Non-linear modifications enhance prediction of pathological response to pre-operative PD-1 blockade in lung cancer: A longitudinal hybrid radiological model

Major pathologic remission (MPR, residual tumor <10%) is a promising clinical endpoint for prognosis analysis in patients with lung cancer receiving pre-operative PD-1 blockade therapy. Most of the current biomarkers for predicting MPR such as PD-L1 and tumor mutation burden (TMB) need to be obtained invasively. They cannot overcome the spatiotemporal heterogeneity or provide dynamic monitoring solutions. Radiomics and artificial intelligence (AI) models provide a practical tool enabling non-invasive follow-up observation of tumor structural information through high-throughput data analysis. Currently, AI-based models mainly focus on the single baseline scan or pipeline, namely sole radiomics or deep learning (DL). This work merged the delta-radiomics based on the slope of classic radiomics indexes within a time interval and the features extracted by deep networks from the subtraction between the baseline and follow-up images. The subtracted images describing the tumor changes were based on the transformation generated by registration. Stepwise optimization of components was performed by repeating experiments among various combinations of DL networks, registration methods, feature selection algorithms, and classifiers. The optimized model could predict MPR with a cross-validation AUC of 0.91 and an external validation AUC of 0.85. A core set of 27 features (eight classic radiomics, 15 delta-radiomics, one classic DL features, and three delta-DL features) was identified. The changes in delta-radiomics indexes during the treatment were fitted with mathematic models. The fitting results revealed that over half of the features were of non-linear dynamics. Therefore, non-linear modifications were made on eight features by replacing the original features with non-linear fitting parameters, and the modified model achieved an improved power. The dynamic hybrid model serves as a novel and promising tool to predict the response of lesions to PD-1 blockade, which implies the importance of introducing the non-linear dynamic effects and DL approaches to the original delta-radiomics in the future.