Application of artificial intelligence in diagnosis and treatment of liver cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

TAN Yifei , ZHANG Wei , YANG Jiayin ,  YAN Lünan

Departmen of Liver Surgery/Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

YAN Lünan, Email: yanlunan688@163.com

Keywords：

artificial intelligence; liver cancer; diagnosis; treatment

DOI：

10.7507/1007-9424.202008023

Video：

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Objective To better understand artificial intelligence (AI) and its application in management of liver cancer.Method The relevant literatures about AI in the diagnosis and treatment of liver cancer in recent years were reviewed.Results In terms of diagnosis, the deep learning could precisely and quickly complete the imaging localization and segmentation of the liver, which was helpful for the diagnosis, while radiomics had a high value in assisting the diagnosis of liver cancer, predicting the postoperative recurrence and long-term survival of patients with liver cancer. In regard of treatment, although it was still difficult for AI to intervene in liver surgery, it had significant advantages in formulating individualized operation scheme for patients with liver cancer, which enabled precise hepatectomy and was helpful for prediction of intraoperative bleeding. AI fusion imaging could provide assistance in operation plan making and realize the precise placement of ablation needle. AI was able to predict the tumor response or even tumor progression after interventional therapy and radiotherapy. Pathological analysis was also facilitated by AI and was able to identify some details and feature textures that were difficult to manually distinguish. For transplantation, guidance of AI on the allocation of donor livers based on hazards models helped make better use of limited organ resources. AI could be applied in prognosis prediction in almost all treatment modalities.Conclusions AI provides more efficient and precise diagnosis, treatment support and prognosis than conventional medical process in liver cancer, generally by constructing a fully functional model based on a series of data mining methods combined with statistical analysis.

Citation： TAN Yifei, ZHANG Wei, YANG Jiayin, YAN Lünan. Application of artificial intelligence in diagnosis and treatment of liver cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2020, 27(9): 1057-1061. doi: 10.7507/1007-9424.202008023 Copy

