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Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease

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Abstract

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.

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References

  1. WHO | Projections of mortality and causes of death, 2016 to 2060. World Health Organization. World Health Organization; 2016.

  2. D’Amico G, Garcia-Tsao G, Pagliaro L. Natural history and prognostic indicators of survival in cirrhosis: a systematic review of 118 studies. J Hepatol. 2006;44:217–231

    Article  PubMed  Google Scholar 

  3. Hu K-Q, Tong MJ. The long-term outcomes of patients with compensated hepatitis C virus–related cirrhosis and history of parenteral exposure in the united states. Hepatology. 1999;29:1311–1316

    Article  CAS  PubMed  Google Scholar 

  4. Benvegnù L, Gios M, Boccato S, Alberti A. Natural history of compensated viral cirrhosis: a prospective study on the incidence and hierarchy of major complications. Gut. 2004;53:744–749

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jepsen P, Ott P, Andersen PK, Sørensen HT, Vilstrup H. Clinical course of alcoholic liver cirrhosis: a Danish population-based cohort study. Hepatology. 2010;51:1675–1682

    Article  PubMed  Google Scholar 

  6. Regev A, Berho M, Jeffers LJ, Milikowski C, Molina EG, Pyrsopoulos NT, et al. Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection. Am J Gastroenterol. 2002;97:2614–2618

    Article  PubMed  Google Scholar 

  7. Merriman RB, Ferrell LD, Patti MG, Weston SR, Pabst MS, Aouizerat BE, et al. Correlation of paired liver biopsies in morbidly obese patients with suspected nonalcoholic fatty liver disease. Hepatology. 2006;44:874–880

    Article  PubMed  Google Scholar 

  8. Dana J, Agnus V, Ouhmich F, Gallix B. Multimodality imaging and artificial intelligence for tumor characterization: current status and future perspective. Semin Nucl Med. 2020. http://www.sciencedirect.com/science/article/pii/S000129982030074X. Accessed 2 Aug 2020.

  9. Aerts HJWL. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2:1636–1642

    Article  PubMed  Google Scholar 

  10. López SA, Manzano ML, Gea F, Gutiérrez ML, Ahumada AM, Devesa MJ, et al. A model based on noninvasive markers predicts very low hepatocellular carcinoma risk after viral response in hepatitis C virus-advanced fibrosis. Hepatology. 2020;72:1924–1934

    Article  CAS  Google Scholar 

  11. Rasmussen DN, Thiele M, Johansen S, Kjærgaard M, Lindvig KP, Israelsen M, et al. Prognostic performance of seven biomarkers compared to liver biopsy in early alcohol-related liver disease. J Hepatol. 2021. https://www.sciencedirect.com/science/article/pii/S0168827821004116. Accessed 14 Jul 2021.

  12. Hoshida Y, Villanueva A, Sangiovanni A, Sole M, Hur C, Andersson KL, et al. Prognostic gene expression signature for patients with hepatitis C-related early-stage cirrhosis. Gastroenterology. 2013;144:1024–1030

    Article  CAS  PubMed  Google Scholar 

  13. 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

    Article  PubMed  Google Scholar 

  14. Savadjiev P, Chong J, Dohan A, Vakalopoulou M, Reinhold C, Paragios N, et al. Demystification of AI-driven medical image interpretation: past, present and future. Eur Radiol. 2019;29:1616–1624

    Article  PubMed  Google Scholar 

  15. Savadjiev P, Chong J, Dohan A, Agnus V, Forghani R, Reinhold C, et al. Image-based biomarkers for solid tumor quantification. Eur Radiol. 2019;29:5431–5440

    Article  PubMed  Google Scholar 

  16. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–338

    Article  PubMed  Google Scholar 

  17. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444

    Article  CAS  PubMed  Google Scholar 

  18. Cheng PM, Montagnon E, Yamashita R, Pan I, Cadrin-Chênevert A, Perdigón Romero F, et al. Deep learning: an update for radiologists. Radiographics. 2021;41:1427–1445

    Article  PubMed  Google Scholar 

  19. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–762

    Article  PubMed  Google Scholar 

  20. Lecointre L, Dana J, Lodi M, Akladios C, Gallix B. Artificial intelligence-based radiomics models in endometrial cancer: a systematic review. Eur J Surg Oncol. 2021;47:2734–2741

    Article  PubMed  Google Scholar 

  21. Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020;2:e200029

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hinton G. Deep learning-a technology with the potential to transform health care. JAMA. 2018;320:1101–1102

