Mazen Soufi1, Hidetaka Arimura2, Takahiro Nakamoto3, Taka-Aki Hirose3, Saiji Ohga4, Yoshiyuki Umezu5, Hiroshi Honda4, Tomonari Sasaki4. 1. Graduate School of Medical Sciences, Kyushu University 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan; Research Fellow at Japan Society for the Promotion of Science 5-3-1, Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan. 2. Faculty of Medical Sciences, Kyushu University 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. Electronic address: arimurah@med.kyushu-u.ac.jp. 3. Graduate School of Medical Sciences, Kyushu University 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. 4. Faculty of Medical Sciences, Kyushu University 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. 5. Kyushu University Hospital 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Abstract
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.
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 cancerpatients, 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 cancerpatients (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 cancerpatients. CONCLUSIONS: This study suggests a novel approach to select temporally stable radiomic features, which could hold prognostic powers in lung cancerpatients.