1.	Zhen SH, Cheng M, Tao YB, et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front Oncol, 2020, 10: 680.
2.	Calderaro J, Ziol M, Paradis V, et al. Molecular and histological correlations in liver cancer. J Hepatol, 2019, 71(3): 616-630.
3.	Yasaka K, Akai H, Abe O, et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology, 2018, 286(3): 887-896.
4.	Mokrane FZ, Lu L, Vavasseur A, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol, 2020, 30(1): 558-570.
5.	Okada T, Linguraru MG, Hori M, et al. Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal, 2015, 26(1): 1-18.
6.	Tong T, Wolz R, Wang Z, et al. Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal, 2015, 23(1): 92-104.
7.	Wang CL, Örjan S. Automatic multi-organ segmentation using fast model based level set method and hierarchical shape priors//Goksel O. Beijing: Proceedings of the VISCERAL Organ Segmentation and Landmark Detection Benchmark at the 2014 IEEE International Symposium on Biomedical Imaging (ISBI), 2014.
8.	Chartrand G, Cresson T, Chav R, et al. Liver segmentation on CT and MR using laplacian mesh optimization. IEEE Trans Biomed Eng, 2017, 64(9): 2110-2121.
9.	Hamm CA, Wang CJ, Savic LJ, 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(7): 3338-3347.
10.	Fu Y, Mazur TR, Wu X, et al. A novel MRI segmentation method using CNN-based correction network for MRI-guided adaptive radiotherapy. Med Phys, 2018, 45(11): 5129-5137.
11.	Wang CJ, Hamm CA, Savic LJ, et al. Deep learning for liver tumor diagnosis part Ⅱ: convolutional neural network interpretation using radiologic imaging features. Eur Radiol, 2019, 29(7): 3348-3357.
12.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
13.	Jiang HY, Liu XJ, Chen J, et al. Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma. Cancer Imaging, 2019, 19(1): 84.
14.	Xu X, Zhang HL, Liu QP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol, 2019, 70(6): 1133-1144.
15.	Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, et al. Artificial intelligence to support clinical decision-making processes. EBioMedicine, 2019, 46: 27-29.
16.	Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet, 2018, 391(10127): 1301-1314.
17.	Burström G, Buerger C, Hoppenbrouwers J, et al. Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. J Neurosurg Spine, 2019, 31(1): 147-154.
18.	Hu M, Hu H, Cai W, et al. The safety and feasibility of three-dimensional visualization technology assisted right posterior lobe allied with part of Ⅴ and Ⅷ sectionectomy for right hepatic malignancy therapy. J Laparoendosc Adv Surg Tech A, 2018, 28(5): 586-594.
19.	中华医学会数字医学分会, 中国医师协会肝癌专业委员会, 中国医师协会临床精准医学专业委员会, 等. 复杂性肝脏肿瘤切除三维可视化精准诊治指南(2019版). 南方医科大学学报, 2020, 40(3): 297-307.
20.	Fang C, An J, Bruno A, et al. Consensus recommendations of three-dimensional visualization for diagnosis and management of liver diseases. Hepatol Int, 2020, 14(4): 437-453.
21.	Ueno M, Hayami S, Sonomura T, et al. Indocyanine green fluorescence imaging techniques and interventional radiology during laparoscopic anatomical liver resection (with video). Surg Endosc, 2018, 32(2): 1051-1055.
22.	方驰华, 张鹏, 刘允怡, 等. 肝胆胰疾病数字智能化诊疗核心技术、体系构建及其应用. 中华外科杂志, 2019, 57(4): 253-257.
23.	Sauer IM, Queisner M, Tang P, et al. Mixed reality in visceral surgery: development of a suitable workflow and evaluation of intraoperative use-cases. Ann Surg, 2017, 266(5): 706-712.
