Integration of Risk of Survival Measures Estimated From Pre- and Post-Treatment CT Scans Improves Stratification of Early Stage Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

Abstract Purpose To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and post-treatment CT scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy. Experimental design Two cohorts of ES-NSCLC patients uniformly treated with SBRT (a median dose of 50Gy in 4-5 fractions) were studied. Prediction models were built on a discovery cohort of 100 patients with treatment planning CT scans, and then were applied to a separate validation cohort of 60 patients with pre- and posttreatment CT scans for evaluating their performance. Results Prediction models achieved a c-index up to 0.734 in predicting survival outcomes of the validation cohort. The integration of the pre-treatment risk of survival measures (Risk-High vs. Risk-Low) and changes (Risk-Increase vs. Risk-Decrease) in risk of survival measures between the pre-treatment and post-treatment scans further stratified the patients into 4 subgroups (Risk: high, increase; Risk: high, decrease; Risk: low, increase; Risk: low, decrease) with significant difference (χ2 = 18.549, p=3.4e-04, log-rank test). There was also significant difference between risk increase and risk decrease group (χ2= 6.80, p=0.0091, log-rank test). In addition, significant difference (χ2= 7.493, p=0.0062, log-rank test) was observed between the high-risk and low-risk groups obtained based on the pre-treatment risk of survival measures. Conclusion The integration of risk of survival measures estimated from pre- and post-treatment CT scans can help differentiate patients with good expected survival from those who will do more poorly following SBRT. The analysis of these radiomics-based longitudinal risk measures may help identify early stage NSCLC patients who will benefit from adjuvant treatment after lung SBRT, such as immunotherapy.