Research progress and clinical application of preoperative liver function evaluation indexes_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LUO Zhijiang ¹ , PENG Tao ¹ , LIN Xuan ¹ ,  CHEN Bin ²

1. The First Clinical Medical College of Gannan Medical University, Ganzhou, Jiangxi 341000, P. R. China;
2. Department of General Surgery, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi 341000, P. R. China;

Corresponding author：

CHEN Bin, Email: chenb1970829@163.com

Keywords：

liver function; evaluation index; prognosis

DOI：

10.7507/1007-9424.202010008

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To explore the clinical application and recent progress of preoperative liver function evaluation methods.Method The literatures about the studies of indexes of preoperative liver function evaluation were summarized and analyzed.Results Serological indexes could reflect liver function in many aspects; Indocyanine green clearance test, Child-Pugh and MELD scoring system could improve the accuracy of evaluating liver reserve function; Imaging examination and artificial intelligence (radiomics and deep learning) helped to diagnose the degree of liver fibrosis and accurately measure the volume of functional liver.Conclusions Each method of preoperative liver function evaluation has certain limitation. It needs comprehensive analysis combined with a variety of evaluation indexes so as to objectively and accurately evaluate liver function and formulate an individualized diagnosis and treatment plan that furthest benefits patients.

Citation： LUO Zhijiang, PENG Tao, LIN Xuan, CHEN Bin. Research progress and clinical application of preoperative liver function evaluation indexes. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(7): 962-968. doi: 10.7507/1007-9424.202010008 Copy

1.	Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2.	沈英皓, 孙惠川, 周俭. 肝切除术前肝脏储备功能评估. 中华肝脏外科手术学电子杂志, 2019, 8(6): 469-472.
3.	Kokudo N, Takemura N, Hasegawa K, et al. Clinical practice guidelines for hepatocellular carcinoma: The Japan Society of Hepatology 2017 (4th JSH-HCC guidelines) 2019 update. Hepatol Res, 2019, 49(10): 1109-1113.
4.	代鸿华, 张高明, 杨慧林, 等. γ-谷氨酰转移酶对原发性肝细胞癌术后患者预后的预测价值. 癌症进展, 2019, 17(5): 589-591, 616.
5.	陆华泽, 王小波, 黎乐群. 原发性肝癌术前肝储备功能评估方法的研究进展. 中国普通外科杂志, 2020, 29(1): 85-96.
6.	Maegawa FB, Shehorn L, Aziz H, et al. Association between noninvasive fibrosis markers and postoperative mortality after hepatectomy for hepatocellular carcinoma. JAMA Netw Open, 2019, 2(1): e187142.
7.	Ben Ayed H, Koubaa M, Yaich S, et al. A new combined predicting model using a non-invasive score for the assessment of liver fibrosis in patients presenting with chronic hepatitis B virus infection. Med Mal Infect, 2019, 49(8): 607-615.
8.	Hiraoka A, Michitaka K, Kumada T, et al. ALBI score as a novel tool in staging and treatment planning for hepatocellular carcinoma: advantage of ALBI grade for universal assessment of hepatic function. Liver Cancer, 2017, 6(4): 377-379.
9.	Na SK, Yim SY, Suh SJ, et al. ALBI versus Child-Pugh grading systems for liver function in patients with hepatocellular carcinoma. J Surg Oncol, 2018, 117(5): 912-921.
10.	Huang G, Jiang H, Lin Y, et al. Prognostic value of plasma fibrinogen in hepatocellular carcinoma: a meta-analysis. Cancer Manag Res, 2018, 10: 5027-5041.
11.	李正佐, 李慧, 吴泓. 术前血浆纤维蛋白原和血清白蛋白水平评分对肝细胞癌患者肝切除术后生存的预测价值. 中国普外基础与临床杂志, 2021, 28(2): 181-188.
