Development and evaluation clinical-radiomics analysis based on T1-weighted imaging for diagnosing neonatal acute bilirubin encephalopathy - Yu, Jinhong; Liu, Yangyingqiu; Jiang, Yuhan; Gao, Bingbing; Wang, Jingshi; Guo, Yan; Xie, Lizhi; Miao, Yanwei (Frontiers in Neurology, 2023)
Abstract Purpose: To investigate the value of clinical-radiomics analysis based on T1-weighted imaging (T1WI) for predicting ABE in neonates. Methods: In this retrospective study, sixty-one neonates with clinically confirmed acute bilirubin encephalopathy (ABE) and 50 healthy control neonates were recruited between October 2014 and March 2019. Two radiologists’ visual diagnoses for all subjects were independently based on T1WI. Eleven clinical and 216 radiomics features were obtained and analyzed. Seventy percent of samples were randomly selected as the training group and were used to establish a clinical-radiomics model to predict ABE, the remaining samples were used to validate the performance of the models. The discrimination performance was accessed by receiver operating characteristic (ROC) curve analysis. Results: Seventy-eight neonates for training (median age, 9 days; interquartile range, 7-20 days; 49 men) and 33 neonates for validation (median age, 10 days; interquartile range, 6-13 days; 24 men). Two clinical features and ten radiomics features were finally selected to construct the clinical-radiomics model. In the training group, the area under the ROC curve (AUC) was 0.90(sensitivity: 0.814; specificity: 0.914); in the validation group, the AUC was 0.93 (sensitivity: 0.944; specificity: 0.800). The AUCs of two radiologists’ and radiologists’ final visual diagnosis results based on T1WI were 0.57, 0.63 and 0.66, respectively. The discriminative performance of clinical-radiomics model in training and validation group were increased compared to radiologists’ visual diagnosis (P<0.001). Conclusions: A combined clinical-radiomics model based on T1WI has the potential to predict ABE. The application of the nomogram could potentially provide a visualized and precise clinical-support tool.