24.	Gordon L, Grantcharov T, Rudzicz F. Explainable artificial intelligence for safe intraoperative decision support. JAMA Surg, 2019, 154(11): 1064-1065.
25.	Yuan H, Liu F, Li X, et al. Transcatheter arterial chemoembolization combined with simultaneous DynaCT-guided radiofrequency ablation in the treatment of solitary large hepatocellular carcinoma. Radiol Med, 2019, 124(1): 1-7.
26.	Citone M, Fanelli F, Falcone G, et al. A closer look to the new frontier of artificial intelligence in the percutaneous treatment of primary lesions of the liver. Med Oncol, 2020, 37(6): 55.
27.	Ahn SJ, Lee JM, Lee DH, et al. Real-time US-CT/MR fusion imaging for percutaneous radiofrequency ablation of hepatocellular carcinoma. J Hepatol, 2017, 66(2): 347-354.
28.	Huang Q, Zeng Q, Long Y, et al. Fusion imaging techniques and contrast-enhanced ultrasound for thermal ablation of hepatocellular carcinoma—A prospective randomized controlled trial. Int J Hyperthermia, 2019, 36(1): 1207-1215.
29.	Lim S, Lee MW, Rhim H, et al. Mistargeting after fusion imaging-guided percutaneous radiofrequency ablation of hepatocellular carcinomas. J Vasc Interv Radiol, 2014, 25(2): 307-314.
30.	Kim BK, Kim SU, Kim KA, et al. Complete response at first chemoembolization is still the most robust predictor for favorable outcome in hepatocellular carcinoma. J Hepatol, 2015, 62(6): 1304-1310.
31.	Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to image-guided therapies using machine learning: an example for trans-arterial treatment of hepatocellular carcinoma. J Vis Exp, 2018, (140): 58382.
32.	Liu D, Liu F, Xie X, et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol, 2020, 30(4): 2365-2376.
33.	Ibragimov B, Toesca D, Chang D, et al. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys, 2018, 45(10): 4763-4774.
34.	Ibragimov B, Toesca DAS, Yuan Y, et al. Neural networks for deep radiotherapy dose analysis and prediction of liver SBRT outcomes. IEEE J Biomed Health Inform, 2019, 23(5): 1821-1833.
35.	Chen H, He Y, Jia W. Precise hepatectomy in the intelligent digital era. Int J Biol Sci, 2020, 16(3): 365-373.
36.	Li S, Jiang H, Pang W. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading. Comput Biol Med, 2017, 84: 156-167.
37.	Liao HT, Long YX, Han YJ, et al. Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma. Clin Transl Med, 2020 Jun 14. doi: 10.1002/ctm2.102.
38.	Ayllón MD, Ciria R, Cruz-Ramírez M, et al. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transpl, 2018, 24(2): 192-203.
39.	Dorado-Moreno M, Pérez-Ortiz M, Gutiérrez PA, et al. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artif Intell Med, 2017, 77: 1-11.
40.	Ershoff BD, Lee CK, Wray CL, et al. Training and validation of deep neural networks for the prediction of 90-day post-liver transplant mortality using UNOS registry data. Transplant Proc, 2020, 52(1): 246-258.
41.	Cesaretti M, Brustia R, Goumard C, et al. Use of artificial intelligence as innovative method for liver graft macrosteatosis assessment. Liver Transpl, 2020 May 19. doi: 10.1002/lt.25801.
42.	Molinari M, Ayloo S, Tsung A, et al. Prediction of perioperative mortality of cadaveric liver transplant recipients during their evaluations. Transplantation, 2019, 103(10): e297-e307.297-307.
43.	Guo D, Gu D, Wang H, et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol, 2019, 117: 33-40.
44.	Shan QY, Hu HT, Feng ST, et al. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging, 2019, 19(1): 11.