12.	Cai X, Chen Z, Chen J, et al. Albumin-to-alkaline phosphatase ratio as an independent prognostic factor for overall survival of advanced hepatocellular carcinoma patients without receiving standard anti-cancer therapies. J Cancer, 2018, 9(1): 189-197.
13.	Li H, Wang L, Chen L, et al. Prognostic value of albumin-to-alkaline phosphatase ratio in hepatocellular carcinoma patients treated with liver transplantation. J Cancer, 2020, 11(8): 2171-2180.
14.	Li H, Li J, Wang J, et al. Assessment of liver function for evaluation of long-term outcomes of intrahepatic cholangiocarcinoma: A multi-institutional analysis of 620 patients. Front Oncol, 2020, 10: 525.
15.	Greten FR, Grivennikov SI. Inflammation and cancer: Triggers, mechanisms, and consequences. Immunity, 2019, 51(1): 27-41.
16.	冯志强, 郭玉明, 赵君会, 等. 行肝切除术肝细胞癌患者炎性指标水平变化及与预后的相关性. 中华实用诊断与治疗杂志, 2020, 34(9): 891-895.
17.	Shelat VG. Role of inflammatory indices in management of hepatocellular carcinoma-neutrophil to lymphocyte ratio. Ann Transl Med, 2020, 8(15): 912.
18.	孙志德, 程利民, 平萍, 等. 基于炎症反应指数评分系统在肝癌患者预后评估中的价值. 中国老年学杂志, 2017, 37(6): 1411-1413.
19.	Wang YY, Zhao XH, Ma L, et al. Comparison of the ability of Child-Pugh score, MELD score, and ICG-R15 to assess preoperative hepatic functional reserve in patients with hepatocellular carcinoma. J Surg Oncol, 2018, 118(3): 440-445.
20.	Wen X, Yao M, Lu Y, et al. Integration of prealbumin into Child-Pugh classification improves prognosis predicting accuracy in HCC patients considering curative surgery. J Clin Transl Hepatol, 2018, 6(4): 377-384.
21.	黄凤, 高建. 比较改良 Child-Pugh 评分与白蛋白-胆红素评分对肝细胞癌患者肝切除术后的预后的预测价值. 重庆: 重庆医科大学, 2020.
22.	Kamath PS, Wiesner RH, Malinchoc M, et al. A model to predict survival in patients with end-stage liver disease. Hepatology, 2001, 33(2): 464-470.
23.	柏科莉, 李妍宏, 王晓. 肝移植预后的影响因素和预测方法的临床进展. 中国普外基础与临床杂志, 2020, 27(3): 383-388.
24.	罗宇君, 张雅敏, 杨龙, 等. 肝部分切除术后连续性监测吲哚菁绿 15 min 滞留率对肝功能不全的评估价值. 临床肝胆病杂志, 2018, 34(5): 1055-1059.
25.	脱红芳, 彭彦辉. 吲哚菁绿在肝切除围手术期的临床应用. 肝胆胰外科杂志, 2020, 32(5): 313-316.
26.	Kokudo T, Hasegawa K, Amikura K, et al. Assessment of preoperative liver function in patients with hepatocellular carcinoma—The Albumin-Indocyanine Green Evaluation (ALICE) Grade. PLoS One, 2016, 11(7): e0159530.
27.	Russolillo N, Forchino F, Conci S, et al. Validation of the albumin-indocyanine green evaluation model in patients with resected hepatocellular carcinoma and comparison with the albumin-bilirubin score. J Hepatobiliary Pancreat Sci, 2019, 26(1): 51-57.
28.	Miyazaki Y, Kokudo T, Amikura K, et al. Albumin-indocyanine green evaluation grading system predicts post-hepatectomy liver failure for biliary tract cancer. Dig Surg, 2019, 36(1): 13-19.
29.	Qi X, An M, Wu T, et al. Transient elastography for significant liver fibrosis and cirrhosis in chronic hepatitis B: A meta-analysis. Can J Gastroenterol Hepatol, 2018, 2018: 3406789.
