Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

  • Park, Sohee (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Lee, Sang Min (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Do, Kyung-Hyun (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Lee, June-Goo (Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Bae, Woong (VUNO Inc.) ;
  • Park, Hyunho (VUNO Inc.) ;
  • Jung, Kyu-Hwan (VUNO Inc.) ;
  • Seo, Joon Beom (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center)
  • Received : 2019.03.20
  • Accepted : 2019.07.12
  • Published : 2019.10.01
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Abstract

Objective: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve the reproducibility of RFs obtained from images with different slice thicknesses. Materials and Methods: CT images with 1-, 3-, and 5-mm slice thicknesses obtained from 100 pathologically proven lung cancers between July 2017 and December 2017 were evaluated. CNN-based SR algorithms using residual learning were developed to convert thick-slice images into 1-mm slices. Lung cancers were semi-automatically segmented and a total of 702 RFs (tumor intensity, texture, and wavelet features) were extracted from 1-, 3-, and 5-mm slices, as well as the 1-mm slices generated from the 3- and 5-mm images. The stabilities of the RFs were evaluated using concordance correlation coefficients (CCCs). Results: The mean CCCs for the comparisons of original 1 mm vs. 3 mm, 1 mm vs. 5 mm, and 3 mm vs. 5 mm images were 0.41, 0.27, and 0.65, respectively (p < 0.001 for all comparisons). Tumor intensity features showed the best reproducibility while wavelets showed the lowest reproducibility. The majority of RFs failed to achieve reproducibility (CCC ≥ 0.85; 3.6%, 1.0%, and 21.5%, respectively). After applying the CNN-based SR algorithms, the reproducibility significantly improved in all three pairings (mean CCCs: 0.58, 0.45, and 0.72; p < 0.001 for all comparisons). The reproducible RFs also increased (36.3%, 17.4%, and 36.9%, respectively). Conclusion: The reproducibility of RFs in lung cancer is significantly influenced by CT slice thickness, which can be improved by the CNN-based SR algorithms.

Keywords

Acknowledgement

This study received funding from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant number: NRF-2016R1A2B1016355) and the Korea Health technology R&D Project, Ministry for Health & Welfare Affairs, Republic of Korea (Grant number: HI18C0673).

References

  1. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563-577 https://doi.org/10.1148/radiol.2015151169
  2. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006 https://doi.org/10.1038/ncomms5006
  3. Zhang B, Tian J, Dong D, Gu D, Dong Y, Zhang L, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 2017;23:4259-4269 https://doi.org/10.1158/1078-0432.CCR-16-2910
  4. Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 2016;281:947-957 https://doi.org/10.1148/radiol.2016152234
  5. Berenguer R, Pastor-Juan MDR, Canales-Vazquez J, Castro-Garcia M, Villas MV, Mansilla Legorburo F, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 2018;288:407-415 https://doi.org/10.1148/radiol.2018172361
  6. He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep 2016;6:34921 https://doi.org/10.1038/srep34921
  7. Kim J, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR);2016 June 27-30:1646-1654;Las Vegas, NV, USA
  8. Bhavsar A, Wu G, Lian J, Shen D. Resolution enhancement of lung 4D-CT via group-sparsity. Med Phys 2013;40:121717 https://doi.org/10.1118/1.4829501
  9. Rueda A, Malpica N, Romero E. Single-image super-resolution of brain MR images using overcomplete dictionaries. Med Image Anal 2013;17:113-132 https://doi.org/10.1016/j.media.2012.09.003
  10. Coupe P, Manjon JV, Chamberland M, Descoteaux M, Hiba B. Collaborative patch-based super-resolution for diffusion-weighted images. Neuroimage 2013;83:245-261 https://doi.org/10.1016/j.neuroimage.2013.06.030
  11. Bahrami K, Shi F, Rekik I, Gao Y, Shen D. 7T-guided superresolution of 3T MRI. Med Phys 2017;44:1661-1677 https://doi.org/10.1002/mp.12132
  12. Kim C, Lee SM, Choe J, Chae EJ, Do KH, Seo JB. Volume doubling time of lung cancer detected in idiopathic interstitial pneumonia: comparison with that in chronic obstructive pulmonary disease. Eur Radiol 2018;28:1402-1409 https://doi.org/10.1007/s00330-017-5091-6
  13. 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 https://doi.org/10.1371/journal.pone.0102107
  14. Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989;45:255-268 https://doi.org/10.2307/2532051
  15. 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 https://doi.org/10.1038/srep23428
  16. Larue RTHM, van Timmeren JE, de Jong EEC, Feliciani G, Leijenaar RTH, Schreurs WMJ, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol 2017;56:1544-1553 https://doi.org/10.1080/0284186X.2017.1351624
  17. Lu L, Ehmke RC, Schwartz LH, Zhao B. Assessing agreement between radiomic features computed for multiple CT imaging settings. PLoS One 2016;11:e0166550 https://doi.org/10.1371/journal.pone.0166550
  18. Bankier AA, MacMahon H, Goo JM, Rubin GD, Schaefer-Prokop CM, Naidich DP. Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology 2017;285:584-600 https://doi.org/10.1148/radiol.2017162894

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