Advances in machine learning in treatment and diagnosis of liver disease_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

DAI Zonglin ¹ , LIANG Yuxin ¹ , LIU Yilong ¹ ,  HUANG Xiaolun ²

1. School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
2. Department of Hepatobiliary and Pancreatic Surgery, Liver Transplantation Center, Affiliated Cancer Hospital of University of Electronic Science and Technology, Sichuan Cancer Hospital, Chengdu 610042, P. R. China;

Corresponding author：

HUANG Xiaolun, Email: huangxiaolun@med.uestc.edu.cn

Keywords：

machine learning; liver disease; treatment and diagnosis; predictive model

DOI：

10.7507/1007-9424.202302008

Video：

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Objective To summarize advances in the application of machine learning in the diagnosis and treatment of liver disease. Method The recent literatures on the progress of machine learning in the diagnosis, treatment and prognosis of liver diseases were reviewed. Results Machine learning could be used to diagnose and categorize substantial liver lesions, tumourous lesions and rare liver diseases at an early stage, which could facilitate clinicians to take timely and appropriate treatment measures. Machine learning was helpful in informing clinicians in choosing the best treatment decision, which was conducive to reducing medical risks. It could also help to determine the prognosis of patients in a comprehensive manner, and provide assistance in formulating early rehabilitation treatment plans, adjusting follow-up strategies and improving future prognosis. Conclusions Multiple types of machine learning algorithms have achieved positive results in the clinical application of liver diseases by constructing different prediction models, and have great potential and excellent prospects in multiple aspects such as diagnosis, treatment and prognosis of liver diseases.

Citation： DAI Zonglin, LIANG Yuxin, LIU Yilong, HUANG Xiaolun. Advances in machine learning in treatment and diagnosis of liver disease. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2023, 30(6): 764-768. doi: 10.7507/1007-9424.202302008 Copy