30.	宋雪, 阮冰. 无创影像学评估慢性乙型病毒性肝炎肝纤维化研究进展. 中国实用内科杂志, 2020, 40(5): 433-437.
31.	Chen SH, Lai HC, Chiang IP, et al. Performance of acoustic radiation force impulse elastography for staging liver fibrosis in patients with chronic hepatitis C after viral eradication. Clin Infect Dis, 2020, 70(1): 114-122.
32.	Liu J, Li Y, Yang X, et al. Comparison of two-dimensional shear wave elastography with nine serum fibrosis indices to assess liver fibrosis in patients with chronic hepatitis B: A prospective cohort study. Ultraschall Med, 2019, 40(2): 237-246.
33.	Herrmann E, de Lédinghen V, Cassinotto C, et al. Assessment of biopsy-proven liver fibrosis by two-dimensional shear wave elastography: An individual patient data-based meta-analysis. Hepatology, 2018, 67(1): 260-272.
34.	姚桃月, 陈文娟, 段星星, 等. 超声弹性成像在儿童肝病中的应用进展. 中国医学影像学杂志, 2020, 28(2): 146-149.
35.	杨华, 曾畅, 罗芳, 等. 超声造影检查无创评估放射性肝损伤患者可行性研究. 实用肝脏病杂志, 2020, 23(5): 674-677.
36.	Wang DC, Jang HJ, Kim TK. Characterization of indeterminate liver lesions on CT and MRI with contrast-enhanced ultrasound: What is the evidence? AJR Am J Roentgenol, 2020, 214(6): 1295-1304.
37.	Zhang Y, Zheng Y, Yang X, et al. Comparison of acoustic structure quantification, transient elastography (fibroscan) and histology in patients with chronic hepatitis B and without moderate to severe hepatic steatosis. Ultrasound Med Biol, 2019, 45(3): 684-692.
38.	Asemota J, Saleh M, Igbinovia O, et al. A concise review on current trends in imaging and surgical management of hepatocellular carcinoma. Cureus, 2020, 12(7): e9191.
39.	Maruyama M, Yoshizako T, Araki H, et al. Future liver remnant indocyanine green plasma clearance rate as a predictor of post-hepatectomy liver failure after portal vein embolization. Cardiovasc Intervent Radiol, 2018, 41(12): 1877-1884.
40.	容鹏飞, 冯智超, 郭睿, 等. CT 动脉增强分数评估肝硬化患者肝功能水平. 中南大学学报(医学版), 2019, 44(5): 469-476.
41.	Yamada S, Shimada M, Morine Y, et al. A new formula to calculate the resection limit in hepatectomy based on Gd-EOB-DTPA-enhanced magnetic resonance imaging. PLoS One, 2019, 14(1): e0210579.
42.	Asenbaum U, Kaczirek K, Ba-Ssalamah A, et al. Post-hepatectomy liver failure after major hepatic surgery: not only size matters. Eur Radiol, 2018, 28(11): 4748-4756.
43.	Wibmer A, Prusa A M, Nolz R, et al. Liver failure after major liver resection: risk assessment by using preoperative Gadoxetic acid-enhanced 3-T MR imaging. Radiology, 2013, 269(3): 777-786.
44.	Costa AF, Tremblay St-Germain A, Abdolell M, et al. Can contrast-enhanced MRI with gadoxetic acid predict liver failure and other complications after major hepatic resection? Clin Radiol, 2017, 72(7): 598-605.
45.	Barth BK, Fischer M A, Kambakamba P, et al. Liver-fat and liver-function indices derived from Gd-EOB-DTPA-enhanced liver MRI for prediction of future liver remnant growth after portal vein occlusion. Eur J Radiol, 2016, 85(4): 843-849.
46.	Tanoue Y, Nanashima A, Yano K, et al. Significance of the preoperative regional maximal removal rate of technetium-99m-galactosyl human serum albumin in the future remnant liver: a sequential study of regional maximal removal rate of technetium-99m-galactosyl human serum albumin in the whole liver. Nucl Med Commun, 2019, 40(2): 145-152.
