Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images. (February 2018)

• Record Type: Journal Article
• Authors: Soufi, Mazen; Arimura, Hidetaka; Nakamoto, Takahiro; Hirose, Taka-aki; Ohga, Saiji; Umezu, Yoshiyuki; Honda, Hiroshi; Sasaki, Tomonari
• Is Part Of: Physica medica. Volume 46(2018)
• Page Start: 32
• Page End: 44
• Publication Date: 2018-02
• Subjects: Temporal stability -- Radiomic feature -- EPID image -- Prognostic prediction; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2017.11.037
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: An approach to select temporally stable radiomic features is suggested. Seven features were found to be temporally stable and have prognostic powers. EPID images could help in assessing the temporal stability of radiomic features. 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.