Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Radiomics and Deep Learning: Hepatic Applications

  • Hyo Jung Park (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Bumwoo Park (Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center) ;
  • Seung Soo Lee (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • Received : 2019.10.09
  • Accepted : 2020.01.05
  • Published : 2020.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

Keywords

Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), which is funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1A2B4003114).

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. Lee G, Lee HY, Park H, Schiebler ML, van Beek EJR, Ohno Y, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: state of the art. Eur J Radiol 2017;86:297-307 https://doi.org/10.1016/j.ejrad.2016.09.005
  3. Legland D, Kieu K, Devaux MF. Computation of Minkowski measures on 2D and 3D binary images. Image Anal Stereol 2007;26:83-92 https://doi.org/10.5566/ias.v26.p83-92
  4. Zwanenburg A, Leger S, Vallieres M, Lock S. Image biomarker standardisation initiative [updated May 2019]. arXiv:1612.07003 [cs.CV], 2016. Available at: https://arxiv.org/abs/1612.07003v9. Accessed August 31, 2019
  5. Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 2016;1:207-226 https://doi.org/10.1080/23808993.2016.1164013
  6. Park HJ, Lee SS, Park B, Yun J, Sung YS, Shim WH, et al. Radiomics analysis of gadoxetic acid-enhanced MRI for staging liver fibrosis. Radiology 2019;290:380-387 https://doi.org/10.1148/radiol.2018181197
  7. Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol 2017;90:20160642
  8. Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, et al. Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 2019;68:729-741
  9. Chen S, Feng S, Wei J, Liu F, Li B, Li X, et al. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol 2019;29:4177-4187 https://doi.org/10.1007/s00330-018-5986-x
  10. Feng ST, Jia Y, Liao B, Huang B, Zhou Q, Li X, et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol 2019;29:4648-4659 https://doi.org/10.1007/s00330-018-5935-8
  11. Ji GW, Zhu FP, Zhang YD, Liu XS, Wu FY, Wang K, et al. A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma. Eur Radiol 2019;29:3725-3735
  12. Liu F, Ning Z, Liu Y, Liu D, Tian J, Luo H, et al. Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study. EBioMedicine 2018;36:151-158 https://doi.org/10.1016/j.ebiom.2018.09.023
  13. Zheng BH, Liu LZ, Zhang ZZ, Shi JY, Dong LQ, Tian LY, et al. Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer 2018;18:1148
  14. Szczypinski PM, Strzelecki M, Materka A, Klepaczko A. MaZda--a software package for image texture analysis. Comput Methods Programs Biomed 2009;94:66-76 https://doi.org/10.1016/j.cmpb.2008.08.005
  15. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104-e107 https://doi.org/10.1158/0008-5472.CAN-17-0339
  16. 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
  17. Ji GW, Zhang YD, Zhang H, Zhu FP, Wang K, Xia YX, et al. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology 2019;290:90-98 https://doi.org/10.1148/radiol.2018181408
  18. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012;30:1234-1248 https://doi.org/10.1016/j.mri.2012.06.010
  19. Cozzi L, Dinapoli N, Fogliata A, Hsu WC, Reggiori G, Lobefalo F, et al. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer 2017;17:829
  20. Guo D, Gu D, Wang H, Wei J, Wang Z, Hao X, et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol 2019;117:33-40 https://doi.org/10.1016/j.ejrad.2019.05.010
  21. Li W, Huang Y, Zhuang BW, Liu GJ, Hu HT, Li X, et al. Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol 2019;29:1496-1506 https://doi.org/10.1007/s00330-018-5680-z
  22. Kim S, Shin J, Kim DY, Choi GH, Kim MJ, Choi JY. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma. Clin Cancer Res 2019;25:3847-3855 https://doi.org/10.1158/1078-0432.CCR-18-2861
  23. Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 2015;5:272
  24. Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 2019;70:1133-1144 https://doi.org/10.1016/j.jhep.2019.02.023
  25. Zhou Y, He L, Huang Y, Chen S, Wu P, Ye W, et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY) 2017;42:1695-1704 https://doi.org/10.1007/s00261-017-1072-0