47.	Nakamura I, Iimuro Y, Hai S, et al. Impaired value of ^99mTc-GSA scintigraphy as an independent risk factor for posthepatectomy liver failure in patients with hepatocellular carcinoma. Eur Surg Res, 2018, 59(1-2): 12-22.
48.	Chiba N, Yokozuka K, Ochiai S, et al. The diagnostic value of 99m-Tc GSA scintigraphy for liver function and remnant liver volume in hepatic surgery: a retrospective observational cohort study in 27 patients. Patient Saf Surg, 2018, 12: 15.
49.	Tokorodani R, Sumiyoshi T, Okabayashi T, et al. Liver fibrosis assessment using ^99mTc-GSA SPECT/CT fusion imaging. Jpn J Radiol, 2019, 37(4): 315-320.
50.	Huang X, Chen Y, Shao M, et al. The value of ^99mTc-labeled galactosyl human serum albumin single-photon emission computerized tomography/computed tomography on regional liver function assessment and posthepatectomy failure prediction in patients with hilar cholangiocarcinoma. Nucl Med Commun, 2020, 41(11): 1128-1135.
51.	Rassam F, Olthof PB, Richardson H, et al. Practical guidelines for the use of technetium-99m mebrofenin hepatobiliary scintigraphy in the quantitative assessment of liver function. Nucl Med Commun, 2019, 40(4): 297-307.
52.	de Graaf W, van Lienden KP, Dinant S, et al. Assessment of future remnant liver function using hepatobiliary scintigraphy in patients undergoing major liver resection. J Gastrointest Surg, 2010, 14(2): 369-378.
53.	Tomassini F, D’Asseler Y, Linecker M, et al. Hepatobiliary scintigraphy and kinetic growth rate predict liver failure after ALPPS: a multi-institutional study. HPB (Oxford), 2020, 22(10): 1420-1428.
54.	Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
55.	Wang K, Mamidipalli A, Retson T, et al. Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Radiol Artif Intell, 2019, 1(2): 180022.
56.	Choi KJ, Jang JK, Lee SS, 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(3): 688-697.
57.	蔡伟, 方驰华. 影像组学技术在预测肝细胞癌病人手术安全性的应用研究. 广州: 南方医科大学, 2018.
58.	Park H J, Lee S S, Park B, et al. Radiomics analysis of gadoxetic acid-enhanced MRI for staging liver fibrosis. Radiology, 2019, 292(1): 269.
59.	Yang L, Gu D, Wei J, et al. A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer, 2019, 8(5): 373-386.
60.	Zhou Y, He L, Huang Y, et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY), 2017, 42(6): 1695-1704.
61.	Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 2020, 295(2): 328-338.
62.	Park JE, Park SY, Kim HJ, et al. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol, 2019, 20(7): 1124-1137.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. 沈英皓, 孙惠川, 周俭. 肝切除术前肝脏储备功能评估. 中华肝脏外科手术学电子杂志, 2019, 8(6): 469-472.
3. Kokudo N, Takemura N, Hasegawa K, et al. Clinical practice guidelines for hepatocellular carcinoma: The Japan Society of Hepatology 2017 (4th JSH-HCC guidelines) 2019 update. Hepatol Res, 2019, 49(10): 1109-1113.
4. 代鸿华, 张高明, 杨慧林, 等. γ-谷氨酰转移酶对原发性肝细胞癌术后患者预后的预测价值. 癌症进展, 2019, 17(5): 589-591, 616.
5. 陆华泽, 王小波, 黎乐群. 原发性肝癌术前肝储备功能评估方法的研究进展. 中国普通外科杂志, 2020, 29(1): 85-96.
6. Maegawa FB, Shehorn L, Aziz H, et al. Association between noninvasive fibrosis markers and postoperative mortality after hepatectomy for hepatocellular carcinoma. JAMA Netw Open, 2019, 2(1): e187142.
