Radiomics model for Predicting Stroke-associated Pneumonia Based on CT

Stroke-associated Pneumonia (SAP) is a common complication of intracerebral hemorrhage (ICH), which increases the burden of hospitalization and the difficulty of nursing, and significantly increases the mortality of patients. To help doctors get risk scores of SAP when ICH patients are just admitted, predicting SAP rapidly and effectively is important.A total of 244 subjects were involved in this research aiming to use radiomics method to extract features from the CT of ICH patients, and use recursive methods to select features and grid search to establish three machine learning models. Two binary classification implementations were carried out to predict the development of SAP, which were to classify with-or-without SAP, and SAP above moderate level.In the two classification tasks, selected different radiomics features for each model, and the AUCs and accuracies of the three constructed models on the test set were all above 0.7.Our experimental results showed that based on brain CT of ICH patients and without collecting the baseline data of patients, radiomics features can be used to quickly and effectively predict the development of SAP, and shape (3D) feature and gray-level feature account for a lot of weight.