Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

Simple Summary In locally advanced or metastatic non-small cell lung cancer (NSCLC), immunotherapy has become a standard as it can improve overall survival and progression-free survival. However, a durable clinical benefit (DCB) is only achieved in 20-50% of patients. Early identification of patients likely to benefit from this treatment is not only challenging but also crucial to avoid immune-related toxicities in patients unlikely to achieve DCB. The aim of our retrospective study was to assess the value of baseline and serial FDG-PET/CT radiomics for the prediction of response and survival in NSCLC patients undergoing immunotherapy. In a group of 83 patients, multimodality radiomics and delta-radiomics models provided added predictive value compared to conventional clinical parameters. Multimodality radiomics-based models developed using appropriate machine learning processes were able to predict progression, DCB, Overall Survival and Progression Free Survival with high confidence. Purpose: We aimed to assess the ability of radiomics features extracted from baseline (PET/CT0) and follow-up PET/CT scans, as well as their evolution (delta-radiomics), to predict clinical outcome (durable clinical benefit (DCB), progression, response to therapy, OS and PFS) in non-small cell lung cancer (NSCLC) patients treated with immunotherapy. Methods: 83 NSCLC patients treated with immunotherapy who underwent a baseline PET/CT were retrospectively included. Response was assessed at 6-8 weeks (PET/CT1) using PERCIST criteria and at 3 months with iPERCIST (PET/CT2) or RECIST 1.1 criteria using CT. The predictive performance of clinical parameters (CP), standard PET metrics (SUV, Metabolic Tumor volume, Total Lesion Glycolysis), delta-radiomics and PET and CT radiomics features extracted at baseline and during follow-up were studied. Seven multivariate models with different combinations of CP and radiomics were trained on a subset of patients (75%) using least absolute shrinkage, selection operator (LASSO) and random forest classification with 10-fold cross-validation to predict outcome. Model validation was performed on the remaining patients (25%). Overall and progression-free survival was also performed by Kaplan-Meier survival analysis. Results: Numerous radiomics and delta-radiomics parameters had a high individual predictive value of patient outcome with areas under receiver operating characteristics curves (AUCs) >0.80. Their performance was superior to that of CP and standard PET metrics. Several multivariate models were also promising, especially for the prediction of progression (AUCs of 1 and 0.96 for the training and testing subsets with the PET-CT model (PET/CT0)) or DCB (AUCs of 0.85 and 0.83 with the PET-CT-CP model (PET/CT0)). Conclusions: Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC.

Automated summary

Immunotherapy has become a standard treatment for advanced NSCLC, but only a minority of patients benefit long-term. Analyzing radiomics features from PET/CT scans can predict treatment response and survival outcomes, aiding in personalized treatment management.