1. Zhen SH, Cheng M, Tao YB, et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front Oncol, 2020, 10: 680.
2. Calderaro J, Ziol M, Paradis V, et al. Molecular and histological correlations in liver cancer. J Hepatol, 2019, 71(3): 616-630.
3. Yasaka K, Akai H, Abe O, et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology, 2018, 286(3): 887-896.
4. Mokrane FZ, Lu L, Vavasseur A, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol, 2020, 30(1): 558-570.
5. Okada T, Linguraru MG, Hori M, et al. Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal, 2015, 26(1): 1-18.
6. Tong T, Wolz R, Wang Z, et al. Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal, 2015, 23(1): 92-104.
7. Wang CL, Örjan S. Automatic multi-organ segmentation using fast model based level set method and hierarchical shape priors//Goksel O. Beijing: Proceedings of the VISCERAL Organ Segmentation and Landmark Detection Benchmark at the 2014 IEEE International Symposium on Biomedical Imaging (ISBI), 2014.
8. Chartrand G, Cresson T, Chav R, et al. Liver segmentation on CT and MR using laplacian mesh optimization. IEEE Trans Biomed Eng, 2017, 64(9): 2110-2121.
9. Hamm CA, Wang CJ, Savic LJ, 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(7): 3338-3347.
10. Fu Y, Mazur TR, Wu X, et al. A novel MRI segmentation method using CNN-based correction network for MRI-guided adaptive radiotherapy. Med Phys, 2018, 45(11): 5129-5137.
11. Wang CJ, Hamm CA, Savic LJ, et al. Deep learning for liver tumor diagnosis part Ⅱ: convolutional neural network interpretation using radiologic imaging features. Eur Radiol, 2019, 29(7): 3348-3357.
12. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
13. Jiang HY, Liu XJ, Chen J, et al. Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma. Cancer Imaging, 2019, 19(1): 84.
14. Xu X, Zhang HL, Liu QP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol, 2019, 70(6): 1133-1144.
15. Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, et al. Artificial intelligence to support clinical decision-making processes. EBioMedicine, 2019, 46: 27-29.
16. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet, 2018, 391(10127): 1301-1314.
17. Burström G, Buerger C, Hoppenbrouwers J, et al. Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. J Neurosurg Spine, 2019, 31(1): 147-154.
18. Hu M, Hu H, Cai W, et al. The safety and feasibility of three-dimensional visualization technology assisted right posterior lobe allied with part of Ⅴ and Ⅷ sectionectomy for right hepatic malignancy therapy. J Laparoendosc Adv Surg Tech A, 2018, 28(5): 586-594.
19. 中华医学会数字医学分会, 中国医师协会肝癌专业委员会, 中国医师协会临床精准医学专业委员会, 等. 复杂性肝脏肿瘤切除三维可视化精准诊治指南(2019版). 南方医科大学学报, 2020, 40(3): 297-307.
20. Fang C, An J, Bruno A, et al. Consensus recommendations of three-dimensional visualization for diagnosis and management of liver diseases. Hepatol Int, 2020, 14(4): 437-453.
21. Ueno M, Hayami S, Sonomura T, et al. Indocyanine green fluorescence imaging techniques and interventional radiology during laparoscopic anatomical liver resection (with video). Surg Endosc, 2018, 32(2): 1051-1055.
22. 方驰华, 张鹏, 刘允怡, 等. 肝胆胰疾病数字智能化诊疗核心技术、体系构建及其应用. 中华外科杂志, 2019, 57(4): 253-257.
23. Sauer IM, Queisner M, Tang P, et al. Mixed reality in visceral surgery: development of a suitable workflow and evaluation of intraoperative use-cases. Ann Surg, 2017, 266(5): 706-712.
24. Gordon L, Grantcharov T, Rudzicz F. Explainable artificial intelligence for safe intraoperative decision support. JAMA Surg, 2019, 154(11): 1064-1065.
25. Yuan H, Liu F, Li X, et al. Transcatheter arterial chemoembolization combined with simultaneous DynaCT-guided radiofrequency ablation in the treatment of solitary large hepatocellular carcinoma. Radiol Med, 2019, 124(1): 1-7.
26. Citone M, Fanelli F, Falcone G, et al. A closer look to the new frontier of artificial intelligence in the percutaneous treatment of primary lesions of the liver. Med Oncol, 2020, 37(6): 55.
27. Ahn SJ, Lee JM, Lee DH, et al. Real-time US-CT/MR fusion imaging for percutaneous radiofrequency ablation of hepatocellular carcinoma. J Hepatol, 2017, 66(2): 347-354.
28. Huang Q, Zeng Q, Long Y, et al. Fusion imaging techniques and contrast-enhanced ultrasound for thermal ablation of hepatocellular carcinoma—A prospective randomized controlled trial. Int J Hyperthermia, 2019, 36(1): 1207-1215.
29. Lim S, Lee MW, Rhim H, et al. Mistargeting after fusion imaging-guided percutaneous radiofrequency ablation of hepatocellular carcinomas. J Vasc Interv Radiol, 2014, 25(2): 307-314.
30. Kim BK, Kim SU, Kim KA, et al. Complete response at first chemoembolization is still the most robust predictor for favorable outcome in hepatocellular carcinoma. J Hepatol, 2015, 62(6): 1304-1310.
31. Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to image-guided therapies using machine learning: an example for trans-arterial treatment of hepatocellular carcinoma. J Vis Exp, 2018, (140): 58382.
32. Liu D, Liu F, Xie X, et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol, 2020, 30(4): 2365-2376.
33. Ibragimov B, Toesca D, Chang D, et al. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys, 2018, 45(10): 4763-4774.
34. Ibragimov B, Toesca DAS, Yuan Y, et al. Neural networks for deep radiotherapy dose analysis and prediction of liver SBRT outcomes. IEEE J Biomed Health Inform, 2019, 23(5): 1821-1833.
35. Chen H, He Y, Jia W. Precise hepatectomy in the intelligent digital era. Int J Biol Sci, 2020, 16(3): 365-373.
36. Li S, Jiang H, Pang W. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading. Comput Biol Med, 2017, 84: 156-167.
37. Liao HT, Long YX, Han YJ, et al. Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma. Clin Transl Med, 2020 Jun 14. doi: 10.1002/ctm2.102.
38. Ayllón MD, Ciria R, Cruz-Ramírez M, et al. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transpl, 2018, 24(2): 192-203.
39. Dorado-Moreno M, Pérez-Ortiz M, Gutiérrez PA, et al. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artif Intell Med, 2017, 77: 1-11.
40. Ershoff BD, Lee CK, Wray CL, et al. Training and validation of deep neural networks for the prediction of 90-day post-liver transplant mortality using UNOS registry data. Transplant Proc, 2020, 52(1): 246-258.
41. Cesaretti M, Brustia R, Goumard C, et al. Use of artificial intelligence as innovative method for liver graft macrosteatosis assessment. Liver Transpl, 2020 May 19. doi: 10.1002/lt.25801.
42. Molinari M, Ayloo S, Tsung A, et al. Prediction of perioperative mortality of cadaveric liver transplant recipients during their evaluations. Transplantation, 2019, 103(10): e297-e307.297-307.
43. Guo D, Gu D, Wang H, et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol, 2019, 117: 33-40.
44. Shan QY, Hu HT, Feng ST, et al. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging, 2019, 19(1): 11.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Application of artificial intelligence in diagnosis and treatment of liver cancer

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