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Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy
European Journal of Neuroscience ( IF 3.698 ) Pub Date : 2020-11-18 , DOI: 10.1111/ejn.15046
Long‐Biao Cui 1, 2, 3 , Yu‐Fei Fu 4 , Lin Liu 5, 6 , Xu‐Sha Wu 4 , Yi‐Bin Xi 4 , Hua‐Ning Wang 2 , Wei Qin 5 , Hong Yin 4
Affiliation  

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.

中文翻译:

基线结构和功能磁共振成像可通过放射性组策略预测精神分裂症的早期治疗反应

多模态神经影像学特征为精神病学领域的准确分类和个性化治疗选择提供了机会。这项研究旨在调查在首次住院或单次住院结束时,大脑特征是否可预测对精神分裂症整体治疗的反应。来自两个独立样本的结构和功能磁共振成像(MRI)数据(N 包括基线时的精神分裂症患者分别为85和63)。治疗后,患者分为有反应者和无反应者。使用最小绝对收缩和选择算子提取了灰质形态和功能连通性的放射学特征。使用支持向量机探索预测性能。预测模型基于结构特征(皮质厚度,表面积,灰质区域体积,平均曲率,度量失真和沟深),功能特征(功能连通性)和组合特征。降维后有12个特征。结构特征涉及右前突,楔骨和顶下小叶。功能功能主要包括半球之间的连通性。对于使用功能特征的模型,我们观察到的预测准确性为80.38%(敏感性:87.28%;特异性为82.47%),对于使用结构特征的模型,预测准确性为69.68%(敏感性:83.96%;特异性:72.41%)。我们的模型结合了结构和功能特征,可达到85.03%的更高准确度,对正确预测的整体治疗有92.04%的响应者和80.23%的无响应者。这些结果凸显了结构和功能性MRI放射学特征预测精神分裂症早期对治疗的反应的能力。精神分裂症的早期治疗反应的预测模型可以增强有效的治疗策略。41%)用于使用结构特征的网站。我们的模型结合了结构和功能特征,可达到85.03%的更高准确度,对正确预测的整体治疗有92.04%的响应者和80.23%的无响应者。这些结果凸显了结构和功能性MRI放射学特征预测精神分裂症早期对治疗的反应的能力。精神分裂症的早期治疗反应的预测模型可以增强有效的治疗策略。41%)用于使用结构特征的网站。我们的模型结合了结构和功能特征,可达到85.03%的更高准确度,对正确预测的整体治疗有92.04%的响应者和80.23%的无响应者。这些结果凸显了结构和功能性MRI放射学特征预测精神分裂症早期对治疗的反应的能力。精神分裂症的早期治疗反应的预测模型可以增强有效的治疗策略。这些结果凸显了结构和功能性MRI放射学特征预测精神分裂症早期对治疗的反应的能力。精神分裂症的早期治疗反应的预测模型可以增强有效的治疗策略。这些结果凸显了结构和功能性MRI放射学特征预测精神分裂症早期对治疗的反应的能力。精神分裂症的早期治疗反应的预测模型可以增强有效的治疗策略。
更新日期:2020-11-18
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