Abstract
Patients with cirrhosis are at the highest risk to develop hepatocellular carcinoma (HCC), with a variable annual risk of 1–8%. Currently, biannual abdominal ultrasound (USG) with or without alpha fetoprotein (AFP) is the recommended HCC surveillance strategy of major professional liver societies for all cirrhotic patients. However, the effectiveness of USG and AFP has been a sprawling subject of debate due to conflicting results and the low quality of the evidence. The role of cross-sectional imaging is controversial due to potential harm and cost-effectiveness concerns. Several novel serum biomarkers have been introduced for HCC screening, but have yet to be validated for various geographic regions. A risk-stratified algorithm is needed to increase the yield of HCC surveillance by distinguishing a high-risk group that requires more intense screening with cross-sectional imaging and serum biomarkers, and a low-risk group, where the standard surveillance strategy is continued. In this review, the strengths and concerns related to standard USG-based surveillance strategy are discussed, as well as efforts to increase the effectiveness of surveillance.
Introduction
Hepatocellular carcinoma (HCC) is the fifth most common cancer and the second leading cause of cancer-related deaths globally.[1] The most commonly established etiologies for the development of HCC are chronic hepatitis B virus (HBV) infection, hepatitis C virus (HCV) infection, excessive alcohol consumption, and non-alcoholic fatty liver disease. In 90% of cases, these risk factors lead to cirrhosis before HCC development, but there is a small proportion (≈10%) of cases of HCC that occur in a non-cirrhotic liver.[2] Patients with cirrhosis are at the highest risk of developing HCC, with a variable annual risk of 1–8%.[3] Several observational cohort studies in patients with cirrhosis, and two large, randomized, controlled trials in patients with HBV have demonstrated that HCC surveillance can lead to discovery of HCC at an earlier stage, potentially curative treatment, and improved survival compared with patients who present symptomatically or are diagnosed incidentally.[4–6] Based on these data, the European Association for the Study of the Liver (EASL), the American Association for the Study of the Liver (AASLD), the Asian Pacific Association for the Study of the Liver (APASL), and the National Comprehensive Cancer Network (NCCN) recommend surveillance for at-risk individuals, including all cirrhotic patients, regardless of etiology, and selected subgroups of chronic HBV patients[7–10] (Table 1).
Currently, a biannual abdominal ultrasound (USG), with or without alpha fetoprotein (AFP), is the recommended HCC surveillance strategy of major professional liver societies for at-risk individuals (Table 2). A biannual USG with AFP is also recommended for at-risk individuals in the Turkey Hepatitis B Road Map, which was proposed by the Turkish Association for the Study of the Liver (TASL) in 2009.[11] However, the effectiveness of USG has been a subject of broad debate, due to conflicting results and the low quality of the evidence. The main reasons for the enquiry about USG are due to patient-related factors, such as obesity and the ability to view nodules on the liver in cases of cirrhosis, as well as operator dependency, which results in huge variations in the success of USG across institutions. The addition of AFP to surveillance seems to have been withdrawn from some recommendations and left to the physician’s preference. This review is a discussion of the strengths and concerns of this standard of care surveillance strategy, in addition to efforts to increase the yield of surveillance and diminish cost-effectiveness concerns.
What do we gain from standard biannual USG±AFP surveillance?
The direct aim of any cancer surveillance program is clear: to detect cancer at an early, curable stage (Barcelona Clinic Liver Cancer [BCLC] stage 0 or A), which results in a favorable survival expectancy. Therefore, when a lesion is caught at a stage beyond eligibility for curative treatments (BCLC stage B, C, or D), USG is not considered to have a surveillance-related benefit as it would not have any influence on survival. The sensitivity and specificity of USG for any stage of HCC detection has been reported to exceed 90%. On the other hand, a recent meta-analysis found that USG alone detected early-stage HCC with only a 47% sensitivity rate, and the addition of AFP increased the sensitivity rate to 63%.[12] This may be explained by the higher rate of omission of small lesions in a cirrhotic liver due to the limitations of USG. A prior meta-analysis of 13 prospective cohort studies concluded that AFP had no additional value compared with USG alone.[13] The demonstrated contribution of AFP in the literature may be due to higher advanced-stage HCC detection rates under USG-based surveillance.
