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Research progress on radiomics reproducibility
Qiu Qingtao, Duan Jinghao, Gong Guanzhong, Yin Yong
Shandong Province Key Laboratory of Medical Physics and Image Processing Technology,Institution of Biomedical Sciences,School of Physics and Electronics,Shandong Normal University,Jinan 250358,China (Qiu QT);Department of Radiation Oncology,Shandong Cancer Hospital Affiliated to Shandong University,Jinan 250117,China (Duan JH,Gong GZ,Yin Y)
Abstract Radiomics has played an irreplaceable role along with the development of precision medicine. In the field of radiomics researches, the stability of imaging features is of vital significance, which is directly linked to the modeling analysis. In this review, we summarized the recent research progress on the reproducibility problems in four crucial steps of the standard workflow of radiomics including imaging acquisition and reconstruction, region of interest (ROI) segmentation, imaging feature extraction and modeling establishment. In addition, the commonly used software related to radiomics was briefly introduced.
Qiu Qingtao,Duan Jinghao,Gong Guanzhong et al. Research progress on radiomics reproducibility[J]. Chinese Journal of Radiation Oncology, 2018, 27(3): 327-330.
Qiu Qingtao,Duan Jinghao,Gong Guanzhong et al. Research progress on radiomics reproducibility[J]. Chinese Journal of Radiation Oncology, 2018, 27(3): 327-330.
[1] 王敏,宋彬,黄子星,等.大数据时代的精准影像医学:放射组学[J].中国普外基础与临床杂志,2016(6):752-755.DOI:10.7507/1007-9424.20160198. Wang M,Song B,Huang ZX,et al. Precision medical imaging in big data:Radiomics[J],Chin J Bas Clin Gene Surg,2016(6):752-755. DOI:10.7507/1007-9424.20160198. [2] 苏会芳,周国锋,谢传淼,等.放射组学的兴起和研究进展[J].中华医学杂志,2015,95(7):553-556.DOI:10.3760/cma.j.issn.0376-2491.2015.07.021. Su HF,Zhou GF,Xie CM,et al. The rise and development of radiology[J].Chin J Med,2015,95(7):553-556.DOI:10.3760/cma.j.issn.0376-2491.2015.07.021. [3] Lambin P,Riosvelazquez E,Leijenaar R,et al. Radiomics:extracting more information from medical images using advanced feature analysis[J].Eur J Cancer,2012,48(4):441-446.DOI:10.1016/j.ejca.2011.11.036. [4] Kumar V,Gu Y,Basu S,et al. Radiomics:the process and the challenges[J].Mag Res Imag,2012,30(9):1234-1248.DOI:10.1016/j.mri.2012.06.010. [5] Gillies RJ,Kinahan PE,Hricak H.Radiomics:images are more than pictures,they are data[J].Radiology,2015,278(2):563-577.DOI:10.1148/radiol.2015151169. [6] Mazurowski MA.Radiogenomics:what it is and why it is important[J].J Am Coll Radiol,2015,12(8):862-866.DOI:10.1016/j.jacr.2015.04.019. [7] Aerts HJ,Velazquez ER,Leijenaar RT,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J].Nat Communicat,2014,5.DOI:10.1038/ncomms5006. [8] Hatt M,Majdoub M,Vallières M,et al.18F-FDG PET uptake characterization through texture analysis:investigating the complementary nature of heterogeneity and functional tumor volume in a multi–cancer site patient cohort[J].J Nucl Med,2015,56(1):38-44.DOI:10.2967/jnumed.114.144055. [9] Parekh V,Jacobs MA.Radiomics:a new application from established techniques[J].Exp Rev Prec Med Drug Devel,2016,1(2):207-226.DOI:10.1080/23808993.2016.1164013. [10] 胡盼盼,王佳舟,胡伟刚,等.影像组学在精准放疗中的应用[J].