Journal
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
Volume 46, Issue -, Pages 32-44Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ejmp.2017.11.037
Keywords
Temporal stability; Radiomic feature; EPID image; Prognostic prediction
Funding
- Japan Society for the Promotion of Science [16J04082]
- Program for Supporting Educations and Researches on Mathematics and Data Science in Kyushu University
- Grants-in-Aid for Scientific Research [16J04082] Funding Source: KAKEN
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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.
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