The indirect, but actual goal of surveillance is to decrease cancer-related mortality in patients with cirrhosis. A recently published case-control study has demonstrated that neither USG nor AFP decreased HCC-related mortality.[14] The suboptimal performance of USG in reaching the direct and indirect goals of HCC surveillance highlights the need for alternative surveillance strategies. The global acceptance of USG in surveillance relies on the absence of risks, non-invasiveness, and lower costs, which is understandable. Model-based simulation studies have demonstrated that a biannual USG for all cirrhotic patients is cost-effective compared with no surveillance, although the average survival extension was less than 6 months.[15] Despite the contradictions, there is still evidence to suggest the use of AFP in combination with USG for patients with cirrhosis until better surveillance strategies are available. The only subgroup of patients with cirrhosis who are not recommended to undergo a standard surveillance program are those with Child-Pugh Class C cirrhosis, unless they are awaiting liver transplantation, given the low probability of treatment eligibility when HCC occurs.
What is the potential harm?
HCC surveillance with USG±AFP cannot constitute a direct physical harm; however, there is potential downstream harm associated with the diagnostic evaluation process. False negative results are common in USG-based surveillance. Suspicious liver lesions typically undergo subsequent computed tomography (CT) and/or magnetic resonance imaging (MRI), and are followed-up in shorter intervals, which adds radiation exposure, possible contrast injury, and financial burden.[16–18] A biopsy may be performed when the lesion cannot be characterized with these cross-sectional imaging techniques; however, biopsy is associated with risks of bleeding, tumor seeding, and injury to nearby organs.[19] In addition, this process and the follow-up period may entail a significant psychosocial burden for the patient. A recent report has revealed that 75% of patients under surveillance are concerned that they will die from the disease. While not specifically questioning the effect of routine intervals of surveillance, the impact on quality of life is apparent.[20] The potential harm has been weighted in a cirrhosis cohort, and 27.5% of patients were exposed to surveillance-related physical harm, of which 22.8% was USG-related and 11.4% was AFP-related.[21] In our Turkish cirrhotic cohort, we demonstrated that an annual, MRI-based surveillance strategy had a lower (6.5%) physical harm rate, without investigating the financial and psychosocial burden.
Is there any place for cross-sectional imaging?
The role of cross-sectional imaging is controversial. Several studies and meta-analyses have investigated the performance of MRI and CT.[22,23] Generally, there is a trend toward greater success in MRI compared with CT. In a randomized trial, an annual CT had a 62.5% sensitivity rate for the detection of early-stage HCC in the surveillance of patients with cirrhosis, which did not significantly differ from a biannual USG.[24] In addition to the lack of demonstrated benefits, CT-based surveillance is restricted due to its physical risks, including radiation exposure and contrast-induced nephrotoxicity.[16,17] Another study conducted to compare biannual, liver-specific, contrast-enhanced MRI and USG has shown that a biannual MRI had a sensitivity of 83.7% in the detection of early-stage HCC, whereas it was only 25.6% in the biannual USG arm.[25] Although a biannual MRI has demonstrated satisfactory results in the literature, the main barriers for MRI use in surveillance programs have been concerns with regard to cost-effectiveness, contraindications, long scan times, and limited availability.[18] Abbreviated-protocol screening MRI, which was proposed as a shorter version of conventional MRI screening, has shown comparable results to complete-protocol diagnostic MRI and made MRI a more assertive and cost-effective tool as a candidate for HCC surveillance.[26,27] Furthermore, the cost-effectiveness of a biannual MRI in the HCC surveillance of patients with cirrhosis has recently been proven using a cohort-based Markov model.[28] The Liver Imaging Reporting and Data System (LI-RADS) was developed to standardize the reporting and increase the diagnostic specificity of HCC using CT or MRI. The latest (2018) version of LI-RADS, which was first supported and endorsed by the American College of Radiology in 2011 and was integrated into the latest HCC guidelines of AASLD in 2018, helps radiologists to standardize the reporting of liver lesions and helps clinicians to optimize the management of liver lesions detected during surveillance.[8,29] In a recent study conducted by our group, we evaluated the efficacy of an annual contrast-enhanced MRI as an HCC-surveillance tool. In our cirrhotic cohort of 294 patients with consistent annual surveillance with MRI, we demonstrated satisfactory performance of MRI in the surveillance of HCC in terms of detecting most of the lesions at earlier, curable stages (85.8%), and observed high sensitivity and specificity (sensitivity: 83.3% and 80%; specificity: 95.4% and 91.4%, respectively for detecting early and very early-stage HCC) with no additional benefit from biannual AFP.[30]
Contrast-enhanced ultrasound (CEUS) has been proposed in the last decade as another advantageous radiological tool for surveillance. The examination is performed by injecting intravenous, microbubble contrast agents without renal excretion and has the advantage of real-time, dynamic imaging. The CEUS technique is generally considered safe and well-tolerated, and may even be used in renal failure patients. Use in clinical practice is suggested by the latest version of the EASL guidelines on the management of HCC as part of a work-up of focal liver lesions and as a diagnostic tool for HCC, where available.[7] CEUS demonstrated superior performance to conventional USG in detecting early HCC in a head-to-head, prospective, multicenter, randomized, controlled trial, and has a sensitivity rate of 85% and a specificity of 91% for HCC detected in a cirrhotic liver.[31,32] CEUS appears to be a more sensitive tool than non-contrast USG for HCC screening, where available. However, it still has several limitations, such as a lack of specificity on differentiation between HCC and intrahepatic cholangiocarcinoma, which occurs in 2–5% of all new nodules in cirrhosis.[33,34] For this reason, a dedicated Contrast Enhanced Ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) was developed in 2016, which uses the size, type, presence of washout, degree of arterial phase enhancement, and the timing and degree of washout to categorize focal liver lesions in patients at high risk for HCC.[35] The CEUS LI-RADS algorithm has been reported to be highly specific for the diagnosis of HCC, and may help CEUS take the lead in the race among radiological tools for HCC surveillance.[36]
Considering all of the limitations with standard, non-contrast USG, a better radiological surveillance tool is needed. In order to overcome the financial burden and increase the yield, the inclusion of advanced imaging tools to surveillance can be narrowed to selected patients with a higher risk of HCC development.
