Use of Radiomics to Predict Response to Immunotherapy of Malignant Tumors of the Digestive System

BACKGROUND: Despite the promising results of immunotherapy in cancer treatment, new response patterns, including pseudoprogression and hyperprogression, have been observed. Radiomics is the automated extraction of high-fidelity, high-dimensional imaging features from standard medical images, allowing comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. This study assessed whether radiomics can predict response to immunotherapy in patients with malignant tumors of the digestive system. MATERIAL/METHODS: Computed tomography (CT) images of patients with malignant tumors of the digestive system obtained at baseline and after immunotherapy were subjected to radiomics analyses. Radiomics features were extracted from each image. The formula of the screened features and the final predictive model were obtained using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. RESULTS: Imaging analysis was feasible in 87 patients, including 3 with pseudoprogression and 7 with hyperprogression. One hundred ten radiomics features were obtained before and after treatment, including 109 features of the target lesions and 1 of the aorta. Four models were constructed, with the model constructed from baseline and post-treatment CT features having the best classification performance, with a sensitivity, specificity, and AUC of 83.3%, 88.9%, and 0.806, respectively. CONCLUSIONS: Radiomics can predict the response of patients with malignant tumors of the digestive system to immunotherapy and can supplement conventional evaluations of response.