A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study.

Simple Summary: In the recent years, immune checkpoint inhibitors (ICIs) have significantly modified non-small cell lung cancer (NSCLC) treatment by providing new therapeutic avenues with superior efficacy and improved tolerability over traditional cytotoxic therapies. The immune activation can lead to durable clinical responses and prolonged survival in some, but not in all of the treated NSCLC patients. Thus, there is an unmet clinical need to identify patients who are most likely to have short-term overall survival against prolonged overall survival (OS). These patients can thus be spared from potential toxicities as well as from the financial burden arising from these immune therapies. Most importantly, early prediction of these patients may allow the clinicians to choose more aggressive or effective treatment options early on, to extend the overall survival of patients. Through this study, we developed parsimonious survival risk models incorporating clinical data and imaging profiles of NSCLC patients treated with immunotherapy. These results may enable the clinicians to design more effective therapeutic regimens or modify treatment strategies for the group of short-term survivors. Background: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. Methods: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. Results: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. Conclusions: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted. [ABSTRACT FROM AUTHOR]