Are there any promising serological biomarkers to be used in HCC surveillance?
Novel biomarkers, such as biochemical metabolites, proteins, and RNA, have been introduced in the screening of many cancer types for early detection and prognosis determination. AFP has been widely accepted and used in combination with USG for HCC surveillance. However, AFP is not able to detect early HCC in 80% of cases, which made its usage in surveillance controversial. Another criticism of current biomarkers, especially AFP, appears to be drawn from its inconsistent performance characteristics across various etiologies of chronic liver disease and different regions. Thus, there has been interest in developing novel biomarkers with more success in early detection that could be used in different regions. The future of biomarker screening is promising, with numerous other molecules under research, such as osteopontin, alfa fetoprotein-L3 (AFP-L3), des-gamma-carboxy prothrombin (DCP), glypican-3 (GCP3), and alpha-L-fucosidase 1.
Since the conventional liver tissue biopsy is an invasive procedure and representative of only a small portion of the tumor, it is unable to represent tumor heterogeneity. Over the years, a new diagnostic method, liquid biopsy, has emerged as a promising tool for both detecting early HCC and determining prognosis and molecular profiling. Liquid biopsy has the advantages of being quick, easy obtainable, minimally invasive, and representative of a comprehensive tissue profile.[37] Liquid biopsy techniques are primarily based on detecting circulating tumor cells, micro RNA, tumor cell-free DNA, tumor-derived/associated extracellular vesicles, and metabolites and proteins.[38] A large number of liquid biopsy biomarkers have been studied in the early detection of HCC, and there is a suggestion that they could be promising biomarkers and an attractive option for AFP-negative early HCC; however, these candidate biomarkers must be internationally validated using methodologies easily transferable to clinical settings.
Is a one-size-fits-all strategy practical for surveillance of HCC in cirrhotic patients?
The risk of developing HCC is not uniform, and may increase due to underlying parameters. However, despite our increasing awareness of prognostic and etiological risk factors, most patients present with advanced stages at the time of diagnosis, and fewer than 20% are eligible for curative treatment options.[39] The most critical game-changer intervention for the course of HCC remains improving the rate of detection at an early stage. To achieve this goal, the most accurate approach may be to optimize screening strategies and better reveal the higher risk patients who require more intense surveillance with better imaging modalities and/or serum biomarkers. This opinion is supported by a recent report examining the cost-effectiveness of risk-stratified HCC surveillance in which the method outperformed the currently recommended non-stratified, biannual USG for all patients, according to Markov decision-analytic modeling.[40] Our risk-stratified algorithm for the surveillance of HCC among cirrhotic patients in whom status has yet to be determined is illustrated in Figure 1.
A number of scoring systems have been developed to predict the risk of HCC, mainly focusing on chronic HBV[41–48] and HCV;[49–53] only a few have targeted all cirrhotic patients, regardless of etiology[54–56] (Table 3). Among them, the REACH-B (Risk Estimation for Hepatocellular Carcinoma in Chronic Hepatitis B), the PAGE-B (Platelets, Age, Gender in Chronic Hepatitis B), and the HALT-C (Hepatitis C Antiviral Long-Term Treatment against Cirrhosis) are the most popular and externally validated. In 2014, the ADRESS (age, diabetes, race, etiology of cirrhosis, sex, and severity) risk model, which uses variables of liver dysfunction, was developed to predict the 1-year HCC risk. ADRESS-HCC only categorized etiologies into three groups (autoimmune, alcohol/metabolic, viral), which proved unsatisfactory to weigh the different etiologies and treatment response status. In 2017, THRI (Toronto Hepatocellular Carcinoma Risk Index) was developed to predict the 10-year HCC risk using simple clinical and laboratory parameters (age, gender, etiology, platelets).[56] THRI weighed etiologies in more detail, including the sustained virological response status of HCV-related cirrhosis. The performance of THRI has been demonstrated in three cohorts from different regions (Canada, Netherlands, and China).[56,57] THRI showed similar efficacy to predict HCC development in these three cohorts. We recently validated the efficacy of THRI in our Turkish cirrhotic cohort and found a similar area under the receiver operating characteristic (AUROC) curve value to the Canadian, Dutch, and Chinese cohorts, and very interestingly, determined the same optimal cut-off value of 240 to distinguish the high-risk HCC group.[58] This emerging evidence encourages the use of THRI and/or other validated scoring systems that apply the combination of clinical and laboratory variables to the risk-stratified surveillance algorithm.