1.	Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med, 2018, 284(6): 603-619.
2.	Gerussi A, Scaravaglio M, Cristoferi L, et al. Artificial intelligence for precision medicine in autoimmune liver disease. Front Immunol, 2022, 13: 966329. doi: 10.3389/fimmu.2022.966329.
3.	Hindson J. Proteomics and machine-learning models for alcohol-related liver disease biomarkers. Nat Rev Gastroenterol Hepatol, 2022, 19(8): 488. doi: 10.1038/s41575-022-00655-1.
4.	Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol, 2021, 36(3): 569-580.
5.	李昊朋. 基于机器学习方法的智能机器人探究. 通讯世界, 2019, 26(4): 241-242.
6.	Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
7.	葛恭豪. 机器学习算法原理及效率分析. 电子世界, 2018, 40(1): 65-66.
8.	Ji W, Xue M, Zhang Y, et al. A machine learning based framework to identify and classify non-alcoholic fatty liver disease in a large-scale population. Front Public Health, 2022, 10: 846118. doi: 10.3389/fpubh.2022.846118.
9.	雷丽, 李运明. 基于机器学习算法建立脂肪肝预测模型. 甘肃科学学报, 2022, 34(3): 16-20, 37.
10.	戴晓霞, 李娟, 王晨阳, 等. 基于机器学习的脂肪肝预测模型构建. 湖北医药学院学报, 2022, 41(6): 574-577, 583, 666.
11.	Lim J, Han S, Lee D, et al. Identification of hepatic steatosis in living liver donors by machine learning models. Hepatol Commun, 2022, 6(7): 1689-1698.
12.	Wang K, Lu X, Zhou H, 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(4): 729-741.
13.	Hatami B, Asadi F, Bayani A, et al. Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study. Clin Chem Lab Med, 2022, 60(12): 1946-1954.
14.	Mao B, Ma J, Duan S, et al. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol, 2021, 31(7): 4576-4586.
15.	Oyama A, Hiraoka Y, Obayashi I, et al. Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach. Sci Rep, 2019, 9(1): 8764. doi: 10.1038/s41598-019-45283-z.
16.	胥生科, 窦景锐, 吾布里塔里甫·达吾提, 等. 基于血清SERS的肝癌分类模型的机器学习算法优化研究. 兰州大学学报 (医学版), 2022, 48(11): 76-80, 85.
17.	Li M, Li X, Guo Y, et al. Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases. Quant Imaging Med Surg, 2020, 10(2): 397-414.
18.	Yang X, Shao G, Liu J, et al. Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system. Front Oncol, 2022, 12: 1021570. doi: 10.3389/fonc.2022.1021570.
19.	张旭辉, 索朗拉姆, 邱甲军, 等. 基于超声影像组学建立肝棘球蚴病分型模型的可行性研究. 中国血吸虫病防治杂志, 2022, 34(5): 500-506, 536.
20.	许文瑶, 张铁亮, 夏雨薇, 等. 基于CT影像的机器学习模型术前预测肝脏泡型包虫病边缘带浸润. 中国医学计算机成像杂志, 2022, 28(3): 286-290.
21.	Tran KA, Kondrashova O, Bradley A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med, 2021, 13(1): 152. doi: 10.1186/s13073-021-00968-x.
22.	Jung S, Park K, Ihn K, et al. Predicting graft failure in pediatric liver transplantation based on early biomarkers using machine learning models. Sci Rep, 2022, 12(1): 22411. doi: 10.1038/s41598-022-25900-0.
23.	Giglio MC, Zanfardino M, Franzese M, et al. Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation. Liver Transpl, 2023, 29(2): 172-183.
24.	Chen S, Liu LP, Wang YJ, et al. Advancing prediction of risk of intraoperative massive blood transfusion in liver transplantation with machine learning models. A multicenter retrospective study. Front Neuroinform, 2022, 16: 893452. doi: 10.3389/fninf.2022.893452.
25.	Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept. J Vasc Interv Radiol, 2018, 29(6): 850-857.
26.	严律南, 杨家印. 人工智能肝癌临床决策支持系统的开发、验证和应用价值. 中国普外基础与临床杂志, 2020, 27(9): 1052-1056.
27.	Liu X, Zheng D, Zhong Y, et al. Machine-learning prediction of oral drug-induced liver injury (DILI) via multiple features and endpoints. Biomed Res Int, 2020, 2020: 4795140. doi: 10.1155/2020/4795140.
28.	Minerali E, Foil DH, Zorn KM, et al. Comparing machine learning algorithms for predicting drug-induced liver injury (DILI). Mol Pharm, 2020, 17(7): 2628-2637.
29.	Kim JS, Han JM, Cho YS, et al. Machine learning approaches to predict hepatotoxicity risk in patients receiving Nilotinib. Molecules, 2021, 26(11): 3300. doi: 10.3390/molecules26113300.
30.	Campagnini S, Arienti C, Patrini M, et al. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review. J Neuroeng Rehabil, 2022, 19(1): 54. doi: 10.1186/s12984-022-01032-4.
31.	Liu S, See KC, Ngiam KY, et al. Reinforcement learning for clinical decision support in critical care: comprehensive review. J Med Internet Res, 2020, 22(7): e18477. doi: 10.2196/18477.
32.	Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology, 2020, 72(6): 2000-2013.
33.	Liu X, Lu J, Zhang G, et al. A machine learning approach yields a multiparameter prognostic marker in liver cancer. Cancer Immunol Res, 2021, 9(3): 337-347.
34.	Wang Y, Ji C, Wang Y, et al. Predicting postoperative liver cancer death outcomes with machine learning. Curr Med Res Opin, 2021, 37(4): 629-634.
35.	Jolissaint JS, Wang T, Soares KC, et al. Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma. HPB (Oxford), 2022, 24(8): 1341-1350.
36.	崔金涛, 许建生. 钆塞酸二钠增强MRI影像组学对肝血管瘤腹腔镜肝切除术后疗效的预测价值. 肝胆胰外科杂志, 2022, 34(5): 271-278.
37.	Guo A, Mazumder NR, Ladner DP, et al. Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning. PLoS One, 2021, 16(8): e0256428. doi: 10.1371/journal.pone.0256428.
38.	Park H, Lo-Ciganic WH, Huang J, et al. Machine learning algorithms for predicting direct-acting antiviral treatment failure in chronic hepatitis C: an HCV-TARGET analysis. Hepatology, 2022, 76(2): 483-491.
39.	Tonon M, Moreau R. Using machine learning for predicting outcomes in ACLF. Liver Int, 2022, 42(11): 2354-2355.

1. Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med, 2018, 284(6): 603-619.
2. Gerussi A, Scaravaglio M, Cristoferi L, et al. Artificial intelligence for precision medicine in autoimmune liver disease. Front Immunol, 2022, 13: 966329. doi: 10.3389/fimmu.2022.966329.
3. Hindson J. Proteomics and machine-learning models for alcohol-related liver disease biomarkers. Nat Rev Gastroenterol Hepatol, 2022, 19(8): 488. doi: 10.1038/s41575-022-00655-1.
4. Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol, 2021, 36(3): 569-580.
5. 李昊朋. 基于机器学习方法的智能机器人探究. 通讯世界, 2019, 26(4): 241-242.
6. Deo RC. Machine learning in medicine. Circulation, 2015, 132(20): 1920-1930.
7. 葛恭豪. 机器学习算法原理及效率分析. 电子世界, 2018, 40(1): 65-66.
8. Ji W, Xue M, Zhang Y, et al. A machine learning based framework to identify and classify non-alcoholic fatty liver disease in a large-scale population. Front Public Health, 2022, 10: 846118. doi: 10.3389/fpubh.2022.846118.
9. 雷丽, 李运明. 基于机器学习算法建立脂肪肝预测模型. 甘肃科学学报, 2022, 34(3): 16-20, 37.
10. 戴晓霞, 李娟, 王晨阳, 等. 基于机器学习的脂肪肝预测模型构建. 湖北医药学院学报, 2022, 41(6): 574-577, 583, 666.
11. Lim J, Han S, Lee D, et al. Identification of hepatic steatosis in living liver donors by machine learning models. Hepatol Commun, 2022, 6(7): 1689-1698.
12. Wang K, Lu X, Zhou H, 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(4): 729-741.
13. Hatami B, Asadi F, Bayani A, et al. Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study. Clin Chem Lab Med, 2022, 60(12): 1946-1954.
14. Mao B, Ma J, Duan S, et al. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol, 2021, 31(7): 4576-4586.
15. Oyama A, Hiraoka Y, Obayashi I, et al. Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach. Sci Rep, 2019, 9(1): 8764. doi: 10.1038/s41598-019-45283-z.
16. 胥生科, 窦景锐, 吾布里塔里甫·达吾提, 等. 基于血清SERS的肝癌分类模型的机器学习算法优化研究. 兰州大学学报 (医学版), 2022, 48(11): 76-80, 85.
17. Li M, Li X, Guo Y, et al. Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases. Quant Imaging Med Surg, 2020, 10(2): 397-414.
18. Yang X, Shao G, Liu J, et al. Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system. Front Oncol, 2022, 12: 1021570. doi: 10.3389/fonc.2022.1021570.
19. 张旭辉, 索朗拉姆, 邱甲军, 等. 基于超声影像组学建立肝棘球蚴病分型模型的可行性研究. 中国血吸虫病防治杂志, 2022, 34(5): 500-506, 536.
20. 许文瑶, 张铁亮, 夏雨薇, 等. 基于CT影像的机器学习模型术前预测肝脏泡型包虫病边缘带浸润. 中国医学计算机成像杂志, 2022, 28(3): 286-290.
21. Tran KA, Kondrashova O, Bradley A, et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med, 2021, 13(1): 152. doi: 10.1186/s13073-021-00968-x.
22. Jung S, Park K, Ihn K, et al. Predicting graft failure in pediatric liver transplantation based on early biomarkers using machine learning models. Sci Rep, 2022, 12(1): 22411. doi: 10.1038/s41598-022-25900-0.
23. Giglio MC, Zanfardino M, Franzese M, et al. Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation. Liver Transpl, 2023, 29(2): 172-183.
24. Chen S, Liu LP, Wang YJ, et al. Advancing prediction of risk of intraoperative massive blood transfusion in liver transplantation with machine learning models. A multicenter retrospective study. Front Neuroinform, 2022, 16: 893452. doi: 10.3389/fninf.2022.893452.
25. Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept. J Vasc Interv Radiol, 2018, 29(6): 850-857.
26. 严律南, 杨家印. 人工智能肝癌临床决策支持系统的开发、验证和应用价值. 中国普外基础与临床杂志, 2020, 27(9): 1052-1056.
27. Liu X, Zheng D, Zhong Y, et al. Machine-learning prediction of oral drug-induced liver injury (DILI) via multiple features and endpoints. Biomed Res Int, 2020, 2020: 4795140. doi: 10.1155/2020/4795140.
28. Minerali E, Foil DH, Zorn KM, et al. Comparing machine learning algorithms for predicting drug-induced liver injury (DILI). Mol Pharm, 2020, 17(7): 2628-2637.
29. Kim JS, Han JM, Cho YS, et al. Machine learning approaches to predict hepatotoxicity risk in patients receiving Nilotinib. Molecules, 2021, 26(11): 3300. doi: 10.3390/molecules26113300.
30. Campagnini S, Arienti C, Patrini M, et al. Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review. J Neuroeng Rehabil, 2022, 19(1): 54. doi: 10.1186/s12984-022-01032-4.
31. Liu S, See KC, Ngiam KY, et al. Reinforcement learning for clinical decision support in critical care: comprehensive review. J Med Internet Res, 2020, 22(7): e18477. doi: 10.2196/18477.
32. Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology, 2020, 72(6): 2000-2013.
33. Liu X, Lu J, Zhang G, et al. A machine learning approach yields a multiparameter prognostic marker in liver cancer. Cancer Immunol Res, 2021, 9(3): 337-347.
34. Wang Y, Ji C, Wang Y, et al. Predicting postoperative liver cancer death outcomes with machine learning. Curr Med Res Opin, 2021, 37(4): 629-634.
35. Jolissaint JS, Wang T, Soares KC, et al. Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma. HPB (Oxford), 2022, 24(8): 1341-1350.
36. 崔金涛, 许建生. 钆塞酸二钠增强MRI影像组学对肝血管瘤腹腔镜肝切除术后疗效的预测价值. 肝胆胰外科杂志, 2022, 34(5): 271-278.
37. Guo A, Mazumder NR, Ladner DP, et al. Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning. PLoS One, 2021, 16(8): e0256428. doi: 10.1371/journal.pone.0256428.
38. Park H, Lo-Ciganic WH, Huang J, et al. Machine learning algorithms for predicting direct-acting antiviral treatment failure in chronic hepatitis C: an HCV-TARGET analysis. Hepatology, 2022, 76(2): 483-491.
39. Tonon M, Moreau R. Using machine learning for predicting outcomes in ACLF. Liver Int, 2022, 42(11): 2354-2355.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Advances in machine learning in treatment and diagnosis of liver disease

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