[18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab

Abstract

Purpose
Immune checkpoint inhibitors have changed the paradigm of cancer treatment, however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [¹⁸F]FDG PET radiomics signature (iRADIOMICS) predicts response of metastatic NSCLC patients to pembrolizumab better than the current clinical standards.

Methods        
Thirty patients receiving pembrolizumab were scanned with [¹⁸F]FDG PET/CT at baseline, month 1 and 4. Associations of six robust primary tumor radiomics features with overall survival were analyzed with Mann-Whitney U-test (MWU), Cox proportional hazards regression analysis, and ROC curve analysis. iRADIOMICS was constructed using univariate and multivariate logistic models of the most promising feature(s). Its predictive power was compared to PD-L1 tumor proportion score (TPS) and iRECIST using ROC curve analysis. Prediction accuracies were assessed with 5-fold cross validation. 
Results
The most predictive were baseline radiomics features, e.g. Small Run Emphasis (MWU, p = 0.001; hazard ratio = 0.46, p = 0.007; AUC = 0.85 (95% CI 0.69-1.00)). Multivariate iRADIOMICS was found superior to the current standards in terms of predictive power and timewise with the following AUC (95% CI) and accuracy (standard deviation): iRADIOMICS (baseline), 0.90 (0.78-1.00), 78% (18%); PD-L1 TPS (baseline), 0.60 (0.37-0.83), 53% (18%); iRECIST (month 1), 0.79 (0.62-0.95), 76% (16%); iRECIST (month 4), 0.86 (0.72-1.00), 76% (17%).

Conclusions
Multivariate iRADIOMICS was identified as a promising imaging biomarker, which could improve management of metastatic NSCLC patients treated with pembrolizumab. The predicted non-responders could be offered other treatment options to improve their overall survival.