    Article  PubMed  Google Scholar 

  23. World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. 2021.

  24. Hernandez-Gea V, Friedman SL. Pathogenesis of liver fibrosis. Annu Rev Pathol. 2011;6:425–456

    Article  CAS  PubMed  Google Scholar 

  25. Vilgrain V, Lagadec M, Ronot M. Pitfalls in liver imaging. Radiology. 2015;278:34–51

    Article  Google Scholar 

  26. Smith AD, Branch CR, Zand K, Subramony C, Zhang H, Thaggard K, et al. Liver surface nodularity quantification from routine CT images as a biomarker for detection and evaluation of cirrhosis. Radiology. 2016;280:771–781

    Article  PubMed  Google Scholar 

  27. Sartoris R, Rautou P-E, Elkrief L, Pollorsi G, Durand F, Valla D, et al. Quantification of liver surface nodularity at CT: utility for detection of portal hypertension. Radiology. 2018;289:698–707

    Article  PubMed  Google Scholar 

  28. Hobeika C, Cauchy F, Sartoris R, Beaufrère A, Yoh T, Vilgrain V, et al. Relevance of liver surface nodularity for preoperative risk assessment in patients with resectable hepatocellular carcinoma. Br J Surg. 2020;107:878–888

    Article  CAS  PubMed  Google Scholar 

  29. Bastati N, Feier D, Wibmer A, Traussnigg S, Balassy C, Tamandl D, et al. Noninvasive differentiation of simple steatosis and steatohepatitis by using gadoxetic acid-enhanced MR imaging in patients with nonalcoholic fatty liver disease: a proof-of-concept study. Radiology. 2014;271:739–747

    Article  PubMed  Google Scholar 

  30. Xiao G, Zhu S, Xiao X, Yan L, Yang J, Wu G. Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: a meta-analysis. Hepatology. 2017;66:1486–1501

    Article  CAS  PubMed  Google Scholar 

  31. Calvopina DA, Noble C, Weis A, Hartel GF, Ramm LE, Balouch F, et al. Supersonic shear-wave elastography and APRI for the detection and staging of liver disease in pediatric cystic fibrosis. J Cyst Fibros. 2019;19:449–454

    Article  PubMed  Google Scholar 

  32. Lewindon PJ, Puertolas-Lopez MV, Ramm LE, Noble C, Pereira TN, Wixey JA, et al. Accuracy of transient elastography data combined with APRI in detection and staging of liver disease in pediatric patients with cystic fibrosis. Clin Gastroenterol Hepatol. 2019;17:2561-2569.e5

    Article  PubMed  Google Scholar 

  33. Berzigotti A, Tsochatzis E, Boursier J, Castera L, Cazzagon N, Friedrich-Rust M, et al. EASL clinical practice guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. J Hepatol. 2021. https://www.sciencedirect.com/science/article/pii/S0168827821003986. Accessed 22 Jun 2021.

  34. Tang A, Cloutier G, Szeverenyi NM, Sirlin CB. Ultrasound elastography and MR elastography for assessing liver fibrosis: part 1, principles and techniques. Am J Roentgenol. 2015;205:22–32

    Article  Google Scholar 

  35. Tang A, Cloutier G, Szeverenyi NM, Sirlin CB. Ultrasound elastography and MR elastography for assessing liver fibrosis: part 2, diagnostic performance, confounders, and future directions. Am J Roentgenol. 2015;205:33–40

    Article  Google Scholar 

  36. Foucher J, Chanteloup E, Vergniol J, Castéra L, Le Bail B, Adhoute X, et al. Diagnosis of cirrhosis by transient elastography (FibroScan): a prospective study. Gut. 2006;55:403–408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Poynard T, Vergniol J, Ngo Y, Foucher J, Munteanu M, Merrouche W, et al. Staging chronic hepatitis C in seven categories using fibrosis biomarker (FibroTestTM) and transient elastography (FibroScan®). J Hepatol. 2014;60:706–714

    Article  PubMed  Google Scholar 

  38. Rajakannu M, Coilly A, Adam R, Samuel D, Vibert E. Prospective validation of transient elastography for staging liver fibrosis in patients undergoing hepatectomy and liver transplantation. J Hepatol. 2018;68:199–200

    Article  Google Scholar 

  39. Castéra L, Vergniol J, Foucher J, Le Bail B, Chanteloup E, Haaser M, et al. Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology. 2005;128:343–350

    Article  PubMed  Google Scholar 

  40. Ziol M, Handra-Luca A, Kettaneh A, Christidis C, Mal F, Kazemi F, et al. Noninvasive assessment of liver fibrosis by measurement of stiffness in patients with chronic hepatitis C. Hepatology. 2005;41:48–54

    Article  PubMed  Google Scholar 

  41. Ganne-Carrié N, Ziol M, de Ledinghen V, Douvin C, Marcellin P, Castera L, et al. Accuracy of liver stiffness measurement for the diagnosis of cirrhosis in patients with chronic liver diseases. Hepatology. 2006;44:1511–1517

