Radiographic Texture Reproducibility: The Impact of Different Materials, their Arrangement, and Focal Spot Size

Younes Qasempour, Amirsalar Mohammadi, Mostafa Rezaie, Parisa Pouryazadanpanah, Fatemeh Ziaddini, comAlma Borbori, Isaac Shiri, Ghasem Hajianfar, Azam Janati Esfahani, Sareh Ghasemirad, Hamid Abdollahi

DOI: 10.4103/jmss.JMSS_64_19

Abstract


Purpose: Feature reproducibility is a critical issue in quantitative radiomic studies. The aim of this study is to assess how radiographic radiomic textures behave against changes in phantom materials, their arrangements, and focal spot size. Materials and Methods: A phantom with detachable parts was made using wood, sponge, Plexiglas, and rubber. Each material had 1 cm thickness and was imaged for consecutive time. The phantom also was imaged by change in the arrangement of its materials. Imaging was done with two focal spot sizes including 0.6 and 1.2 mm. All images were acquired with a digital radiography machine. Several texture features were extracted from the same size region of interest in all images. To assess reproducibility, coefficient of variation (COV), intraclass correlation coefficient (ICC), and Bland–Altman tests were used. Results: Results show that 59%, 50%, and 4.5% of all features are most reproducible (COV < 5%) against change in focal spot size, material arrangements, and phantom’s materials, respectively. Results on Bland–Altman analysis showed that there is just a nonreproducible feature against change in the focal spot size. On the ICC results, we observed that the ICCs for more features are > 0.90 and there were few features with ICC lower than 0.90. Conclusions: We showed that radiomic textures are vulnerable against changes in materials, arrangement, and different focal spot sizes. These results suggest that a careful analysis of the effects of these parameters is essential before any radiomic clinical application.

Keywords


Arrangement, focal spot, materials, radiomic textures, reproducibility

Full Text:

PDF

References


Abdollahi H, Shiri I, Heydari M. Medical imaging technologists in radiomics era: An alice in wonderland problem. Iran J Public Health 2019;48:184-6.

Abdollahi H, Mahdavi SR, Shiri I, Mofid B, Bakhshandeh M, Rahmani K. Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy. J Cancer Res Ther 2019;15:S11-9.

Abdollahi H, Tanha K, Mofid B, Razzaghdoust A, Saadipoor A, Khalafi L, et al. MRI radiomic analysis of IMRT-induced bladder wall changes in prostate cancer patients: A relationship with radiation dose and toxicity. J Med Imaging Radiat Sci 2019;50:252-60.

Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: A review of technology and methodological procedures. Br J Radiol 2017;90:20160665.

Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol 2016;61:R150-66.

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. Eur Radiol 2017;27:4498-509.

Abdollahi H, Mahdavi SR, Mofid B, Bakhshandeh M, Razzaghdoust A, Saadipoor A, et al. Rectal wall MRI radiomics in prostate cancer patients: Prediction of and correlation with early rectal toxicity. Int J Radiat Biol 2018;94:829-37.

Abdollahi H, Mostafaei S, Cheraghi S, Shiri I, Rabi Mahdavi S, Kazemnejad A. Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study. Phys Med 2018;45:192-7.

Saeedi E, Dezhkam A, Beigi J, Rastegar S, Yousefi Z, Mehdipour LA, et al. Radiomic feature robustness and reproducibility in quantitative bone radiography: A study on radiologic parameter changes. J Clin Densitom 2019;22:203-13.

Baeßler B, Weiss K, Pinto Dos Santos D. Robustness and reproducibility of radiomics in magnetic resonance imaging: A phantom study. Invest Radiol 2019;54:221-8.

Fiset S, Welch ML, Weiss J, Pintilie M, Conway JL, Milosevic M, et al. Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. Radiother Oncol 2019;135:107-14.

Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 2014;9:e102107.

Traverso A, Wee L, Dekker A, Gillies R. Repeatability and reproducibility of radiomic features: A systematic review. Int J Radiation Oncol Bio Phy 2018;102:1143-58.

Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 2016;6:23428.

Zwanenburg A. Radiomics in nuclear medicine: Robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 2019;46:2638-55.

Huda W, Abrahams RB. X-ray-based medical imaging and resolution. AJR Am J Roentgenol 2015;204:W393-7.

Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol 2015;50:757-65.

Mackin D, Ger R, Dodge C, Fave X, Chi PC, Zhang L, et al. Effect of tube current on computed tomography radiomic features. Sci Rep 2018;8:2354.

Pfaehler E, Beukinga RJ, De Jong JR, Slart RH, Slump CH, Dierckx RA, et al. Repeatability of 18F-FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method. Med Phy 2019;46:665-78.

Abdollahi H. Radiotherapy dose painting by circadian rhythm based radiomics. Med Hypotheses 2019;133:109415.

Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: The need for standardized methodology in tumor texture analysis. Sci Rep 2015;5:11075.

El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 2009;42:1162-71.


Refbacks

  • There are currently no refbacks.