title: Reproducibility of radiomics for deciphering tumor phenotype with imaging.
abstract: Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.
authors.affiliation: Department of Radiology, Columbia University Medical Center, 710 West 168th Street, New York NY 10032, USA.
authors.forename: Binsheng
authors.initials: B
authors.lastname: Zhao
authors.affiliation: Department of Radiology, Columbia University Medical Center, 710 West 168th Street, New York NY 10032, USA.
authors.forename: Yongqiang
authors.initials: Y
authors.lastname: Tan
authors.affiliation: Department of Biostatistics, Columbia University Medical Center, 722 West 168th Street, New York NY 10032, USA.
authors.forename: Wei-Yann
authors.initials: WY
authors.lastname: Tsai
authors.affiliation: Department of Radiology, Columbia University Medical Center, 710 West 168th Street, New York NY 10032, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Qi
authors.affiliation: Department of Radiology, Columbia University Medical Center, 710 West 168th Street, New York NY 10032, USA.
authors.forename: Chuanmiao
authors.initials: C
authors.lastname: Xie
authors.affiliation: Department of Radiology, Columbia University Medical Center, 710 West 168th Street, New York NY 10032, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Lu
authors.affiliation: Department of Radiology, Columbia University Medical Center, 710 West 168th Street, New York NY 10032, USA.
authors.forename: Lawrence H
authors.initials: LH
authors.lastname: Schwartz
mesh_terms: D000465:Algorithms; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D010641:Phenotype; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*