Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy

作者：Long‐Biao Cui, Lin Liu, Huaning Wang, Liuxian Wang, Fan Guo, Yibin Xi, Tingting Liu, Chen Li, Ping Tian, Kang Liu, Wen-Jun Wu, Yihuan Chen, Wei Qin, Hong Yin · 发表于：Schizophrenia Bulletin · 年份：2018 · DOI：10.1093/schbul/sby007 · 被引用次数：76 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Medical Imaging Techniques and Applications、Advanced Neuroimaging Techniques and Applications

Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed to develop and validate a method of disease definition for SZ by resting-state functional connectivity using radiomics strategy. This study included 2 data sets collected with different scanners. A total of 108 first-episode SZ patients and 121 healthy controls (HCs) participated in the current study, among which 80% patients and HCs (n = 183) and 20% (n = 46) were selected for training and testing in intra-data set validation and 1 of the 2 data sets was selected for training and the other for testing in inter-data set validation, respectively. Functional connectivity was calculated for both groups, features were selected by Least Absolute Shrinkage and Selection Operator (LASSO) method, and the clinical utility of its features and the generalizability of effects across samples were assessed using machine learning by training and validating multivariate classifiers in the independent samples. We found that the accuracy of intra-data set training was 87.09% for diagnosing SZ patients by applying functional connectivity features, with a validation in the independent replication data set (accuracy = 82.61%). The inter-data set validation further confirmed the disease definition by functional connectivity features (accuracy = 83.15% f...