Reviews

Clinical application of hepatocellular carcinoma prediction models: current challenges and future directions

  • Hang DENG ,
  • Hao. ZHANG
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  • *.School of Medicine,University of Electronic Science and Technology of China,Chengdu 610057,Sichuan,China

Received date: 2024-08-26

  Online published: 2024-12-23

Abstract

Hepatocellular carcinoma (HCC) is a prevalent global malignancy characterized by inconspicuous early clinical manifestations; the majority of patients have already progressed to an advanced stage by the time symptoms and signs become apparent, thereby missing the opportunity for surgical intervention. Despite surgical interventions, early recurrence and unfavorable prognoses are common, while subsequent treatment options remain limited, contributing to an elevated HCC-associated mortality rate. The increasing awareness and emphasis on HCC prevention and treatment have stimulated the development of numerous predictive models in recent years. This review aims to discuss the current state of research on HCC predictive models, comparing the strengths and limitations of widely recognized models, and evaluating their applicability in clinical settings with an ultimate goal of promoting their integration into clinical practice for improved outcomes in managing HCC.

Cite this article

Hang DENG , Hao. ZHANG . Clinical application of hepatocellular carcinoma prediction models: current challenges and future directions[J]. The Journal of Practical Medicine, 2024 , 40(24) : 3561 -3567 . DOI: 10.3969/j.issn.1006-5725.2024.24.020