7. Ben Ayed H, Koubaa M, Yaich S, et al. A new combined predicting model using a non-invasive score for the assessment of liver fibrosis in patients presenting with chronic hepatitis B virus infection. Med Mal Infect, 2019, 49(8): 607-615.
8. Hiraoka A, Michitaka K, Kumada T, et al. ALBI score as a novel tool in staging and treatment planning for hepatocellular carcinoma: advantage of ALBI grade for universal assessment of hepatic function. Liver Cancer, 2017, 6(4): 377-379.
9. Na SK, Yim SY, Suh SJ, et al. ALBI versus Child-Pugh grading systems for liver function in patients with hepatocellular carcinoma. J Surg Oncol, 2018, 117(5): 912-921.
10. Huang G, Jiang H, Lin Y, et al. Prognostic value of plasma fibrinogen in hepatocellular carcinoma: a meta-analysis. Cancer Manag Res, 2018, 10: 5027-5041.
11. 李正佐, 李慧, 吴泓. 术前血浆纤维蛋白原和血清白蛋白水平评分对肝细胞癌患者肝切除术后生存的预测价值. 中国普外基础与临床杂志, 2021, 28(2): 181-188.
12. Cai X, Chen Z, Chen J, et al. Albumin-to-alkaline phosphatase ratio as an independent prognostic factor for overall survival of advanced hepatocellular carcinoma patients without receiving standard anti-cancer therapies. J Cancer, 2018, 9(1): 189-197.
13. Li H, Wang L, Chen L, et al. Prognostic value of albumin-to-alkaline phosphatase ratio in hepatocellular carcinoma patients treated with liver transplantation. J Cancer, 2020, 11(8): 2171-2180.
14. Li H, Li J, Wang J, et al. Assessment of liver function for evaluation of long-term outcomes of intrahepatic cholangiocarcinoma: A multi-institutional analysis of 620 patients. Front Oncol, 2020, 10: 525.
15. Greten FR, Grivennikov SI. Inflammation and cancer: Triggers, mechanisms, and consequences. Immunity, 2019, 51(1): 27-41.
16. 冯志强, 郭玉明, 赵君会, 等. 行肝切除术肝细胞癌患者炎性指标水平变化及与预后的相关性. 中华实用诊断与治疗杂志, 2020, 34(9): 891-895.
17. Shelat VG. Role of inflammatory indices in management of hepatocellular carcinoma-neutrophil to lymphocyte ratio. Ann Transl Med, 2020, 8(15): 912.
18. 孙志德, 程利民, 平萍, 等. 基于炎症反应指数评分系统在肝癌患者预后评估中的价值. 中国老年学杂志, 2017, 37(6): 1411-1413.
19. Wang YY, Zhao XH, Ma L, et al. Comparison of the ability of Child-Pugh score, MELD score, and ICG-R15 to assess preoperative hepatic functional reserve in patients with hepatocellular carcinoma. J Surg Oncol, 2018, 118(3): 440-445.
20. Wen X, Yao M, Lu Y, et al. Integration of prealbumin into Child-Pugh classification improves prognosis predicting accuracy in HCC patients considering curative surgery. J Clin Transl Hepatol, 2018, 6(4): 377-384.
21. 黄凤, 高建. 比较改良 Child-Pugh 评分与白蛋白-胆红素评分对肝细胞癌患者肝切除术后的预后的预测价值. 重庆: 重庆医科大学, 2020.
22. Kamath PS, Wiesner RH, Malinchoc M, et al. A model to predict survival in patients with end-stage liver disease. Hepatology, 2001, 33(2): 464-470.
23. 柏科莉, 李妍宏, 王晓. 肝移植预后的影响因素和预测方法的临床进展. 中国普外基础与临床杂志, 2020, 27(3): 383-388.
24. 罗宇君, 张雅敏, 杨龙, 等. 肝部分切除术后连续性监测吲哚菁绿 15 min 滞留率对肝功能不全的评估价值. 临床肝胆病杂志, 2018, 34(5): 1055-1059.
