Ying Li1, Guanghua Tan1, Mark Vangel1, Jonathan Hall1,2, Wenli Cai1. 1. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 2. Department of Computer and Electrical Engineering, Boston University, Boston, MA, USA.
Abstract
BACKGROUND: Existing studies have demonstrated that imaging parameters may affect radiomic features. However, the influence of feature calculating parameters has been overlooked. The purpose of this study is to investigate the influence of feature calculating parameters (gray-level range and bin size) on the reproducibility of CT radiomic features. METHODS: Thirty-six CT scans from an anthropomorphic thoracic phantom were acquired with different imaging parameters including effective dose, pitch, slice thicknesses and reconstruction kernels. The influence of feature calculating parameters was investigated in terms of three gray-level ranges and eleven gray-level bin sizes. Feature reproducibility was assessed by the intraclass correlation coefficient (ICC) with the cutoff value of 0.8 and the coefficient of variation (CV) with the cutoff value of 20%. The agreements of reproducible features in different ranges and bin sizes were analyzed by Kendall's W test and Kappa test. The proportions of reproducible features, in terms of two calculating, four imaging and two segmentation parameters, were evaluated using Cochran's Q test and Dunn's test. RESULTS: For the three gray-level ranges, 50% (44/88) of features were reproducible with a perfect agreement (Kendall's W coefficient 0.844, P<0.001). Of the 72 features that may be influenced by gray-level bin size, 33.3% (24/72) were reproducible for 11 bin sizes with a perfect agreement (Kendall's W coefficient 0.879, P<0.001). For the proportions of reproducible features, there was no statistically significant difference among three ranges (P=0.420), but there was among eleven bin sizes (P=0.013). The proportions of reproducible features in feature calculating parameters were statistically significantly lower than those in imaging parameters (adjusted P<0.05). CONCLUSIONS: Feature calculating parameters may have a greater influence than imaging parameters on the reproducibility of CT radiomic features, which should be given special attention in clinical applications. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: Existing studies have demonstrated that imaging parameters may affect radiomic features. However, the influence of feature calculating parameters has been overlooked. The purpose of this study is to investigate the influence of feature calculating parameters (gray-level range and bin size) on the reproducibility of CT radiomic features. METHODS: Thirty-six CT scans from an anthropomorphic thoracic phantom were acquired with different imaging parameters including effective dose, pitch, slice thicknesses and reconstruction kernels. The influence of feature calculating parameters was investigated in terms of three gray-level ranges and eleven gray-level bin sizes. Feature reproducibility was assessed by the intraclass correlation coefficient (ICC) with the cutoff value of 0.8 and the coefficient of variation (CV) with the cutoff value of 20%. The agreements of reproducible features in different ranges and bin sizes were analyzed by Kendall's W test and Kappa test. The proportions of reproducible features, in terms of two calculating, four imaging and two segmentation parameters, were evaluated using Cochran's Q test and Dunn's test. RESULTS: For the three gray-level ranges, 50% (44/88) of features were reproducible with a perfect agreement (Kendall's W coefficient 0.844, P<0.001). Of the 72 features that may be influenced by gray-level bin size, 33.3% (24/72) were reproducible for 11 bin sizes with a perfect agreement (Kendall's W coefficient 0.879, P<0.001). For the proportions of reproducible features, there was no statistically significant difference among three ranges (P=0.420), but there was among eleven bin sizes (P=0.013). The proportions of reproducible features in feature calculating parameters were statistically significantly lower than those in imaging parameters (adjusted P<0.05). CONCLUSIONS: Feature calculating parameters may have a greater influence than imaging parameters on the reproducibility of CT radiomic features, which should be given special attention in clinical applications. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: Ruben T H M Larue; Janna E van Timmeren; Evelyn E C de Jong; Giacomo Feliciani; Ralph T H Leijenaar; Wendy M J Schreurs; Meindert N Sosef; Frank H P J Raat; Frans H R van der Zande; Marco Das; Wouter van Elmpt; Philippe Lambin Journal: Acta Oncol Date: 2017-09-08 Impact factor: 4.089
Authors: Roberto Berenguer; María Del Rosario Pastor-Juan; Jesús Canales-Vázquez; Miguel Castro-García; María Victoria Villas; Francisco Mansilla Legorburo; Sebastià Sabater Journal: Radiology Date: 2018-04-24 Impact factor: 11.105
Authors: Mitchell M Goodsitt; Heang-Ping Chan; Ted W Way; Sandra C Larson; Emmanuel G Christodoulou; Jeomsoon Kim Journal: Med Phys Date: 2006-08 Impact factor: 4.071
Authors: Roger Sun; Elaine Johanna Limkin; Maria Vakalopoulou; Laurent Dercle; Stéphane Champiat; Shan Rong Han; Loïc Verlingue; David Brandao; Andrea Lancia; Samy Ammari; Antoine Hollebecque; Jean-Yves Scoazec; Aurélien Marabelle; Christophe Massard; Jean-Charles Soria; Charlotte Robert; Nikos Paragios; Eric Deutsch; Charles Ferté Journal: Lancet Oncol Date: 2018-08-14 Impact factor: 41.316
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919