In recent years, combinations of available clinical and laboratory variables have been evaluated to develop HCC risk-predictive scores, although the performance is somewhat limited and has yet to be adopted in clinical practice. To further adjust HCC prediction, the combination of three biomarkers (AFP, AFP-L3, and DCP) with sex and age was proposed as a diagnostic model (GALAD),[59] and later had better results when combined with USG (presence of solid lesion on surveillance) (GALADUS).[60] The GALAD score demonstrated remarkable performance in surveillance with an AUROC value of 0.95 (95% confidence interval [CI]: 0.93–97), a sensitivity of 92% and a specificity of 85%. The performance of GALAD for early HCC detection remained high as well (AUROC: 0.92; 95% CI: 0.88–0.96; sensitivity 92%, specificity 79%). Another risk score, the Doylestown algorithm, incorporates biomarkers (AFP and fucosylated biomarkers) and relevant clinical variables (age, gender, and ALT).[61] To supplement inadequate clinical scores, new molecular biomarkers have been investigated. Several germline single-nucleotide polymorphisms in epidermal growth factor and myeloperoxidase have been identified and validated as an HCC risk predictor, and a liver-derived 186-gene signature has been proposed as a prognostic parameter.[62–67] Although they have been considered for patients with cirrhosis in most need of surveillance, all are far from being in widespread use due to heterogeneity in etiological and differential characteristics of HCC globally. Validation studies from different geographic regions are required before further affirmative comments can be made for these combined clinical and serological prediction models.
Conclusion
Our knowledge of the cost-effectiveness of performing HCC surveillance is founded on model-based studies. Since follow-up without surveillance is not an option for trials, surveillance has become the worldwide standard of care. Despite the questionable quality of evidence, the literature suggests performing surveillance. A standard of care with a biannual USG±AFP is premature, and it is not rational to implement the same strategy for every cirrhotic patient. The key to increasing the yield and cost-effectiveness lies in risk-stratified surveillance strategy. There is growing evidence for and progress in the integration of cross-sectional imaging modalities and serum biomarkers to HCC surveillance. The evolving HCC-risk stratification models may help us to tailor a surveillance strategy and integrate costly tools in selected patients. Further studies are needed to better stratify the risk for HCC and to determine improved surveillance strategies, including imaging and biomarkers.
Peer-review: Externally peer-reviewed.
Author Contributions: Concept – COD, OCO; Design – COD, OCO; Supervision – OCO; Literature Search – COD; Writing – COD; Critical Reviews – OCO.
Conflict of Interest: The authors have no conflict of interest to declare.
Financial Disclosure: The authors declared that this study has received no financial support.