    Article  PubMed  Google Scholar 

  42. de Lédinghen V, Douvin C, Kettaneh A, Ziol M, Roulot D, Marcellin P, et al. Diagnosis of hepatic fibrosis and cirrhosis by transient elastography in HIV/hepatitis C virus-coinfected patients. J Acquir Immune Defic Syndr. 2006;41:175–179

    Article  PubMed  Google Scholar 

  43. Castera L, Forns X, Alberti A. Non-invasive evaluation of liver fibrosis using transient elastography. J Hepatol. 2008;48:835–847

    Article  PubMed  Google Scholar 

  44. Cassinotto C, Boursier J, Paisant A, Guiu B, Irles‐Depe M, Canivet C, et al. Transient versus 2-dimensional shear-wave elastography in a multistep strategy to detect advanced fibrosis in NAFLD. Hepatology. http://aasldpubs.onlinelibrary.wiley.com/doi/abs/10.1002/hep.31655. Accessed 14 May 2021.

  45. Gao Y, Zheng J, Liang P, Tong M, Wang J, Wu C, et al. Liver fibrosis with two-dimensional US shear-wave elastography in participants with chronic hepatitis B: a prospective multicenter study. Radiology. 2018;289:407–415

    Article  PubMed  Google Scholar 

  46. Yoneda M, Thomas E, Sclair SN, Grant TT, Schiff ER. Supersonic shear imaging and transient elastography with the XL probe accurately detect fibrosis in overweight or obese patients with chronic liver disease. Clin Gastroenterol Hepatol. 2015;13:1502-1509.e5

    Article  PubMed  Google Scholar 

  47. Leung VY, Shen J, Wong VW, Abrigo J, Wong GL, Chim AM, et al. Quantitative elastography of liver fibrosis and spleen stiffness in chronic hepatitis B carriers: comparison of shear-wave elastography and transient elastography with liver biopsy correlation. Radiology. 2013;269:910–918

    Article  PubMed  Google Scholar 

  48. Friedrich-Rust M, Lupsor M, de Knegt R, Dries V, Buggisch P, Gebel M, et al. Point shear wave elastography by acoustic radiation force impulse quantification in comparison to transient elastography for the noninvasive assessment of liver fibrosis in chronic hepatitis C: a prospective international multicenter study. Ultraschall Med. 2015;36:239–247

    Article  CAS  PubMed  Google Scholar 

  49. Ferraioli G, Tinelli C, Bello BD, Zicchetti M, Filice G, Filice C. Accuracy of real-time shear wave elastography for assessing liver fibrosis in chronic hepatitis C: a pilot study. Hepatology. 2012;56:2125–2133

    Article  PubMed  Google Scholar 

  50. Zhuang Y, Ding H, Zhang Y, Sun H, Xu C, Wang W. Two-dimensional shear-wave elastography performance in the noninvasive evaluation of liver fibrosis in patients with chronic hepatitis B: comparison with serum fibrosis indexes. Radiology. 2016;283:873–882

    Article  PubMed  Google Scholar 

  51. Zheng J, Guo H, Zeng J, Huang Z, Zheng B, Ren J, et al. Two-dimensional shear-wave elastography and conventional US: the optimal evaluation of liver fibrosis and cirrhosis. Radiology. 2015;275:290–300

    Article  PubMed  Google Scholar 

  52. Ferraioli G, Tinelli C, Zicchetti M, Above E, Poma G, Di Gregorio M, et al. Reproducibility of real-time shear wave elastography in the evaluation of liver elasticity. Eur J Radiol. 2012;81:3102–3106

    Article  PubMed  Google Scholar 

  53. Lefebvre T, Wartelle-Bladou C, Wong P, Sebastiani G, Giard J-M, Castel H, et al. Prospective comparison of transient, point shear wave, and magnetic resonance elastography for staging liver fibrosis. Eur Radiol. 2019;29:6477–6488

    Article  PubMed  Google Scholar 

  54. Asbach P, Klatt D, Hamhaber U, Braun J, Somasundaram R, Hamm B, et al. Assessment of liver viscoelasticity using multifrequency MR elastography. Magn Reson Med. 2008;60:373–379

    Article  PubMed  Google Scholar 

  55. Dyvorne HA, Jajamovich GH, Bane O, Fiel MI, Chou H, Schiano TD, et al. Prospective comparison of magnetic resonance imaging to transient elastography and serum markers for liver fibrosis detection. Liver Int. 2016;36:659–666

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Chen J, Yin M, Talwalkar JA, Oudry J, Glaser KJ, Smyrk TC, et al. Diagnostic performance of MR elastography and vibration-controlled transient elastography in the detection of hepatic fibrosis in patients with severe to morbid obesity. Radiology. 2016;283:418–428

