Constructing a predictive model for children with autism spectrum disorder based on whole brain magnetic resonance radiomics: a machine learning study.

Xi Chen, Jiaxuan Peng, Zihan Zhang, Qiaowei Song, Dongxue Li, Gongyong Zhai, Wanyun Fu, Zhenyu Shu
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Abstract

Background and purpose: Autism spectrum disorder (ASD) diagnosis remains challenging and could benefit from objective imaging-based approaches. This study aimed to construct a prediction model using whole-brain imaging radiomics and machine learning to identify children with ASD.

Materials and methods: We analyzed 223 subjects (120 with ASD) from the ABIDE database, randomly divided into training and test sets (7:3 ratio), and an independent external test set of 87 participants from Georgetown University and University of Miami. Radiomics features were extracted from white matter, gray matter, and cerebrospinal fluid from whole-brain MR images. After feature dimensionality reduction, we screened clinical predictors using multivariate logistic regression and combined them with radiomics signatures to build machine learning models. Model performance was evaluated using ROC curves and by stratifying subjects into risk subgroups.

Results: Radiomics markers achieved AUCs of 0.78, 0.75, and 0.74 in training, test, and external test sets, respectively. Verbal intelligence quotient(VIQ) emerged as a significant ASD predictor. The decision tree algorithm with radiomics markers performed best, with AUCs of 0.87, 0.84, and 0.83; sensitivities of 0.89, 0.84, and 0.86; and specificities of 0.70, 0.63, and 0.66 in the three datasets, respectively. Risk stratification using a cut-off value of 0.4285 showed significant differences in ASD prevalence between subgroups across all datasets (training: χ2=21.325; test: χ2=5.379; external test: χ2=21.52m, P<0.05).

Conclusions: A radiomics signature based on whole-brain MRI features can effectively identify ASD, with performance enhanced by incorporating VIQ data and using a decision tree algorithm, providing a potential adaptive strategy for clinical practice.

Abbreviations: ASD = Autism Spectrum Disorder; MRI = Magnetic Resonance Imaging; SVM = support vector machine; KNN = K-nearest neighbor; VIQ = Verbal intelligence quotient; FIQ = Full-Scale intelligence quotient; ROC = Receiver Operating Characteristic; AUC = Area under Curve.

基于全脑磁共振放射组学的儿童自闭症谱系障碍预测模型构建:机器学习研究。
背景与目的:自闭症谱系障碍(ASD)的诊断仍然具有挑战性,可以从基于客观成像的方法中获益。本研究旨在利用全脑成像放射组学和机器学习技术构建ASD儿童的预测模型。材料和方法:我们分析了来自ABIDE数据库的223名受试者(其中120名患有ASD),随机分为训练组和测试组(比例为7:3),以及来自乔治敦大学和迈阿密大学的87名独立的外部测试组。从全脑MR图像中提取白质、灰质和脑脊液的放射组学特征。在特征降维后,我们使用多元逻辑回归筛选临床预测因子,并将其与放射组学特征相结合,构建机器学习模型。采用ROC曲线和将受试者分为风险亚组来评估模型的性能。结果:放射组学标记在训练集、测试集和外部测试集的auc分别为0.78、0.75和0.74。语言智商(VIQ)是一个重要的ASD预测指标。采用放射组学标记的决策树算法效果最好,auc分别为0.87、0.84和0.83;灵敏度分别为0.89、0.84和0.86;特异性分别为0.70、0.63和0.66。采用截断值0.4285的风险分层显示,所有数据集的亚组间ASD患病率存在显著差异(χ2=21.325;测试:χ2 = 5.379;结论:基于全脑MRI特征的放射组学特征可有效识别ASD,结合VIQ数据和决策树算法可提高其识别性能,为临床实践提供了一种潜在的自适应策略。缩写:ASD =自闭症谱系障碍;磁共振成像;支持向量机;KNN = k近邻;语言智商;FIQ =全面智商;ROC =受试者工作特征;AUC =曲线下面积。
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