CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy

Abstract Background Predicting recurrence after stereotactic body radiotherapy (SBRT) in non-small cell lung cancer (NSCLC) patients is problematic, but critical for the decision of following treatment. This study aims to investigate the association of imaging features derived from the first follow-up computed tomography (CT) on lung cancer patient outcomes following SBRT, and identify patients at high risk of recurrence. Methods Fifty nine biopsy-proven non-small cell lung cancer patients were qualified for this study. The first follow-up CTs were performed about 3 months after SBRT (median time: 91 days). Imaging features included 34 manually scored radiological features (semantics) describing the lesion, lung and thorax and 219 quantitative imaging features (radiomics) extracted automatically after delineation of the lesion. Cox proportional hazard models and Harrel’s C-index were used to explore predictors of overall survival (OS), recurrence-free survival (RFS), and loco-regional recurrence-free survival (LR-RFS). Five-fold cross validation was performed on the final prognostic model. Results The median follow-up time was 42 months. The model for OS contained Eastern Cooperative Oncology Group (ECOG) performance status (HR = 3.13, 95% CI: 1.17–8.41), vascular involvement (HR = 3.21, 95% CI: 1.29–8.03), lymphadenopathy (HR = 3.59, 95% CI: 1.58–8.16) and the 1st principle component of radiomic features (HR = 1.24, 95% CI: 1.02–1.51). The model for RFS contained vascular involvement (HR = 3.06, 95% CI: 1.40–6.70), vessel attachment (HR = 3.46, 95% CI: 1.65–7.25), pleural retraction (HR = 3.24, 95% CI: 1.41–7.42), lymphadenopathy (HR = 6.41, 95% CI: 2.58–15.90) and relative enhancement (HR = 1.40, 95% CI: 1.00–1.96). The model for LR-RFS contained vascular involvement (HR = 4.96, 95% CI: 2.23–11.03), lymphadenopathy (HR = 2.64, 95% CI: 1.19–5.82), circularity (F13, HR = 1.60, 95% CI: 1.10–2.32) and 3D Laws feature (F92, HR = 1.96, 95% CI: 1.35–2.83). Five-fold cross-validated the areas under the receiver operating characteristic curves (AUC) of these three models were all above 0.8. Conclusions Our analysis reveals disease progression could be prognosticated as early as 3 months after SBRT using CT imaging features, and these features would be helpful in clinical decision-making