Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Abstract Purpose The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non‐small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre‐treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. Materials and methods Image features, consisting of crafted radiomic features or machine‐learned features extracted using a convolutional neural network, were calculated from pre‐treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (Rad CT ), radiomics CT and BED (Rad CT,BED ), deep learning (DL) CT (DL CT ), and DL CT and BED (DL CT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5. Results Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a Rad CT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75–0.85)) and AUC of 0.773 (0.688–0.846), and a DL CT,BED model also using three variables with an accuracy of 0.798 (0.649–0.829) and AUC of 0.812 (0.755–0.867). Conclusion According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.