    Article  PubMed  Google Scholar 

  57. Imajo K, Kessoku T, Honda Y, Tomeno W, Ogawa Y, Mawatari H, et al. Magnetic resonance imaging more accurately classifies steatosis and fibrosis in patients with nonalcoholic fatty liver disease than transient elastography. Gastroenterology. 2016;150:626-637.e7

    Article  PubMed  Google Scholar 

  58. Loomba R, Wolfson T, Ang B, Hooker J, Behling C, Peterson M, et al. Magnetic resonance elastography predicts advanced fibrosis in patients with nonalcoholic fatty liver disease: a prospective study. Hepatology. 2014;60:1920–1928

    Article  CAS  PubMed  Google Scholar 

  59. Shi Y, Guo Q, Xia F, Dzyubak B, Glaser KJ, Li Q, et al. MR elastography for the assessment of hepatic fibrosis in patients with chronic hepatitis B infection: does histologic necroinflammation influence the measurement of hepatic stiffness? Radiology. 2014;273:88–98

    Article  PubMed  Google Scholar 

  60. Chang W, Lee JM, Yoon JH, Han JK, Choi BI, Yoon JH, et al. Liver fibrosis staging with MR elastography: comparison of diagnostic performance between patients with chronic hepatitis B and those with other etiologic causes. Radiology. 2016;280:88–97

    Article  PubMed  Google Scholar 

  61. Cui J, Heba E, Hernandez C, Haufe W, Hooker J, Andre MP, et al. Magnetic resonance elastography is superior to acoustic radiation force impulse for the diagnosis of fibrosis in patients with biopsy-proven nonalcoholic fatty liver disease: a prospective study. Hepatology. 2016;63:453–461

    Article  PubMed  Google Scholar 

  62. Yin M, Talwalkar JA, Glaser KJ, Manduca A, Grimm RC, Rossman PJ, et al. Assessment of hepatic fibrosis with magnetic resonance elastography. Clin Gastroenterol Hepatol. 2007;5:1207-1213.e2

    Article  PubMed  PubMed Central  Google Scholar 

  63. Huwart L, Sempoux C, Vicaut E, Salameh N, Annet L, Danse E, et al. Magnetic resonance elastography for the noninvasive staging of liver fibrosis. Gastroenterology. 2008;135:32–40

    Article  PubMed  Google Scholar 

  64. Venkatesh SK, Yin M, Ehman RL. Magnetic resonance elastography of liver: technique, analysis, and clinical applications. J Magn Reson Imaging. 2013;37:544–555

    Article  PubMed  PubMed Central  Google Scholar 

  65. Lee JH, Joo I, Kang TW, Paik YH, Sinn DH, Ha SY, et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol. 2020;30:1264–1273

    Article  PubMed  Google Scholar 

  66. 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

    Article  CAS  PubMed  Google Scholar 

  67. Xue L-Y, Jiang Z-Y, Fu T-T, Wang Q-M, Zhu Y-L, Dai M, et al. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis. Eur Radiol. 2020;30:2973–2983

    Article  PubMed  PubMed Central  Google Scholar 

  68. He L, Li H, Dudley JA, Maloney TC, Brady SL, Somasundaram E, et al. Machine learning prediction of liver stiffness using clinical and T2-weighted MRI radiomic data. AJR Am J Roentgenol. 2019;213:592–601

    Article  PubMed  Google Scholar 

  69. Hectors SJ, Kennedy P, Huang K-H, Stocker D, Carbonell G, Greenspan H, et al. Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI. Eur Radiol. 2020;31:3805–3814

    Article  CAS  PubMed  Google Scholar 

  70. 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

    Article  PubMed  Google Scholar 

  71. 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

    Article  PubMed  Google Scholar 

  72. Younossi ZM. Non-alcoholic fatty liver disease – a global public health perspective. J Hepatol. 2019;70:531–544

    Article  PubMed  Google Scholar 

  73. Nguyen VH, Le MH, Cheung RC, Nguyen MH. Differential clinical characteristics and mortality outcomes in persons with NAFLD and/or MAFLD. Clin Gastroenterol Hepatol. 2021. https://www.sciencedirect.com/science/article/pii/S154235652100567X. Accessed 25 Aug 2021.