References
1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136(5):E359-386. https://doi.org/10.1002/ijc.29210 PMid:25220842 | ||||
2. Yang JD, Kim WR, Coelho R, Mettler TA, Benson JT, Sanderson SO, et al. Cirrhosis is present in most patients with hepatitis B and hepatocellular carcinoma. Clin Gastroenterol Hepatol 2011;9(1):64-70. https://doi.org/10.1016/j.cgh.2010.08.019 PMid:20831903 PMCid:PMC3951426 | ||||
3. El-Serag HB. Hepatocellular carcinoma. N Engl J Med 2011;365(12):1118-1127. https://doi.org/10.1056/NEJMra1001683 PMid:21992124 | ||||
4. Kanwal F, Singal AG. Surveillance for Hepatocellular Carcinoma: Current Best Practice and Future Direction. Gastroenterology 2019;157(1):54-64. https://doi.org/10.1053/j.gastro.2019.02.049 PMid:30986389 PMCid:PMC6636644 | ||||
5. Zhang BH, Yang BH, Tang ZY. Randomized controlled trial of screening for hepatocellular carcinoma. J Cancer Res Clin Oncol 2004;130(7):417-422. https://doi.org/10.1007/s00432-004-0552-0 PMid:15042359 | ||||
6. Yang B, Zhang B, Xu Y, Wang W, Shen Y, Zhang A, et al. Prospective study of early detection for primary liver cancer. J Cancer Res Clin Oncol 1997;123(6):357-360. https://doi.org/10.1007/BF01438313 PMid:9222303 | ||||
7. European Association for the Study of the Liver. Electronic address: easloffice@easloffice.eu; European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol 2018;69(1):182-236. | ||||
8. Marrero JA, Kulik LM, Sirlin CB, Zhu AX, Finn RS, Abecassis MM, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology 2018;68(2):723-750. https://doi.org/10.1002/hep.29913 PMid:29624699 | ||||
9. Omata M, Cheng AL, Kokudo N, Kudo M, Lee JM, Jia J, et al. Asia-Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatol Int 2017;11(4):317-370. https://doi.org/10.1007/s12072-017-9799-9 PMid:28620797 PMCid:PMC5491694 | ||||
10. National Cancer Institute (NIH). Liver (Hepatocellular) Cancer Screening (PDQ®)-Health Professional Version. Available at: https://www.cancer.gov/types/liver/hp/liver-screening-pdq. Accessed Jan 9, 2018. | ||||
11. Türk Karaciğer Araştırmaları Derneği. Türkiye Hepatit B Yol Haritası. Available at: https://www.vhsd.org/tr/files/download/p1be3700991q4hlip1fjbrgqj64.pdf. | ||||
12. Tzartzeva K, Obi J, Rich NE, Parikh ND, Marrero JA, Yopp A, et al. Surveillance Imaging and Alpha Fetoprotein for Early Detection of Hepatocellular Carcinoma in Patients With Cirrhosis: A Meta-analysis. Gastroenterology 2018;154(6):1706-1718.e1. https://doi.org/10.1053/j.gastro.2018.01.064 PMid:29425931 PMCid:PMC5927818 | ||||
13. Singal A, Volk ML, Waljee A, Salgia R, Higgins P, Rogers MA, et al. Meta-analysis: surveillance with ultrasound for early-stage hepatocellular carcinoma in patients with cirrhosis. Aliment Pharmacol Ther 2009;30(1):37-47. https://doi.org/10.1111/j.1365-2036.2009.04014.x PMid:19392863 PMCid:PMC6871653 | ||||
14. Moon AM, Weiss NS, Beste LA, Su F, Ho SB, Jin GY, et al. No Association Between Screening for Hepatocellular Carcinoma and Reduced Cancer-Related Mortality in Patients With Cirrhosis. Gastroenterology 2018;155(4):1128-1139.e6. https://doi.org/10.1053/j.gastro.2018.06.079 PMid:29981779 PMCid:PMC6180323 | ||||
15. Andersson KL, Salomon JA, Goldie SJ, Chung RT. Cost effectiveness of alternative surveillance strategies for hepatocellular carcinoma in patients with cirrhosis. Clin Gastroenterol Hepatol 2008;6(12):1418-1424. https://doi.org/10.1016/j.cgh.2008.08.005 PMid:18848905 PMCid:PMC4340842 | ||||
16. Lee CI, Haims AH, Monico EP, Brink JA, Forman HP. Diagnostic CT scans: assessment of patient, physician, and radiologist awareness of radiation dose and possible risks. Radiology 2004;231(2):393-398. https://doi.org/10.1148/radiol.2312030767 PMid:15031431 | ||||
17. Barrett BJ, Carlisle EJ. Metaanalysis of the relative nephrotoxicity of high- and low-osmolality iodinated contrast media. Radiology 1993;188(1):171-178. https://doi.org/10.1148/radiology.188.1.8511292 PMid:8511292 | ||||
