Citation

Gong J, Cui LB, Xi YB, Zhao YS, Yang XJ, Xu ZL, Sun JB, Liu P, Jia J, Li P, Yin H, Qin W. Schizophr. Res. 2020; 216: 262-271.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.schres.2019.11.046

PMID

31826827

Abstract

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.

Language: en

Keywords

Humans; Magnetic Resonance Imaging; Schizophrenia; Neuroimaging; Electroconvulsive therapy; Prediction; Electroconvulsive Therapy; Antipsychotic Agents; Structural magnetic resonance imaging; Diffusion magnetic resonance imaging; Radiomics