  74. Natarajan Y, Kramer JR, Yu X, Li L, Thrift AP, El-Serag HB, et al. Risk of cirrhosis and hepatocellular cancer in patients with NAFLD and normal liver enzymes. Hepatology. 2020;72:1242–1252

    Article  CAS  PubMed  Google Scholar 

  75. Mendes FD, Suzuki A, Sanderson SO, Lindor KD, Angulo P. Prevalence and indicators of portal hypertension in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2012;10:1028-1033.e2

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhang B, Ding F, Chen T, Xia L-H, Qian J, Lv G-Y. Ultrasound hepatic/renal ratio and hepatic attenuation rate for quantifying liver fat content. World J Gastroenterol. 2014;20:17985–17992

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Dioguardi Burgio M, Ronot M, Reizine E, Rautou P-E, Castera L, Paradis V, et al. Quantification of hepatic steatosis with ultrasound: promising role of attenuation imaging coefficient in a biopsy-proven cohort. Eur Radiol. 2020;30:2293–2301

    Article  PubMed  Google Scholar 

  78. Imbault M, Burgio MD, Faccinetto A, Ronot M, Bendjador H, Deffieux T, et al. Ultrasonic fat fraction quantification using in vivo adaptive sound speed estimation. Phys Med Biol. 2018;63:215013

    Article  PubMed  Google Scholar 

  79. Runge JH, Smits LP, Verheij J, Depla A, Kuiken SD, Baak BC, et al. MR spectroscopy–derived proton density fat fraction is superior to controlled attenuation parameter for detecting and grading hepatic steatosis. Radiology. 2017;286:547–556

    Article  PubMed  Google Scholar 

  80. Guo Z, Blake GM, Li K, Liang W, Zhang W, Zhang Y, et al. Liver fat content measurement with quantitative CT validated against MRI proton density fat fraction: a prospective study of 400 healthy volunteers. Radiology. 2020;294:89–97

    Article  PubMed  Google Scholar 

  81. Moret A, Boursier J, Debry PH, Riou J, Crouan A, Dubois M, et al. Evaluation of the hepatorenal B-mode ratio and the “controlled attenuation parameter” for the detection and grading of steatosis. Ultraschall Med. 2020. http://www.thieme.connect.de/DOI/DOI?10.1055/a-1233-2290. Accessed 15 May 2021.

  82. Tang A, Tan J, Sun M, Hamilton G, Bydder M, Wolfson T, et al. Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis. Radiology. 2013;267:422–431

    Article  PubMed  PubMed Central  Google Scholar 

  83. Han A, Byra M, Heba E, Andre MP, Erdman JW, Loomba R, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology. 2020;295:342–350

    Article  PubMed  Google Scholar 

  84. d’Assignies G, Paisant A, Bardou-Jacquet E, Boulic A, Bannier E, Lainé F, et al. Non-invasive measurement of liver iron concentration using 3-Tesla magnetic resonance imaging: validation against biopsy. Eur Radiol. 2018;28:2022–2030

    Article  PubMed  Google Scholar 

  85. Henninger B, Alustiza J, Garbowski M, Gandon Y. Practical guide to quantification of hepatic iron with MRI. Eur Radiol. 2020;30:383–393

    Article  PubMed  Google Scholar 

  86. Czaja AJ. Iron disturbances in chronic liver diseases other than haemochromatosis – pathogenic, prognostic, and therapeutic implications. Aliment Pharmacol Ther. 2019;49:681–701

    Article  PubMed  Google Scholar 

  87. Pietrangelo A. Hereditary hemochromatosis: pathogenesis, diagnosis, and treatment. Gastroenterology. 2010;139:393-408.e2

    Article  PubMed  Google Scholar 

  88. Sugimoto K, Moriyasu F, Oshiro H, Takeuchi H, Abe M, Yoshimasu Y, et al. The role of multiparametric US of the liver for the evaluation of nonalcoholic steatohepatitis. Radiology. 2020;296:532–540

    Article  PubMed  Google Scholar 

  89. Sugimoto K, Moriyasu F, Oshiro H, Takeuchi H, Yoshimasu Y, Kasai Y, et al. Viscoelasticity measurement in rat livers using shear-wave US elastography. Ultrasound Med Biol. 2018;44:2018–2024

    Article  PubMed  Google Scholar 

  90. Deffieux T, Gennisson J-L, Bousquet L, Corouge M, Cosconea S, Amroun D, et al. Investigating liver stiffness and viscosity for fibrosis, steatosis and activity staging using shear wave elastography. J Hepatol. 2015;62:317–324

    Article  PubMed  Google Scholar 

  91. Chen S, Sanchez W, Callstrom MR, Gorman B, Lewis JT, Sanderson SO, et al. Assessment of liver viscoelasticity by using shear waves induced by ultrasound radiation force. Radiology. 2013;266:964–970

    Article  PubMed  PubMed Central  Google Scholar 

  92. Allen AM, Shah VH, Therneau TM, Venkatesh SK, Mounajjed T, Larson JJ, et al. The role of three-dimensional magnetic resonance elastography in the diagnosis of nonalcoholic steatohepatitis in obese patients undergoing bariatric surgery. Hepatology. 2020;71:510–521