18. Dill T. Contraindications to magnetic resonance imaging: non-invasive imaging. Heart 2008;94(7):943-948. https://doi.org/10.1136/hrt.2007.125039 PMid:18552230 | ||||
19. Rockey DC, Caldwell SH, Goodman ZD, Nelson RC, Smith AD; American Association for the Study of Liver Diseases. Liver biopsy. Hepatology 2009;49(3):1017-1044. https://doi.org/10.1002/hep.22742 PMid:19243014 | ||||
20. Farvardin S, Patel J, Khambaty M, Yerokun OA, Mok H, Tiro JA, et al. Patient-reported barriers are associated with lower hepatocellular carcinoma surveillance rates in patients with cirrhosis. Hepatology 2017;65(3):875-884. https://doi.org/10.1002/hep.28770 PMid:27531684 PMCid:PMC5568252 | ||||
21. Atiq O, Tiro J, Yopp AC, Muffler A, Marrero JA, Parikh ND, et al. An assessment of benefits and harms of hepatocellular carcinoma surveillance in patients with cirrhosis. Hepatology 2017;65(4):1196-1205. https://doi.org/10.1002/hep.28895 PMid:27775821 PMCid:PMC5659110 | ||||
22. Hanna RF, Miloushev VZ, Tang A, Finklestone LA, Brejt SZ, Sandhu RS, et al. Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdom Radiol (NY) 2016;41(1):71-90. https://doi.org/10.1007/s00261-015-0592-8 PMid:26830614 | ||||
23. Lee YJ, Lee JM, Lee JS, Lee HY, Park BH, Kim YH, et al. Hepatocellular carcinoma: diagnostic performance of multidetector CT and MR imaging-a systematic review and meta-analysis. Radiology 2015;275(1):97-109. https://doi.org/10.1148/radiol.14140690 PMid:25559230 | ||||
24. Pocha C, Dieperink E, McMaken KA, Knott A, Thuras P, Ho SB. Surveillance for hepatocellular cancer with ultrasonography vs. computed tomography -- a randomised study. Aliment Pharmacol Ther 2013;38(3):303-312. https://doi.org/10.1111/apt.12370 PMid:23750991 | ||||
25. Kim SY, An J, Lim YS, Han S, Lee JY, Byun JH, et al. MRI With Liver-Specific Contrast for Surveillance of Patients With Cirrhosis at High Risk of Hepatocellular Carcinoma. JAMA Oncol 2017;3(4):456-463. https://doi.org/10.1001/jamaoncol.2016.3147 PMid:27657493 PMCid:PMC5470420 | ||||
26. Khatri G, Pedrosa I, Ananthakrishnan L, de Leon AD, Fetzer DT, Leyendecker J, et al. Abbreviated-protocol screening MRI vs. complete-protocol diagnostic MRI for detection of hepatocellular carcinoma in patients with cirrhosis: An equivalence study using LI-RADS v2018. J Magn Reson Imaging 2020;51(2):415-425. https://doi.org/10.1002/jmri.26835 PMid:31209978 | ||||
27. Lee JY, Huo EJ, Weinstein S, Santos C, Monto A, Corvera CU, et al. Evaluation of an abbreviated screening MRI protocol for patients at risk for hepatocellular carcinoma. Abdom Radiol (NY) 2018;43(7):1627-1633. https://doi.org/10.1007/s00261-017-1339-5 PMid:29018942 | ||||
28. Kim HL, An J, Park JA, Park SH, Lim YS, Lee EK. Magnetic Resonance Imaging Is Cost-Effective for Hepatocellular Carcinoma Surveillance in High-Risk Patients With Cirrhosis. Hepatology 2019;69(4):1599-1613. https://doi.org/10.1002/hep.30330 PMid:30365164 | ||||
29. American College of Radiology. Liver Imaging Reporting and Data System. Available at: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS. Accessed May 21, 2018. | ||||
30. Demirtas CO, Gunduz F, Tuney D, Baltacioglu F, Kani HT, Bugdayci O, et al. Annual contrast-enhanced magnetic resonance imaging is highly effective in the surveillance of hepatocellular carcinoma among cirrhotic patients. Eur J Gastroenterol Hepatol 2020;32(4):517-523. https://doi.org/10.1097/MEG.0000000000001528 PMid:31524775 | ||||
31. Kudo M, Ueshima K, Osaki Y, Hirooka M, Imai Y, Aso K, et al. B-Mode Ultrasonography versus Contrast-Enhanced Ultrasonography for Surveillance of Hepatocellular Carcinoma: A Prospective Multicenter Randomized Controlled Trial. Liver Cancer 2019;8(4):271-280. https://doi.org/10.1159/000501082 PMid:31602370 PMCid:PMC6738204 | ||||
32. Zhang J, Yu Y, Li Y, Wei L. Diagnostic value of contrast-enhanced ultrasound in hepatocellular carcinoma: a meta-analysis with evidence from 1998 to 2016. Oncotarget 2017;8(43):75418-75426. https://doi.org/10.18632/oncotarget.20049 PMid:29088877 PMCid:PMC5650432 | ||||
