A CT-Based Radiomics Nomogram To Predict The Biological Activity of Hepatic Alveolar Echinococcosis
Abstract BackgroundThis study aims to establish a computed tomography (CT) - based radiomics nomogram to predict the biological activity of hepatic alveolar echinococcosis (HAE).MethodsA total of 174 HAE patients (139 for training, 35 for test) were enrolled whose CT and positron emission tomography-computed tomography (PET/CT) examinations were performed before surgery, and the biological activity was evaluated according to the PET/CT. Radiomic features were extracted from CT images, based on which radiomic scores (Rad-score) were calculated with the least absolute shrinkage and selection operator logistic regression. Three radiomics models (K-Nearest Neighbors, Logical regression, and Multilayer Perceptron), including only radiomic features and a radiomics nomogram, comprised of demographics, clinical indexes, and radiomic features were constructed respectively to predict the biological activity of HAE. The model performance was evaluated by area under curve (AUC), decision curve, and calibration curve.Results30 features in total were selected as optimal radiomic features and considered as input to calculate the Rad-score. There were no significant differences in the predictive efficacy between the combined models and the radiomics models from the perspective of the decision curve. The radiomics models was unparalleled, with an AUC of 0.952 (95%CI=0.902~0.981, P<0.0001) and 0.800 (95%CI=0.631~0.916, P<0.0020) in the training and testing cohort, respectively.ConclusionThe radiomics nomogram model showed great potential in identifying HAE biological activity.