Predefined and data-driven CT radiomics predict recurrence-free and overall survival in patients with pulmonary metastases treated with stereotactic body radiotherapy.

Abstract

Background: This retrospective study explores two radiomics methods combined with other clinical variables for predicting recurrence free survival (RFS) and overall survival (OS) in patients with pulmonary metastases treated with stereotactic body radiotherapy (SBRT).

Methods: 111 patients with 163 metastases treated with SBRT were included with a median follow-up time of 927 days. First-order radiomic features were extracted using two methods: 2D CT texture analysis (CTTA) using TexRAD software, and a data-driven technique: functional principal components analysis (FPCA) using segmented tumoral and peri-tumoural 3D regions.

Results: Using both Kaplan-Meier analysis with its log-rank tests and multivariate Cox regression analysis, the best radiomic features of both methods were selected: CTTA-based "entropy" and the FPCA-based first mode of variation of tumoural CT density histogram: "F1." Predictive models combining radiomic variables and age showed a C-index of 0.62 95% with a CI of (0.57-0.67). "Clinical indication for SBRT" and "lung primary cancer origin" were strongly associated with RFS and improved the RFS C-index: 0.67 (0.62-0.72) when combined with the best radiomic features. The best multivariate Cox model for predicting OS combined CTTA-based features-skewness and kurtosis-with size and "lung primary cancer origin" with a C-index of 0.67 (0.61-0.74).

Conclusion: In conclusion, concise predictive models including CT density-radiomics of metastases, age, clinical indication, and lung primary cancer origin can help identify those patients with probable earlier recurrence or death prior to SBRT treatment so that more aggressive treatment can be applied.