33. Vilana R, Forner A, Bianchi L, García-Criado A, Rimola J, de Lope CR, et al. Intrahepatic peripheral cholangiocarcinoma in cirrhosis patients may display a vascular pattern similar to hepatocellular carcinoma on contrast-enhanced ultrasound. Hepatology 2010;51(6):2020-2029. https://doi.org/10.1002/hep.23600 PMid:20512990 | ||||
34. Wilson SR, Jang HJ, Kim TK, Iijima H, Kamiyama N, Burns PN. Real-time temporal maximum-intensity-projection imaging of hepatic lesions with contrast-enhanced sonography. AJR Am J Roentgenol 2008;190(3):691-695. https://doi.org/10.2214/AJR.07.3116 PMid:18287440 | ||||
35. American College of Radiology. CEUS LI-RADS® v2017. Available at: https:// www.acr.org/-/media/ACR/Images/Clinical-Resources/RADS/LIRADS/CEUSv2017Core.png. Accessed Oct 18, 2018. | ||||
36. Aubé C, Oberti F, Lonjon J, Pageaux G, Seror O, N'Kontchou G, et al. EASL and AASLD recommendations for the diagnosis of HCC to the test of daily practice. Liver Int 2017;37(10):1515-1525. https://doi.org/10.1111/liv.13429 PMid:28346737 | ||||
37. Ye Q, Ling S, Zheng S, Xu X. Liquid biopsy in hepatocellular carcinoma: circulating tumor cells and circulating tumor DNA. Mol Cancer 2019;18(1):114. https://doi.org/10.1186/s12943-019-1043-x PMid:31269959 PMCid:PMC6607541 | ||||
38. Macias RIR, Kornek M, Rodrigues PM, Paiva NA, Castro RE, Urban S, et al. Diagnostic and prognostic biomarkers in cholangiocarcinoma. Liver Int 2019;39 Suppl 1:108-122. https://doi.org/10.1111/liv.14090 PMid:30843325 | ||||
39. Bruix J, Sherman M; American Association for the Study of Liver Diseases. Management of hepatocellular carcinoma: an update. Hepatology 2011;53(3):1020-1022. https://doi.org/10.1002/hep.24199 PMid:21374666 PMCid:PMC3084991 | ||||
40. Goossens N, Singal AG, King LY, Andersson KL, Fuchs BC, Besa C, et al. Cost-Effectiveness of Risk Score-Stratified Hepatocellular Carcinoma Screening in Patients with Cirrhosis. Clin Transl Gastroenterol 2017;8(6):e101. https://doi.org/10.1038/ctg.2017.26 PMid:28640287 PMCid:PMC5518949 | ||||
41. Yuen MF, Tanaka Y, Fong DY, Fung J, Wong DK, Yuen JC, et al. Independent risk factors and predictive score for the development of hepatocellular carcinoma in chronic hepatitis B. J Hepatol 2009;50(1):80-88. https://doi.org/10.1016/j.jhep.2008.07.023 PMid:18977053 | ||||
42. Wong VW, Chan SL, Mo F, Chan TC, Loong HH, Wong GL, et al. Clinical scoring system to predict hepatocellular carcinoma in chronic hepatitis B carriers. J Clin Oncol 2010;28(10):1660-1665. https://doi.org/10.1200/JCO.2009.26.2675 PMid:20194845 | ||||
43. 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(4):800-806. https://doi.org/10.1016/j.jhep.2015.11.035 PMid:26678008 | ||||
44. Kim JH, Kim YD, Lee M, Jun BG, Kim TS, Suk KT, et al. Modified PAGE-B score predicts the risk of hepatocellular carcinoma in Asians with chronic hepatitis B on antiviral therapy. J Hepatol 2018;69(5):1066-1073. https://doi.org/10.1016/j.jhep.2018.07.018 PMid:30075230 | ||||
45. Wong GL, Chan HL, Wong CK, Leung C, Chan CY, Ho PP, et al. Liver stiffness-based optimization of hepatocellular carcinoma risk score in patients with chronic hepatitis B. J Hepatol 2014;60(2):339-345. https://doi.org/10.1016/j.jhep.2013.09.029 PMid:24128413 | ||||
46. Yang HI, Yuen MF, Chan HL, Han KH, Chen PJ, Kim DY, et al; REACH-B Working Group. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score. Lancet Oncol 2011;12(6):568-574. https://doi.org/10.1016/S1470-2045(11)70077-8 | ||||
47. Lee MH, Yang HI, Liu J, Batrla-Utermann R, Jen CL, Iloeje UH, et al; R.E.V.E.A.L.-HBV Study Group. Prediction models of long-term cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients: risk scores integrating host and virus profiles. Hepatology 2013;58(2):546-554. https://doi.org/10.1002/hep.26385 PMid:23504622 | ||||
