Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy - Xie, Dong; Xu, Fangyi; Zhu, Wenchao; Pu, Cailing; Huang, Shaoyu; Lou, Kaihua; Wu, Yan; Huang, Dingpin; He, Cong; Hu, Hongjie (Frontiers in Oncology, 2022)
Objective: To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy. Methods: Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (TP0) and after the 2nd-3rd immunotherapy cycles (TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS. Results: The delta radiomics model (RM) showed superior predictive accuracy over the TP0 and TP1 radiomics models (RMs). The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05). Additionally, the delta RM had a higher predictive accuracy compared to PD-L1 expression alone (P<0.0001). Conclusions: The combined prediction model can achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.