Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy

• Published in: The European journal of neuroscience Vol. 53; no. 6; pp. 1961 - 1975
• Main Authors: Cui, Long‐Biao, Fu, Yu‐Fei, Liu, Lin, Wu, Xu‐Sha, Xi, Yi‐Bin, Wang, Hua‐Ning, Qin, Wei, Yin, Hong
• Format: Journal Article
• Language: English
• Published: France Wiley Subscription Services, Inc 01.03.2021
• Subjects: Brain mapping; Cortex (parietal); Early intervention; early treatment response; Functional magnetic resonance imaging; Magnetic resonance imaging; Mental disorders; Neural networks; Neuroimaging; prediction; Prediction models; Radiomics; Schizophrenia; Structure-function relationships; Substantia grisea; prediction; early treatment response; radiomics; magnetic resonance imaging; schizophrenia
• ISSN: 0953-816X; 1460-9568; 1460-9568
• DOI: 10.1111/ejn.15046

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non‐responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter‐hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non‐responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI‐derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies. By radiomics strategy, we established prediction models based on structural features and functional features extracted from multi‐modal magnetic resonance imaging (MRI) data of 148 schizophrenia patients collected from two independent samples. The model combining both structural and functional features achieved a prediction accuracy of 85.03% to the overall treatment response. Our results highlight the capability of structural and functional MRI‐derived radiomics features to predict early responses to the treatment in schizophrenia.