

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Conclusion