  26. Hu HT, Wang Z, Huang XW, Chen SL, Zheng X, Ruan SM, et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol 2019;29:2890-2901 https://doi.org/10.1007/s00330-018-5797-0
  27. Ogutu JO, Schulz-Streeck T, Piepho HP. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc 2012;6 Suppl 2:S10
  28. Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012;16:933-951 https://doi.org/10.1016/j.media.2012.02.005
  29. Lubner MG, Malecki K, Kloke J, Ganeshan B, Pickhardt PJ. Texture analysis of the liver at MDCT for assessing hepatic fibrosis. Abdom Radiol (NY) 2017;42:2069-2078 https://doi.org/10.1007/s00261-017-1096-5
  30. Naganawa S, Enooku K, Tateishi R, Akai H, Yasaka K, Shibahara J, et al. Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol 2018;28:3050-3058 https://doi.org/10.1007/s00330-017-5270-5
  31. Shan QY, Hu HT, Feng ST, Peng ZP, Chen SL, Zhou Q, et al. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging 2019;19:11
  32. Yuan C, Wang Z, Gu D, Tian J, Zhao P, Wei J, et al. Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a radiomics nomogram. Cancer Imaging 2019;19:21
  33. Akai H, Yasaka K, Kunimatsu A, Nojima M, Kokudo T, Kokudo N, et al. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn Interv Imaging 2018;99:643-651 https://doi.org/10.1016/j.diii.2018.05.008
  34. Iwatsuki S, Dvorchik I, Marsh JW, Madariaga JR, Carr B, Fung JJ, et al. Liver transplantation for hepatocellular carcinoma: a proposal of a prognostic scoring system. J Am Coll Surg 2000;191:389-394 https://doi.org/10.1016/S1072-7515(00)00688-8
  35. Lim KC, Chow PK, Allen JC, Chia GS, Lim M, Cheow PC, et al. Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Ann Surg 2011;254:108-113 https://doi.org/10.1097/SLA.0b013e31821ad884
  36. Iguchi T, Shirabe K, Aishima S, Wang H, Fujita N, Ninomiya M, et al. New pathologic stratification of microvascular invasion in hepatocellular carcinoma: predicting prognosis after living-donor liver transplantation. Transplantation 2015;99:1236-1242 https://doi.org/10.1097/TP.0000000000000489
  37. Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol 2018;24:121-127 https://doi.org/10.5152/dir.2018.17467
  38. 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
  39. Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019;20:1124-1137 https://doi.org/10.3348/kjr.2018.0070
  40. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology 2019;291:53-59 https://doi.org/10.1148/radiol.2019182023
  41. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics 2017;37:2113-2131 https://doi.org/10.1148/rg.2017170077
  42. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean J Radiol 2017;18:570-584 https://doi.org/10.3348/kjr.2017.18.4.570
  43. Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019;25:672-682 https://doi.org/10.3748/wjg.v25.i6.672
  44. Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al. Current applications and future impact of machine learning in radiology. Radiology 2018;288:318-328 https://doi.org/10.1148/radiol.2018171820
  45. Choi KJ, Jang JK, Lee SS, Sung YS, Shim WH, Kim HS, et al. Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. Radiology 2018;289:688-697 https://doi.org/10.1148/radiol.2018180763
  46. Iranmanesh P, Vazquez O, Terraz S, Majno P, Spahr L, Poncet A, et al. Accurate computed tomography-based portal pressure assessment in patients with hepatocellular carcinoma. J Hepatol 2014;60:969-974 https://doi.org/10.1016/j.jhep.2013.12.015
  47. Nakayama Y, Li Q, Katsuragawa S, Ikeda R, Hiai Y, Awai K, et al. Automated hepatic volumetry for living related liver transplantation at multisection CT. Radiology 2006;240:743-748 https://doi.org/10.1148/radiol.2403050850
  48. Wang K, Mamidipalli A, Retson T, Bahrami N, Hasenstab K, Blansit K, et al. Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Radiology: Artificial Intelligence 2019 Mar 27 [Epub]. https://doi.org/10.1148/ryai.2019180022
  49. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. arXiv:1505.04597 [cs.CV], 2015. Available at: https://arxiv.org/abs/1505.04597. Accessed August 31, 2019
  50. Hu P, Wu F, Peng J, Liang P, Kong D. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol 2016;61:8676-8698 https://doi.org/10.1088/1361-6560/61/24/8676
  51. Huo Y, Terry JG, Wang J, Nair S, Lasko TA, Freedman BI, et al. Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations. Med Phys 2019;46:3508-3519 https://doi.org/10.1002/mp.13675
