Radiomic Feature Reproducibility: The Impact of Inter-Scanner and Inter-Modality Variations

Document Type : Original Paper


1 Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

2 Department of Radiation Oncology, Isfahan Milad Hospital, Isfahan, Iran

3 Department of Radiotherapy Oncology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

4 Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran

5 Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


Introduction: Radiomic features robustness analysis is a critical issue before clinical decision making. In this study, the reproducibility and robustness of radiomic features in computed tomography (CT) and magnetic resonance (MR) images of glioblastoma cancer patients were analyzed regarding inter-scanner and inter-modality variations.
Material and Methods: CT and MR Images of eighteen glioblastoma cancer patients were used to extract the radiomic features following image segmentation. Coefficient of variation (COV), intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC) analysis were done to select the most robust features in all paired combinations of CT and MR images include T1-T2, T1-FLAIR, T1-ADC, T1-CT, T2-FLAIR, T2-ADC, T2-CT, FLAIR-ADC, FLAIR-CT, and ADC-CT.
Results: The features with COV ≤ 5% or ICC ≥ 90% or CCC ≥ 90%, considered as the most robust features, include the shape features, Minimum (belong to first-order Features), IMC1, IDN, IDMN (belong to GLCM), and Run Length Non-Uniformity (belongs to Gray Level Run Length Matrix).
Conclusion: In this study we presented a large image feature variation among different imaging modalities including CT and MRI. Our results identified several robust features that could be used for further clinical analysis.


Main Subjects

  1. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ. Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer. 2012 Mar 1;48(4):441-6.
  2. Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, Schwartz LH. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Scientific reports. 2016 Mar 24;6(1):1-7.
  3. Meng Y, Sun J, Qu N, Zhang G, Yu T, Piao H. Application of radiomics for personalized treatment of cancer patients. Cancer management and research. 2019;11:10851.
  4. Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7.
  5. Narang S, Lehrer M, Yang D, Lee J, Rao A. Radiomics in glioblastoma: current status, challenges and potential opportunities. Translational Cancer Research. 2016 Aug 1;5(4):383-97.
  6. Peeken JC, Bernhofer M, Wiestler B, Goldberg T, Cremers D, Rost B, Wilkens JJ, Combs SE, Nüsslin F. Radiomics in radiooncology–challenging the medical physicist. Physica medica. 2018 Apr 1;48:27-36.
  7. Baeßler B, Weiss K, Dos Santos DP. Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Investigative radiology. 2019 Apr 1;54(4):221-8.
  8. Cattell R, Chen S, Huang C. Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Visual computing for industry, biomedicine, and art. 2019 Dec;2(1):1-6.
  9. Park JE, Kim HS. Radiomics as a quantitative imaging biomarker: practical considerations and the current standpoint in neuro-oncologic studies. Nuclear medicine and molecular imaging. 2018 Apr;52(2):99-108.
  10. Saeedi E, Dezhkam A, Beigi J, Rastegar S, Yousefi Z, Mehdipour LA, Abdollahi H, Tanha K. Radiomic feature robustness and reproducibility in quantitative bone radiography: a study on radiologic parameter changes. Journal of Clinical Densitometry. 2019 Apr 1;22(2):203-13.
  11. Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A. The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. European radiology. 2017 Nov;27(11):4498-509.
  12. Bartko JJ. The intraclass correlation coefficient as a measure of reliability. Psychological reports. 1966 Aug;19(1):3-11.
  13. Fisher RA. Statistical methods for research workers. InBreakthroughs in statistics 1992 (pp. 66-70). Springer, New York, NY.
  14. Lawrence I, Lin K. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989 Mar 1:255-68.
  15. Rastegar S, Beigi J, Saeedi E, Shiri I, Qasempour Y, Rezaei M, Abdollahi H. Radiographic Image Radiomics Feature Reproducibility: A Preliminary Study on the Impact of Field Size. Journal of medical imaging and radiation sciences. 2020 Mar 1;51(1):128-36.
  16. Moradmand H, Aghamiri SM, Ghaderi R. Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma. Journal of applied clinical medical physics. 2020 Jan;21(1):179-90.






Volume 18, Issue 6
November and December 2021
Pages 397-402
  • Receive Date: 28 September 2020
  • Revise Date: 04 December 2021
  • Accept Date: 10 November 2020