References

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[J]. CA Cancer J Clin, 2018,68(6):394-424. doi:10.3322/caac.21492
2 鲜林峰, 方乐天, 刘文斌, 等. 原发性肝癌流行现状、主要发病机制及防控策略[J]. 中国癌症防治杂志, 2022,14(3):320-328.
3 YANG J D, HAINAUT P, GORES G J, et al. A global view of hepatocellular carcinoma: trends, risk, prevention and management[J]. Nat Rev Gastroenterol Hepatol, 2019,16(10):589-604. doi:10.1038/s41575-019-0186-y
4 SINGAL A G, KANWAL F, LLOVET J M. Global trends in hepatocellular carcinoma epidemiology: Implications for screening, prevention and therapy[J]. Nat Rev Clin Oncol, 2023,20(12):864-884. doi:10.1038/s41571-023-00825-3
5 JOHNSON P, ZHOU Q, DAO D Y, et al. Circulating biomarkers in the diagnosis and management of hepatocellular carcinoma[J]. Nat Rev Gastroenterol Hepatol, 2022,19(10):670-681. doi:10.1038/s41575-022-00620-y
6 European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma[J]. J Hepatol, 2018,69(1):182-236.
7 COLLINS G S, REITSMA J B, ALTMAN D G, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement[J]. BMJ, 2015,350:g7594. doi:10.1136/bmj.g7594
8 ALBA A C, AGORITSAS T, WALSH M, et al. Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature[J]. JAMA, 2017,318(14):1377-1384. doi:10.1001/jama.2017.12126
9 STEYERBERG E W, VERGOUWE Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation[J]. Eur Heart J, 2014,35(29):1925-1931. doi:10.1093/eurheartj/ehu207
10 JOHNSON P J, PIRRIE S J, COX T F, et al. The detection of hepatocellular carcinoma using a prospectively developed and validated model based on serological biomarkers[J]. Cancer Epidemiol Biomarkers Prev, 2014,23(1):144-153. doi:10.1158/1055-9965.epi-13-0870
11 李鸿江. 基于血清学标志物的肝癌风险预测模型研究[D]. 北京:北京化工大学, 2022. doi:10.3760/cma.j.cn114452-20220315-00150
12 HANIF H, ALI M J, SUSHEELA A T, et al. Update on the applications and limitations of alpha-fetoprotein for hepatocellular carcinoma[J]. World J Gastroenterol, 2022,28(2):216-229. doi:10.3748/wjg.v28.i2.216
13 ZHANG Y S, CHU J H, CUI S X, et al. Des-gamma-carboxy prothrombin (DCP) as a potential autologous growth factor for the development of hepatocellular carcinoma[J]. Cell Physiol Biochem, 2014,34(3):903-915. doi:10.1159/000366308
14 BEST J, BECHMANN L P, SOWA J P, et al. GALAD Score Detects Early Hepatocellular Carcinoma in an International Cohort of Patients With Nonalcoholic Steatohepatitis[J]. Clin Gastroenterol Hepatol, 2020,18(3):728-735. doi:10.1016/j.cgh.2019.11.012
15 GUAN M C, ZHANG S Y, DING Q, et al. The Performance of GALAD Score for Diagnosing Hepatocellular Carcinoma in Patients with Chronic Liver Diseases: A Systematic Review and Meta-Analysis[J]. J Clin Med, 2023,12(3):949. doi:10.3390/jcm12030949
16 SCHOTTEN C, OSTERTAG B, SOWA J P, et al. GALAD Score Detects Early-Stage Hepatocellular Carcinoma in a European Cohort of Chronic Hepatitis B and C Patients[J]. Pharmaceuticals (Basel), 2021,14(8):735. doi:10.3390/ph14080735
17 WANG Y, WANG M, LI H, et al. A male-ABCD algorithm for hepatocellular carcinoma risk prediction in HBsAg carriers[J]. Chin J Cancer Res, 2021,33(3):352-363.
18 SINGAL A G, TAYOB N, MEHTA A, et al. GALAD demonstrates high sensitivity for HCC surveillance in a cohort of patients with cirrhosis[J]. Hepatology, 2022,75(3):541-549. doi:10.1002/hep.32185
19 李慧泉. 福清市高山镇HBV感染者肝癌预测风险GALAD评分相关影响因素的研究[D]. 福州:福建医科大学, 2021.
20 黄晨军, 肖潇, 周琳, 等. 肝癌三项(AFP、AFP-L3%、DCP)与GALAD、类GALAD模型临床应用专家共识[J]. 检验医学, 2023,38(7):607-623.
21 FAN R, PAPATHEODORIDIS G, SUN J, et al. aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis[J]. J Hepatol, 2020,73(6):1368-1378. doi:10.1016/j.jhep.2020.07.025
22 国家卫生健康委办公厅. 原发性肝癌诊疗指南(2022年版)[J]. 浙江实用医学, 2022,27(6):528-536.
23 喻苧. aMAP评分联合肝脏硬度值测定可准确评估慢性乙型肝炎患者抗病毒治疗前后的肝纤维化[D]. 广州:南方医科大学, 2023.
24 FAN R, CHEN L, ZHAO S, et al. Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures[J]. J Hepatol, 2023,79(4):933-944. doi:10.1016/j.jhep.2023.05.039
25 YANG H I, YUEN M F, CHAN H L, et al. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score[J]. Lancet Oncol, 2011,12(6):568-574. doi:10.1016/s1470-2045(11)70077-8
26 WU S, ZENG N, SUN F, et al. Hepatocellular Carcinoma Prediction Models in Chronic Hepatitis B: A Systematic Review of 14 Models and External Validation[J]. Clin Gastroenterol Hepatol, 2021,19(12):2499-2513. doi:10.1016/j.cgh.2021.02.040
27 KIM H Y, LAMPERTICO P, NAM J Y, et al. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B[J]. J Hepatol, 2022,76(2):311-318. doi:10.1016/j.jhep.2021.09.025
28 PAPATHEODORIDIS G, DALEKOS G, SYPSA V, et al. PAGE-B predicts the risk of developing hepatocellular carcinoma in Caucasians with chronic hepatitis B on 5-year antiviral therapy[J]. J Hepatol, 2016,64(4):800-806. doi:10.1016/j.jhep.2015.11.035
