A computerized framework for monitoring four-dimensional dose distributions during stereotactic body radiation therapy using a portal dose image-based 2D/3D registration approach

Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images

PURPOSE

We aimed to explore the temporal stability of radiomic features in the presence of tumor motion and the prognostic powers of temporally stable features.

METHODS

We selected single fraction dynamic electronic portal imaging device (EPID) (n = 275 frames) and static digitally reconstructed radiographs (DRRs) of 11 lung cancer patients, who received stereotactic body radiation therapy (SBRT) under free breathing. Forty-seven statistical radiomic features, which consisted of 14 histogram-based features and 33 texture features derived from the graylevel co-occurrence and graylevel run-length matrices, were computed. The temporal stability was assessed by using a multiplication of the intra-class correlation coefficients (ICCs) between features derived from the EPID and DRR images at three quantization levels. The prognostic powers of the features were investigated using a different database of lung cancer patients (n = 221) based on a Kaplan-Meier survival analysis.

RESULTS

Fifteen radiomic features were found to be temporally stable for various quantization levels. Among these features, seven features have shown potentials for prognostic prediction in lung cancer patients.

CONCLUSIONS

This study suggests a novel approach to select temporally stable radiomic features, which could hold prognostic powers in lung cancer patients.