    Article  PubMed  Google Scholar 

  93. Kim JW, Lee Y-S, Park YS, Kim B-H, Lee SY, Yeon JE, et al. Multiparametric MR index for the diagnosis of non-alcoholic steatohepatitis in patients with non-alcoholic fatty liver disease. Sci Rep. 2020;10:2671

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Ding Y, Rao S-X, Meng T, Chen C, Li R, Zeng M-S. Usefulness of T1 mapping on Gd-EOB-DTPA-enhanced MR imaging in assessment of non-alcoholic fatty liver disease. Eur Radiol. 2014;24:959–966

    Article  PubMed  Google Scholar 

  95. Sevastianova K, Hakkarainen A, Kotronen A, Cornér A, Arkkila P, Arola J, et al. Nonalcoholic fatty liver disease: detection of elevated nicotinamide adenine dinucleotide phosphate with in vivo 3.0-T 31P MR spectroscopy with proton decoupling. Radiology. 2010;256:466–473

    Article  PubMed  Google Scholar 

  96. Ioannou GN. HCC surveillance after SVR in patients with F3/F4 fibrosis. J Hepatol. 2021;74:458–465

    Article  PubMed  Google Scholar 

  97. Ioannou GN, Green P, Kerr KF, Berry K. Models estimating risk of hepatocellular carcinoma in patients with alcohol or NAFLD-related cirrhosis for risk stratification. J Hepatol. 2019;71:523–533

    Article  PubMed  PubMed Central  Google Scholar 

  98. Ioannou GN, Tang W, Beste LA, Tincopa MA, Su GL, Van T, et al. Assessment of a deep learning model to predict hepatocellular carcinoma in patients with hepatitis C cirrhosis. JAMA Netw Open. 2020;3:e2015626–e2015626

    Article  PubMed  PubMed Central  Google Scholar 

  99. Sharma SA, Kowgier M, Hansen BE, Brouwer WP, Maan R, Wong D, et al. Toronto HCC risk index: a validated scoring system to predict 10-year risk of HCC in patients with cirrhosis. J Hepatol. 2018;68:92–99

    Article  Google Scholar 

  100. Papatheodoridis G, Dalekos G, Sypsa V, Yurdaydin C, Buti M, Goulis J, et al. PAGE-B predicts the risk of developing hepatocellular carcinoma in Caucasians with chronic hepatitis B on 5-year antiviral therapy. J Hepatol. 2016;64:800–806

    Article  CAS  PubMed  Google Scholar 

  101. Fan R, Papatheodoridis G, Sun J, Innes H, Toyoda H, Xie Q, et al. aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis. J Hepatol. 2020;73:1368–1378

    Article  CAS  PubMed  Google Scholar 

  102. Audureau E, Carrat F, Layese R, Cagnot C, Asselah T, Guyader D, et al. Personalized surveillance for hepatocellular carcinoma in cirrhosis – using machine learning adapted to HCV status. J Hepatology. 2020. https://www.journal-of-hepatology.eu/article/S0168-8278(20)30394-9/abstract. Accessed 30 Jun 2020.

  103. Kitamura S, Iishi H, Tatsuta M, Ishikawa H, Hiyama T, Tsukuma H, et al. Liver with hypoechoic nodular pattern as a risk factor for hepatocellular carcinoma. Gastroenterology. 1995;108:1778–1784

    Article  CAS  PubMed  Google Scholar 

  104. Tarao K, Hoshino H, Shimizu A, Ohkawa S, Harada M, Nakamura Y, et al. Patients with ultrasonic coarse-nodular cirrhosis who are anti-hepatitis C virus-positive are at high risk for hepatocellular carcinoma. Cancer. 1995;75:1255–1262

    Article  CAS  PubMed  Google Scholar 

  105. Caturelli E, Castellano L, Fusilli S, Palmentieri B, Niro GA, del Vecchio-Blanco C, et al. Coarse nodular US pattern in hepatic cirrhosis: risk for hepatocellular carcinoma. Radiology. 2003;226:691–697

    Article  PubMed  Google Scholar 

  106. Kitson MT, Roberts SK, Colman JC, Paul E, Button P, Kemp W. Liver stiffness and the prediction of clinically significant portal hypertension and portal hypertensive complications. Scand J Gastroenterol. 2015;50:462–469

    Article  PubMed  Google Scholar 

  107. Elkrief L, Rautou P-E, Ronot M, Lambert S, Dioguardi Burgio M, Francoz C, et al. Prospective comparison of spleen and liver stiffness by using shear-wave and transient elastography for detection of portal hypertension in cirrhosis. Radiology. 2015;275:589–598

    Article  PubMed  Google Scholar 

  108. Elkrief L, Ronot M, Andrade F, Dioguardi Burgio M, Issoufaly T, Zappa M, et al. Non-invasive evaluation of portal hypertension using shear-wave elastography: analysis of two algorithms combining liver and spleen stiffness in 191 patients with cirrhosis. Aliment Pharmacol Ther. 2018;47:621–630

