Machine Learning Models Based on Hippocampal T2-Weighted-Fluid-Attenuated Inversion Recovery Radiomics for Diagnosis of Posttraumatic Stress Disorder

Abstract

Abstract Background Radiomics is characterized by high-throughput extraction of texture features from medical images for deep mining and analysis to establish meaningful associations between image texture data and specific diseases. Radiomics has demonstrated significant advantages and potential in the diagnosis and evaluation of numerous neurological and psychiatric diseases. However, few studies on its use in the diagnosis of posttraumatic stress disorder (PTSD) have been reported. This study investigated the feasibility of machine learning models based on hippocampal T2-weighted-fluid-attenuated inversion recovery (T2-FLAIR) radiomics for the diagnosis of PTSD. Methods We performed a retrospective analysis of the demographic, clinical, and magnetic resonance imaging data of 94 patients with a history of road traffic accident. Regions of interest were manually selected at the bilateral hippocampus on the slices showing the largest respective sizes of the hippocampus. Additionally, the 524 texture features on T2-FLAIR images were extracted. Least absolute shrinkage and selection operator regression was used to screen for the optimal texture features. Thereafter, logistic regression (LR), support vector machine (SVM), and random forest (RF) machine learning models were constructed using the R language for PTSD diagnosis. Receiver operating characteristic curves were used to evaluate the diagnostic performance of each machine learning model. Results No statistically significant differences in demographic and clinical characteristics were observed between PTSD and non-PTSD cases after road traffic accident (P > 0.05). However, statistically significant differences in the simplified coping style questionnaire positive/-negative coping scores and PTSD Checklist-Civilian Version scores existed between PTSD and non-PTSD cases at 3 months after road traffic accident (P < 0.01). The performance of three machine learning models in distinguishing PTSD cases from non-PTSD cases was good. In the training and test groups, the area under curves (AUCs) of the LR were 0.829 (95% confidence interval [CI]: 0.717–0.911) and 0.779 (95% CI: 0.584–0.913), with sensitivities and specificities of 74.19% and 77.13%, 76.92% and 80.00%, respectively. The AUCs of the SVM were 0.899 (95% CI: 0.801–0.960) and 0.810 (95% CI: 0.618–0.933), with sensitivities and specificities of 96.77% and 74.29%, 61.54% and 86.67%, respectively. The AUCs of the RF were 0.865 (95% CI: 0.758–0.936) and 0.728 (95% CI: 0.537–0.878), with sensitivities and specificities of 87.10% and 77.14%, 92.31% and 53.33%, respectively. Conclusions Machine learning models based on hippocampal T2-FLAIR radiomics have good diagnostic performance for PTSD and can be used as novel neuroimaging biomarkers for the clinical diagnosis of PTSD.