Predicting Response of Lung Cancer with Definitive Radiation Therapy: A Radiomics-Based Prediction Model.

Purpose/Objective(s) We hypothesize that radiomic features provide information for predicting early treatment response of lung cancer with definitive radiation therapy (RT). This study aims to develop and validate a radiomics-based model for predicting early treatment response of lung cancer with definitive radiation therapy (RT). Materials/Methods From January 2018 to March 2020, 128 lung cancer patients with definitive radiation therapy (RT) had enrolled retrospectively in the study and randomly divided into training set (n = 90) and test set(n = 38). Based on the Response Evaluation Criteria in Solid Tumors1.1 (RECIST1.1), response of lung cancer with definitive radiation therapy (RT) was dichotomized that treatment was effective if tumor volume decreased over 30༅ at the first follow-up CT after definitive radiation therapy (RT). Radiomic features were extracted using an open-source software development tool from the Gross Tumor Volume (GTV) of planning CT images. Specifically, a total of 107 radiomics features are extracted that include the first-order statistic features, the second-order texture features, and 2D-3D shape-based features. To reduce the feature dimensionality, we used the RFE (Recursive Feature Elimination) method to filter the features and obtained 13 out of 107 features that are most effective for final classification. By analyzing the data and comparing the experimental validation, we employed Random Forest classifier for the response prediction. Results All patients received definitive radiation therapy (56-70Gy) and no operation. 36 patients received Intensity-modulated radiation therapy (IMRT) and 92 patients received Volumetric Intensity Modulated Arc Therapy (VMAT) (P = 0.951). 103 patients are men and 25 patients are women (P = 0.969). In addition to the five-fold cross-validation, we evaluated the classification results using the independent test set. The AUC for five-fold cross validation is 0.798, and the AUC for the independent test set is 0.771. Conclusion This study demonstrated that radiomic model shows a good prediction value of early treatment response of lung cancer with definitive radiation therapy (RT). Radiomic features extracted from the Gross Tumor Volume (GTV) of planning CT images may predict response of radiation and might be helpful for individual treatments. The result still needs to be further validated by increasing the amount of data, especially prospective data.