    Article  CAS  PubMed  Google Scholar 

  109. Ronot M, Lambert S, Elkrief L, Doblas S, Rautou P-E, Castera L, et al. Assessment of portal hypertension and high-risk oesophageal varices with liver and spleen three-dimensional multifrequency MR elastography in liver cirrhosis. Eur Radiol. 2014;24:1394–1402

    PubMed  Google Scholar 

  110. Choi S-Y, Jeong WK, Kim Y, Kim J, Kim TY, Sohn JH. Shear-wave elastography: a noninvasive tool for monitoring changing hepatic venous pressure gradients in patients with cirrhosis. Radiology. 2014;273:917–926

    Article  PubMed  Google Scholar 

  111. Grgurević I, Bokun T, Mustapić S, Trkulja V, Heinzl R, Banić M, et al. Real-time two-dimensional shear wave ultrasound elastography of the liver is a reliable predictor of clinical outcomes and the presence of esophageal varices in patients with compensated liver cirrhosis. Croat Med J. 2015;56:470–481

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Merchante N, Rivero-Juárez A, Téllez F, Merino D, Ríos-Villegas MJ, Ojeda-Burgos G, et al. Liver stiffness predicts variceal bleeding in HIV/HCV-coinfected patients with compensated cirrhosis. AIDS. 2017;31:493–500

    Article  PubMed  Google Scholar 

  113. Robic MA, Procopet B, Métivier S, Péron JM, Selves J, Vinel JP, et al. Liver stiffness accurately predicts portal hypertension related complications in patients with chronic liver disease: a prospective study. J Hepatol. 2011;55:1017–1024

    Article  PubMed  Google Scholar 

  114. Souhami A, Sartoris R, Rautou P-E, Cauchy F, Bouattour M, Durand F, et al. Similar performance of liver stiffness measurement and liver surface nodularity for the detection of portal hypertension in patients with hepatocellular carcinoma. JHEP Rep. 2020;2:100147

    Article  PubMed  PubMed Central  Google Scholar 

  115. Takuma Y, Nouso K, Morimoto Y, Tomokuni J, Sahara A, Takabatake H, et al. Portal hypertension in patients with liver cirrhosis: diagnostic accuracy of spleen stiffness. Radiology. 2016;279:609–619

    Article  PubMed  Google Scholar 

  116. Qi X, An W, Liu F, Qi R, Wang L, Liu Y, et al. Virtual hepatic venous pressure gradient with CT angiography (CHESS 1601): a prospective multicenter study for the noninvasive diagnosis of portal hypertension. Radiology. 2019;290:370–377

    Article  PubMed  Google Scholar 

  117. Lin Y, Li L, Yu D, Liu Z, Zhang S, Wang Q, et al. A novel radiomics-platelet nomogram for the prediction of gastroesophageal varices needing treatment in cirrhotic patients. Hepatol Int. 2021;15:995–1005

    Article  PubMed  Google Scholar 

  118. Liu Y, Ning Z, Örmeci N, An W, Yu Q, Han K, et al. Deep convolutional neural network-aided detection of portal hypertension in patients with cirrhosis. Clin Gastroenterol Hepatol. 2020;18:2998-3007.e5

    Article  PubMed  Google Scholar 

  119. Tseng Y, Ma L, Li S, Luo T, Luo J, Zhang W, et al. Application of CT-based radiomics in predicting portal pressure and patient outcome in portal hypertension. Eur J Radiol. 2020;126:108927

    Article  PubMed  Google Scholar 

  120. Meng D, Wei Y, Feng X, Kang B, Wang X, Qi J, et al. CT-based radiomics score can accurately predict esophageal variceal rebleeding in cirrhotic patients. Front Med. 2021;8:745931

    Article  Google Scholar 

  121. Zhu W-S, Shi S-Y, Yang Z-H, Song C, Shen J. Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure. World J Gastroenterol. 2020;26:1208–1220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Chen Y, Liu Z, Mo Y, Li B, Zhou Q, Peng S, et al. Prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma based on radiomics using Gd-EOB-DTPA-enhanced MRI: the liver failure model. Front Oncol. 2021;11:605296

    Article  PubMed  PubMed Central  Google Scholar 

  123. Cai W, He B, Hu M, Zhang W, Xiao D, Yu H, et al. A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma. Surg Oncol. 2019;28:78–85

    Article  PubMed  Google Scholar 

  124. Xu X, Zhang H-L, Liu Q-P, Sun S-W, Zhang J, Zhu F-P, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70:1133–1144