  52. Lu F, Wu F, Hu P, Peng Z, Kong D. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 2017;12:171-182 https://doi.org/10.1007/s11548-016-1467-3
  53. van Gastel MDA, Edwards ME, Torres VE, Erickson BJ, Gansevoort RT, Kline TL. Automatic measurement of kidney and liver volumes from MR images of patients affected by autosomal dominant polycystic kidney disease. J Am Soc Nephrol 2019;30:1514-1522 https://doi.org/10.1681/ASN.2018090902
  54. Wang CJ, Hamm CA, Savic LJ, Ferrante M, Schobert I, Schlachter T, et al. Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. Eur Radiol 2019;29:3348-3357 https://doi.org/10.1007/s00330-019-06214-8
  55. Liu X, Song JL, Wang SH, Zhao JW, Chen YQ. Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification. Sensors (Basel) 2017;17. pii: E149
  56. Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol 2018;28:4578-4585 https://doi.org/10.1007/s00330-018-5499-7
  57. Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR images. Radiology 2018;287:146-155 https://doi.org/10.1148/radiol.2017171928
  58. Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, Marinhoe RT, et al. Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed 2018;155:165-177 https://doi.org/10.1016/j.cmpb.2017.12.016
  59. Byra M, Styczynski G, Szmigielski C, Kalinowski P, Michalowski L, Paluszkiewicz R, et al. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 2018;13:1895-1903 https://doi.org/10.1007/s11548-018-1843-2
  60. Cao W, An X, Cong L, Lyu C, Zhou Q, Guo R. Application of deep learning in quantitative analysis of 2-dimensional ultrasound imaging of nonalcoholic fatty liver disease. J Ultrasound Med 2020;39:51-59 https://doi.org/10.1002/jum.15070
  61. Vorontsov E, Cerny M, Regnier P, Di Jorio L, Pal CJ, Lapointe R, et al. Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases. Radiology: Artificial Intelligence 2019 Mar 13 [Epub]. https://doi.org/10.1148/ryai.2019180014
  62. Schmauch B, Herent P, Jehanno P, Dehaene O, Saillard C, Aube C, et al. Diagnosis of focal liver lesions from ultrasound using deep learning. Diagn Interv Imaging 2019;100:227-233 https://doi.org/10.1016/j.diii.2019.02.009
  63. Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 2018;286:887-896 https://doi.org/10.1148/radiol.2017170706
  64. Hamm CA, Wang CJ, Savic LJ, Ferrante M, Schobert I, Schlachter T, et al. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 2019;29:3338-3347 https://doi.org/10.1007/s00330-019-06205-9
  65. Ma J, Dercle L, Lichtenstein P, Wang D, Chen A, Zhu J, et al. Automated identification of optimal portal venous phase timing with convolutional neural networks. Acad Radiol 2020;27:e10-e18 https://doi.org/10.1016/j.acra.2019.02.024
  66. Esses SJ, Lu X, Zhao T, Shanbhogue K, Dane B, Bruno M, et al. Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture. J Magn Reson Imaging 2018;47:723-728 https://doi.org/10.1002/jmri.25779
  67. Liu F, Samsonov A, Chen L, Kijowski R, Feng L. SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction. Magn Reson Med 2019;82:1890-1904 https://doi.org/10.1002/mrm.27827
  68. Tamada D, Kromrey ML, Ichikawa S, Onishi H, Motosugi U. Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver. Magn Reson Med Sci 2020;19:64-76 https://doi.org/10.2463/mrms.mp.2018-0156
  69. Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res 2018;24:1248-1259 https://doi.org/10.1158/1078-0432.CCR-17-0853
  70. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800-809 https://doi.org/10.1148/radiol.2017171920
  71. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162:W1-W73 https://doi.org/10.7326/M14-0698
  72. England JR, Cheng PM. Artificial intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol 2019;212:513-519 https://doi.org/10.2214/AJR.18.20490
  73. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). Ann Intern Med 2015;162:735-736 https://doi.org/10.7326/L15-5093
  74. Han K, Song K, Choi BW. How to develop, validate, and compare clinical prediction models involving radiological parameters: study design and statistical methods. Korean J Radiol 2016;17:339-350 https://doi.org/10.3348/kjr.2016.17.3.339
  75. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019;20:405-410 https://doi.org/10.3348/kjr.2019.0025