29 COSTA A, DA S M, CASTRO R S, et al. PAGE-B and REACH-B Predicts the Risk of Developing Hepatocellular Carcinoma in Chronic Hepatitis B Patients from Northeast, Brazil[J]. Viruses, 2022,14(4):732. doi:10.3390/v14040732
30 GOKCEN P, GUZELBULUT F, ADALI G, et al. Validation of the PAGE-B score to predict hepatocellular carcinoma risk in caucasian chronic hepatitis B patients on treatment[J]. World J Gastroenterol, 2022,28(6):665-674. doi:10.3748/wjg.v28.i6.665
31 YANG H I, YEH M L, WONG G L, et al. Real-world effectiveness from the Asia Pacific Rim liver consortium for HBV risk score for the prediction of hepatocellular carcinoma in chronic hepatitis B patients treated with oral antiviral therapy[J]. J Infect Dis, 2020,221(3):389-399. doi:10.1093/infdis/jiz477
32 PIRATVISUTH T, HOU J, TANWANDEE T, et al. Development and clinical validation of a novel algorithmic score (GAAD) for detecting HCC in prospective cohort studies[J]. Hepatol Commun, 2023,7(11):e0317. doi:10.1097/hc9.0000000000000317
33 POTE N, CAUCHY F, ALBUQUERQUE M, et al. Performance of PIVKA-II for early hepatocellular carcinoma diagnosis and prediction of microvascular invasion[J]. J Hepatol, 2015,62(4):848-854. doi:10.1016/j.jhep.2014.11.005
34 中华人民共和国国家卫生健康委员会医政司. 原发性肝癌诊疗指南(2024年版)[J].中国实用外科杂志,2024,44(4):361-386. doi:10.3877/cma.j.issn.2095-3232.2024.04.001
35 YANG T, XING H, WANG G, et al. A Novel Online Calculator Based on Serum Biomarkers to Detect Hepatocellular Carcinoma among Patients with Hepatitis B[J]. Clin Chem, 2019,65(12):1543-1553. doi:10.1373/clinchem.2019.308965
36 周宁杰. 恩替卡韦抗病毒治疗对肝癌HBV感染患者的疗效及其对HBV-DNA和肝功能水平的影响[J]. 抗感染药学, 2019,16(1):113-116.
37 YUEN M F, AHN S H, CHEN D S, et al. Chronic Hepatitis B Virus Infection: Disease Revisit and Management Recommendations[J]. J Clin Gastroenterol, 2016,50(4):286-294. doi:10.1097/mcg.0000000000000478
38 赫捷, 陈万青, 沈洪兵, 等. 中国人群肝癌筛查指南(2022,北京)[J]. 临床肝胆病杂志, 2022,38(8):1739-1758.
39 牛一鸣. 代谢综合征与肝癌早期复发的相关性研究[D]. 银川:宁夏医科大学, 2022.
40 IGUCHI K, SADA R, MATSUMOTO S, et al. DKK1-CKAP4 signal axis promotes hepatocellular carcinoma aggressiveness[J]. Cancer Sci, 2023,114(5):2063-2077. doi:10.1111/cas.15743
41 XU J, AN P, WINKLER C A, et al. Dysregulated microRNAs in Hepatitis B Virus-Related Hepatocellular Carcinoma: Potential as Biomarkers and Therapeutic Targets[J]. Front Oncol, 2020,10:1271. doi:10.3389/fonc.2020.01271
42 ZHOU F, SHANG W, YU X, et al. Glypican-3: A promising biomarker for hepatocellular carcinoma diagnosis and treatment[J]. Med Res Rev, 2018,38(2):741-767. doi:10.1002/med.21455
43 曾繁利, 王东和, 苏锐, 等. AFP、AFP-L3和DKK1联合检测对原发性肝癌的诊断价值[J]. 川北医学院学报, 2020,35(1):130-132.
44 郑睿颖, 刘根焰. 机器学习在感染性疾病临床预测模型中的应用进展[J]. 中国血吸虫病防治杂志, 2023,35(3):317-321.
45 黄良江, 毛德文, 郑景辉, 等. 人工智能在肝性脑病风险预测模型中的应用进展[J]. 实用医学杂志, 2024,40(3):289-294.
46 IOANNOU G N, TANG W, BESTE L A, et al. Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis[J]. JAMA Netw Open, 2020,3(9):e2015626. doi:10.1001/jamanetworkopen.2020.15626
47 HEATON H, FUNG S W. Explainable AI via learning to optimize[J]. Sci Rep, 2023,13(1):10103. doi:10.1038/s41598-023-36249-3
48 SHI Y, ZHU C, QI W, et al. Critical appraisal and assessment of bias among studies evaluating risk prediction models for in-hospital and 30-day mortality after percutaneous coronary intervention: a systematic review[J]. BMJ Open, 2024,14(6):e85930. doi:10.1136/bmjopen-2024-085930
49 OKADA Y, NING Y, ONG M. Explainable artificial intelligence in emergency medicine: an overview[J]. Clin Exp Emerg Med, 2023,10(4):354-362. doi:10.15441/ceem.23.145
50 王梓屹, 简萌, 李彬, 等. 2023年可解释人工智能技术主要发展动向分析[J]. 无人系统技术, 2024,7(2):113-120.
51 DESHPANDE P, RASIN A, TCHOUA R, et al. Biomedical heterogeneous data categorization and schema mapping toward data integration[J]. Front Big Data, 2023,6:1173038. doi:10.3389/fdata.2023.1173038
52 ELMORE J G, LEE C I. Data Quality, Data Sharing, and Moving Artificial Intelligence Forward[J]. JAMA Netw Open, 2021,4(8):e2119345. doi:10.1001/jamanetworkopen.2021.19345
53 姚仁玲, 朱艺璇, 黄睿, 等. 维生素摄入量与代谢相关脂肪性肝病进程的相关性分析[J]. 实用医学杂志, 2024,40(6):820-826.
54 黄庆, 邹旻红, 李旺林, 等. 左右半结肠黏液腺癌术后患者生存特征分析:一项基于SEER数据库的研究[J]. 实用医学杂志, 2021,37(10):1351-1356.
55 吕艺蓁, 孙世权. 2023年度肿瘤单细胞与空间多组学研究进展盘点[J]. 中国癌症防治杂志, 2024,16(1):17-22.
56 AL-KUHALI H A, SHAN M, HAEL M A, et al. Multiview clustering of multi-omics data integration by using a penalty model[J]. BMC Bioinformatics, 2022,23(1):288. doi:10.1186/s12859-022-04826-4
57 刘晓帆, 鲁志. 复杂疾病中多组学多模态数据的生物信息学研究进展[J]. 科学通报, 2024,69(30):4432-4446.
58 SATHYANARAYANAN A, MUELLER T T, ALI M M, et al. Multi-omics data integration methods and their applications in psychiatric disorders[J]. Eur Neuropsychopharmacol, 2023,69:26-46. doi:10.1016/j.euroneuro.2023.01.001
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