中华放射肿瘤学杂志,2017,26(1):103-106.DOI:10.3760/cma.j.issn.1001-4221.2017.01.025. Hu PP,Wang JZH,Hu WG,et al. Radiomics in precision radiotherapy[J].Chin J Radiat Oncol,2017,26(1):103-106. DOI:10.3760/cma.j.issn.1001-4221.2017.01.025. [11] Balagurunathan Y,Gu Y,Wang H,et al. Reproducibility and prognosis of quantitative features extracted from CT images[J].Translat Oncol,2014,7(1):72-87.DOI:10.1593/tlo.13844. [12] Balagurunathan Y,Kumar V,Gu Y,et al. Test–retest reproducibility analysis of lung CT image features[J].J Dig Imag,2014,27(6):805-823.DOI:10.1007/s10278-014-9716-x. [13] Leijenaar RT,Carvalho S,Velazquez ER,et al. Stability of FDG-PET Radiomics features:an integrated analysis of test-retest and inter-observer variability[J].Acta Oncol,2013,52(7):1391-1397.DOI:10.3109/0284186X.2013.812798. [14] Hunter LA,Krafft S,Stingo F,et al. High quality machine-robust image features:Identification in nonsmall cell lung cancer computed tomography images[J].Med Phys,2013,40(12):121916. DOI:10.1118/1.4829514. [15] Mackin D,Fave X,Zhang L,et al. Measuring computed tomography scanner variability of radiomics features[J].Invest Radiol,2015,50(11):757-765. DOI:10.1097/RLI.0000000000000180. [16] O′Connor JP,Aboagye EO,Adams JE,et al. Imaging biomarker roadmap for cancer studies[J].Nat Rev Clin Oncol,2016,14(3):169-186.DOI:10.1038/nrclinonc.2016.162. [17] Shafiqul-Hassan M,Zhang GG,Latifi K,et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels[J].Med Phys,2017,44(3):1050-1062.DOI:10.1002/mp.12123. [18] Zhao B,Tan Y,Tsai WY,et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging[J].Scient Rep,2016,6(1):23428.DOI:10.1038/srep23428. [19] Lu L,Ehmke RC,Schwartz LH,et al. Assessing agreement between radiomic features computed for multiple CT imaging settings[J/OL].PloS one,2016,11(12):e0166550. DOI:10.1371/journal.pone.0166550. [20] Parmar C,Rios VE,Leijenaar R,et al. Robust Radiomics feature quantification using semiautomatic volumetric segmentation[J/OL].Plos One,2014,9(7):e102107. DOI:10.1371/journal.pone.0102107. [21] Echegaray S,Gevaert O,Shah R,et al. Core samples for radiomics features that are insensitive to tumor segmentation:method and pilot study using CT images of hepatocellular carcinoma[J].J Med Imag,2015,2(4):041011.DOI:10.1117/1.JMI.2.4.041011. [22] Fave X,Zhang L,Yang J,et al. Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer[J].Transl Cancer Res,2016,5(4):349-363.DOI:10.21037/tcr.2016.07.11. [23] Hu P,Wang J,Zhong H,et al. Reproducibility with repeat CT in radiomics study for rectal cancer[J].Oncotarget,2016,7(44):71440-71446.DOI:10.18632/oncotarget.12199. [24] Doumou G,Siddique M,Tsoumpas C,et al. The precision of textural analysis in 18F-FDG-PET scans of oesophageal cancer[J].Eur Radiol,2015,25(9):2805-2812.DOI:10.1007/s00330-015-3681-8. [25] Soh LK,Tsatsoulis C.Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices[J].IEEE Transact Geosci Remote Sensing,1999,37(2):780-795.DOI:10.1109/36.752194. [26] Fave X,Mackin D,Yang J,et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?[J].Med Phys,2015,42(12):6784-6797.DOI:10.1118/1.4934826. [27] Cui Y,Tha KK,Terasaka S,et al. Prognostic imaging biomarkers in glioblastoma:development and independent validation on the basis of multiregion and quantitative analysis of MR images[J].Radiology,2015,278(2):546-553. DOI:10.1148/radiol.2015150358. [28] Parmar C,Grossmann P,Bussink J,et al. Machine learning methods for quantitative radiomic biomarkers[J].Scient Rep,2015,5(1):13087.DOI:10.1038/srep13087. [29] Liu J,Mao Y,Li Z,et al. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma[J].J Mag Res Imag,2016,44(2):445-455.DOI:10.1002/jmri.25156. [30] Coroller TP,Grossmann P,Hou Y,et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma[J].Radiother Oncol,2015,114(3):345-350. DOI:10.1016/j.radonc.2015.02.015.
2018, Vol. 27 