25. 脱红芳, 彭彦辉. 吲哚菁绿在肝切除围手术期的临床应用. 肝胆胰外科杂志, 2020, 32(5): 313-316.
26. Kokudo T, Hasegawa K, Amikura K, et al. Assessment of preoperative liver function in patients with hepatocellular carcinoma—The Albumin-Indocyanine Green Evaluation (ALICE) Grade. PLoS One, 2016, 11(7): e0159530.
27. Russolillo N, Forchino F, Conci S, et al. Validation of the albumin-indocyanine green evaluation model in patients with resected hepatocellular carcinoma and comparison with the albumin-bilirubin score. J Hepatobiliary Pancreat Sci, 2019, 26(1): 51-57.
28. Miyazaki Y, Kokudo T, Amikura K, et al. Albumin-indocyanine green evaluation grading system predicts post-hepatectomy liver failure for biliary tract cancer. Dig Surg, 2019, 36(1): 13-19.
29. Qi X, An M, Wu T, et al. Transient elastography for significant liver fibrosis and cirrhosis in chronic hepatitis B: A meta-analysis. Can J Gastroenterol Hepatol, 2018, 2018: 3406789.
30. 宋雪, 阮冰. 无创影像学评估慢性乙型病毒性肝炎肝纤维化研究进展. 中国实用内科杂志, 2020, 40(5): 433-437.
31. Chen SH, Lai HC, Chiang IP, et al. Performance of acoustic radiation force impulse elastography for staging liver fibrosis in patients with chronic hepatitis C after viral eradication. Clin Infect Dis, 2020, 70(1): 114-122.
32. Liu J, Li Y, Yang X, et al. Comparison of two-dimensional shear wave elastography with nine serum fibrosis indices to assess liver fibrosis in patients with chronic hepatitis B: A prospective cohort study. Ultraschall Med, 2019, 40(2): 237-246.
33. Herrmann E, de Lédinghen V, Cassinotto C, et al. Assessment of biopsy-proven liver fibrosis by two-dimensional shear wave elastography: An individual patient data-based meta-analysis. Hepatology, 2018, 67(1): 260-272.
34. 姚桃月, 陈文娟, 段星星, 等. 超声弹性成像在儿童肝病中的应用进展. 中国医学影像学杂志, 2020, 28(2): 146-149.
35. 杨华, 曾畅, 罗芳, 等. 超声造影检查无创评估放射性肝损伤患者可行性研究. 实用肝脏病杂志, 2020, 23(5): 674-677.
36. Wang DC, Jang HJ, Kim TK. Characterization of indeterminate liver lesions on CT and MRI with contrast-enhanced ultrasound: What is the evidence? AJR Am J Roentgenol, 2020, 214(6): 1295-1304.
37. Zhang Y, Zheng Y, Yang X, et al. Comparison of acoustic structure quantification, transient elastography (fibroscan) and histology in patients with chronic hepatitis B and without moderate to severe hepatic steatosis. Ultrasound Med Biol, 2019, 45(3): 684-692.
38. Asemota J, Saleh M, Igbinovia O, et al. A concise review on current trends in imaging and surgical management of hepatocellular carcinoma. Cureus, 2020, 12(7): e9191.
39. Maruyama M, Yoshizako T, Araki H, et al. Future liver remnant indocyanine green plasma clearance rate as a predictor of post-hepatectomy liver failure after portal vein embolization. Cardiovasc Intervent Radiol, 2018, 41(12): 1877-1884.
40. 容鹏飞, 冯智超, 郭睿, 等. CT 动脉增强分数评估肝硬化患者肝功能水平. 中南大学学报(医学版), 2019, 44(5): 469-476.
41. Yamada S, Shimada M, Morine Y, et al. A new formula to calculate the resection limit in hepatectomy based on Gd-EOB-DTPA-enhanced magnetic resonance imaging. PLoS One, 2019, 14(1): e0210579.
42. Asenbaum U, Kaczirek K, Ba-Ssalamah A, et al. Post-hepatectomy liver failure after major hepatic surgery: not only size matters. Eur Radiol, 2018, 28(11): 4748-4756.