    Article  PubMed  Google Scholar 

  125. Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep. 2018;8:15497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Chlebus G, Meine H, Thoduka S, Abolmaali N, van Ginneken B, Hahn HK, et al. Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections. PLoS One. 2019;14:e0217228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Zabron A, Quaglia A, Fatourou E, Peddu P, Lewis D, Heneghan M, et al. Clinical and prognostic associations of liver volume determined by computed tomography in acute liver failure. Liver Int. 2018;38:1592–1601

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by French state funds managed within the “Plan Investissements d’Avenir” and by the ANR (ANR-10-IAHU-02 to B.G), by the ARC, Paris and Institut Hospitalo-Universitaire, Strasbourg (TheraHCC1.0 and 2.0 IHUARC IHU201301187 and IHUARC2019 to T.F.B.), the European Union (ERC-AdG-2014-671231-HEPCIR to T.F.B. and Y.H., ERC-AdG-2020-667273-FIBCAN to T.F.B. and Y. H.), ANRS, Paris (ECTZ171594 to J.L., ECTZ131760 to J.L. and P.N., ECTZ160436 and ECTZ103701 to T.F.B), NIH (DK099558 and CA233794 to Y.H., CA209940 and R03AI131066 to T.F.B.), Cancer Prevention and Research Institute of Texas (RR180016 to Y.H), US Department of Defense (W81XWH-16-1-0363 to T.F.B. and Y.H.), the Irma T. Hirschl/Monique Weill-Caulier Trust (Y.H.) and the Foundation of the University of Strasbourg (HEPKIN to T.F.B.) and the Institut Universitaire de France (IUF; T.F.B.). This work has been published under the framework of the LABEX ANR-10-LABX-0028_HEPSYS and Inserm Plan Cancer and benefits from funding from the state managed by the French National Research Agency as part of the Investments for the future program.

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Authors and Affiliations

  1. Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France

    Jérémy Dana, Antonio Saviano, Joachim Lupberger & Thomas F. Baumert

  2. Institut Hospitalo-Universitaire (IHU), Strasbourg, France

    Jérémy Dana, Aïna Venkatasamy & Benoit Gallix

  3. Université de Strasbourg, Strasbourg, France

    Jérémy Dana, Antonio Saviano, Joachim Lupberger, Benoit Gallix & Thomas F. Baumert

  4. Department of Diagnostic Radiology, McGill University, Montreal, Canada

    Jérémy Dana, Caroline Reinhold & Benoit Gallix

  5. Streinth Lab (Stress Response and Innovative Therapies), Inserm UMR_S 1113 IRFAC, Interface Recherche Fondamentale et Appliquée à la Cancérologie, 3 Avenue Moliere, Strasbourg, France

    Aïna Venkatasamy

  6. Department of Radiology Medical Physics, Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Killianstrasse 5a, 79106, Freiburg, Germany

    Aïna Venkatasamy

  7. Pôle Hépato-Digestif, Hôpitaux Universitaires de Strasbourg, Strasbourg, France

    Antonio Saviano & Thomas F. Baumert

  8. Liver Tumor Translational Research Program, Division of Digestive and Liver Diseases, Department of Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, USA

    Yujin Hoshida

  9. Radiology Department, Hôpital Beaujon, Université de Paris, CRI, INSERM 1149, APHP. Nord, Paris, France

    Valérie Vilgrain

  10. Liver Unit, Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris Seine Saint-Denis, Bobigny, France

    Pierre Nahon

  11. Université Sorbonne Paris Nord, 93000, Bobigny, France

    Pierre Nahon

  12. Inserm, UMR-1138 “Functional Genomics of Solid Tumors”, Paris, France

    Pierre Nahon

  13. Augmented Intelligence and Precision Health Laboratory, Research Institute of McGill University Health Centre, Montreal, Canada

    Caroline Reinhold

  14. Montreal Imaging Experts Inc., Montreal, Canada

    Caroline Reinhold

Authors
  1. Jérémy Dana
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  2. Aïna Venkatasamy
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  3. Antonio Saviano
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  5. Yujin Hoshida
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  6. Valérie Vilgrain
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  8. Caroline Reinhold
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  9. Benoit Gallix
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  10. Thomas F. Baumert
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Corresponding authors

Correspondence to Jérémy Dana or Thomas F. Baumert.

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Conflict of interest

TFB is founder, shareholder and advisor of Alentis Therapeutics. He is an inventor on patent applications of the University of Strasbourg, Inserm and IHU for liver disease therapeutics and biomarkers. PN has relationships with AstraZeneca, Bayer, Bristol-Myers Squibb, EISAI, Ipsen, Roche. JD, AV, AS, JL, YH, VV, CR and BG declare no conflict of interest with this publication.

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Dana, J., Venkatasamy, A., Saviano, A. et al. Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease. Hepatol Int 16, 509–522 (2022). https://doi.org/10.1007/s12072-022-10303-0

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