Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China

• DOI: 10.3389/fimmu.2022.913703
• Publication Date: 2022-06-01T15:49:36Z
• Authors: Yayun Xiang; Xiaoxuan Dong; Chun Zeng; Junhang Liu; Hanjing Liu; Xiaofei Hu; Jinzhou Feng; Silin Du; Jingjie Wang; Yongliang Han; Qi Luo; Shanxiong Chen; Yongmei Li
• Keywords: Anti-N-Methyl-D-Aspartate Receptor Encephalitis; clinical features; anti-N-methyl-D-aspartate receptor; Immunology; deep learning; RC581-607; Prognosis; Magnetic Resonance Imaging; autoimmune encephalitis; 3. Good health; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; radiomics; Humans; prognosis; Immunologic diseases. Allergy; Retrospective Studies

ObjectiveTo develop a fusion model combining clinical variables, deep learning (DL), and radiomics features to predict the functional outcomes early in patients with adult anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis in Southwest China.MethodsFrom January 2012, a two-center study of anti-NMDAR encephalitis was initiated to collect clinical and MRI data from acute patients in Southwest China. Two experienced neurologists independently assessed the patients’ prognosis at 24 moths based on the modified Rankin Scale (mRS) (good outcome defined as mRS 0–2; bad outcome defined as mRS 3-6). Risk factors influencing the prognosis of patients with acute anti-NMDAR encephalitis were investigated using clinical data. Five DL and radiomics models trained with four single or combined four MRI sequences (T1-weighted imaging, T2-weighted imaging, fluid-attenuated inversion recovery imaging and diffusion weighted imaging) and a clinical model were developed to predict the prognosis of anti-NMDAR encephalitis. A fusion model combing a clinical model and two machine learning-based models was built. The performances of the fusion model, clinical model, DL-based models and radiomics-based models were compared using the area under the receiver operating characteristic curve (AUC) and accuracy and then assessed by paired t-tests (P < 0.05 was considered significant).ResultsThe fusion model achieved the significantly greatest predictive performance in the internal test dataset with an AUC of 0.963 [95% CI: (0.874-0.999)], and also significantly exhibited an equally good performance in the external validation dataset, with an AUC of 0.927 [95% CI: (0.688-0.975)]. The radiomics_combined model (AUC: 0.889; accuracy: 0.857) provided significantly superior predictive performance than the DL_combined (AUC: 0.845; accuracy: 0.857) and clinical models (AUC: 0.840; accuracy: 0.905), whereas the clinical model showed significantly higher accuracy. Compared with all single-sequence models, the DL_combined model and the radiomics_combined model had significantly greater AUCs and accuracies.ConclusionsThe fusion model combining clinical variables and machine learning-based models may have early predictive value for poor outcomes associated with anti-NMDAR encephalitis.