43. Wibmer A, Prusa A M, Nolz R, et al. Liver failure after major liver resection: risk assessment by using preoperative Gadoxetic acid-enhanced 3-T MR imaging. Radiology, 2013, 269(3): 777-786.
44. Costa AF, Tremblay St-Germain A, Abdolell M, et al. Can contrast-enhanced MRI with gadoxetic acid predict liver failure and other complications after major hepatic resection? Clin Radiol, 2017, 72(7): 598-605.
45. Barth BK, Fischer M A, Kambakamba P, et al. Liver-fat and liver-function indices derived from Gd-EOB-DTPA-enhanced liver MRI for prediction of future liver remnant growth after portal vein occlusion. Eur J Radiol, 2016, 85(4): 843-849.
46. Tanoue Y, Nanashima A, Yano K, et al. Significance of the preoperative regional maximal removal rate of technetium-99m-galactosyl human serum albumin in the future remnant liver: a sequential study of regional maximal removal rate of technetium-99m-galactosyl human serum albumin in the whole liver. Nucl Med Commun, 2019, 40(2): 145-152.
47. Nakamura I, Iimuro Y, Hai S, et al. Impaired value of ^99mTc-GSA scintigraphy as an independent risk factor for posthepatectomy liver failure in patients with hepatocellular carcinoma. Eur Surg Res, 2018, 59(1-2): 12-22.
48. Chiba N, Yokozuka K, Ochiai S, et al. The diagnostic value of 99m-Tc GSA scintigraphy for liver function and remnant liver volume in hepatic surgery: a retrospective observational cohort study in 27 patients. Patient Saf Surg, 2018, 12: 15.
49. Tokorodani R, Sumiyoshi T, Okabayashi T, et al. Liver fibrosis assessment using ^99mTc-GSA SPECT/CT fusion imaging. Jpn J Radiol, 2019, 37(4): 315-320.
50. Huang X, Chen Y, Shao M, et al. The value of ^99mTc-labeled galactosyl human serum albumin single-photon emission computerized tomography/computed tomography on regional liver function assessment and posthepatectomy failure prediction in patients with hilar cholangiocarcinoma. Nucl Med Commun, 2020, 41(11): 1128-1135.
51. Rassam F, Olthof PB, Richardson H, et al. Practical guidelines for the use of technetium-99m mebrofenin hepatobiliary scintigraphy in the quantitative assessment of liver function. Nucl Med Commun, 2019, 40(4): 297-307.
52. de Graaf W, van Lienden KP, Dinant S, et al. Assessment of future remnant liver function using hepatobiliary scintigraphy in patients undergoing major liver resection. J Gastrointest Surg, 2010, 14(2): 369-378.
53. Tomassini F, D’Asseler Y, Linecker M, et al. Hepatobiliary scintigraphy and kinetic growth rate predict liver failure after ALPPS: a multi-institutional study. HPB (Oxford), 2020, 22(10): 1420-1428.
54. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
55. Wang K, Mamidipalli A, Retson T, et al. Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Radiol Artif Intell, 2019, 1(2): 180022.
56. Choi KJ, Jang JK, Lee SS, 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(3): 688-697.
57. 蔡伟, 方驰华. 影像组学技术在预测肝细胞癌病人手术安全性的应用研究. 广州: 南方医科大学, 2018.
58. Park H J, Lee S S, Park B, et al. Radiomics analysis of gadoxetic acid-enhanced MRI for staging liver fibrosis. Radiology, 2019, 292(1): 269.
59. Yang L, Gu D, Wei J, et al. A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer, 2019, 8(5): 373-386.
60. Zhou Y, He L, Huang Y, et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY), 2017, 42(6): 1695-1704.
61. Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 2020, 295(2): 328-338.
62. Park JE, Park SY, Kim HJ, et al. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol, 2019, 20(7): 1124-1137.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Research progress and clinical application of preoperative liver function evaluation indexes

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content