48. Lee HW, Yoo EJ, Kim BK, Kim SU, Park JY, Kim DY, et al. Prediction of development of liver-related events by transient elastography in hepatitis B patients with complete virological response on antiviral therapy. Am J Gastroenterol 2014;109(8):1241-1249. https://doi.org/10.1038/ajg.2014.157 PMid:24957159 | ||||
49. Lok AS, Seeff LB, Morgan TR, di Bisceglie AM, Sterling RK, Curto TM, et al; HALT-C Trial Group. Incidence of hepatocellular carcinoma and associated risk factors in hepatitis C-related advanced liver disease. Gastroenterology 2009;136(1):138-148. https://doi.org/10.1053/j.gastro.2008.09.014 PMid:18848939 PMCid:PMC3749922 | ||||
50. El-Serag HB, Kanwal F, Davila JA, Kramer J, Richardson P. A new laboratory-based algorithm to predict development of hepatocellular carcinoma in patients with hepatitis C and cirrhosis. Gastroenterology 2014;146(5):1249-1255.e1. https://doi.org/10.1053/j.gastro.2014.01.045 PMid:24462733 PMCid:PMC3992177 | ||||
51. Chang KC, Hung CH, Lu SN, Wang JH, Lee CM, Chen CH, et al. A novel predictive score for hepatocellular carcinoma development in patients with chronic hepatitis C after sustained response to pegylated interferon and ribavirin combination therapy. J Antimicrob Chemother 2012;67(11):2766-2772. https://doi.org/10.1093/jac/dks269 PMid:22899800 | ||||
52. Masuzaki R, Tateishi R, Yoshida H, Goto E, Sato T, Ohki T, et al. Prospective risk assessment for hepatocellular carcinoma development in patients with chronic hepatitis C by transient elastography. Hepatology 2009;49(6):1954-1961. https://doi.org/10.1002/hep.22870 PMid:19434742 | ||||
53. Ganne-Carrié N, Layese R, Bourcier V, Cagnot C, Marcellin P, Guyader D, et al. Nomogram for individualized prediction of hepatocellular carcinoma occurrence in hepatitis C virus cirrhosis (ANRS CO12 CirVir). Hepatology 2016;64(4):1136-1147. https://doi.org/10.1002/hep.28702 PMid:27348075 | ||||
54. Flemming JA, Yang JD, Vittinghoff E, Kim WR, Terrault NA. Risk prediction of hepatocellular carcinoma in patients with cirrhosis: the ADRESS-HCC risk model. Cancer 2014;120(22):3485-3493. https://doi.org/10.1002/cncr.28832 PMid:25042049 PMCid:PMC4553222 | ||||
55. Liang KH, Ahn SH, Lee HW, Huang YH, Chien RN, Hu TH, et al. A novel risk score for hepatocellular carcinoma in Asian cirrhotic patients: a multicentre prospective cohort study. Sci Rep 2018;8(1):8608. https://doi.org/10.1038/s41598-018-26992-3 PMid:29872158 PMCid:PMC5988718 | ||||
56. 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 2017:S0168-8278(17)32248-32241. | ||||
57. Zhang H, Zhu J, Xi L, Xu C, Wu A. Validation of the Toronto hepatocellular carcinoma risk index for patients with cirrhosis in China: a retrospective cohort study. World J Surg Oncol 2019;17(1):75. https://doi.org/10.1186/s12957-019-1619-3 PMid:31039803 PMCid:PMC6492382 | ||||
58. Demirtas CO, Gunduz F, Kani HT, Keklikkiran C, Alahdab YO, Yilmaz Y, et al. External Validation of Toronto Hepatocellular Carcinoma Risk Index in a Turkish Cirrhotic Cohort. Eur J Gastroenterol Hepatol. 2020;32(7):882-888. https://doi.org/10.1097/MEG.0000000000001685 PMid:32395972 | ||||
59. Johnson PJ, Pirrie SJ, Cox TF, Berhane S, Teng M, Palmer D, et al. The detection of hepatocellular carcinoma using a prospectively developed and validated model based on serological biomarkers. Cancer Epidemiol Biomarkers Prev 2014;23(1):144-153. https://doi.org/10.1158/1055-9965.EPI-13-0870 PMid:24220911 | ||||
60. Yang JD, Addissie BD, Mara KC, Harmsen WS, Dai J, Zhang N, et al. GALAD Score for Hepatocellular Carcinoma Detection in Comparison with Liver Ultrasound and Proposal of GALADUS Score. Cancer Epidemiol Biomarkers Prev 2019;28(3):531-538. https://doi.org/10.1158/1055-9965.EPI-18-0281 PMid:30464023 PMCid:PMC6401221 | ||||
61. Wang M, Devarajan K, Singal AG, Marrero JA, Dai J, Feng Z, et al. The Doylestown Algorithm: A Test to Improve the Performance of AFP in the Detection of Hepatocellular Carcinoma. Cancer Prev Res (Phila) 2016;9(2):172-179. https://doi.org/10.1158/1940-6207.CAPR-15-0186 PMid:26712941 PMCid:PMC4740237 | ||||
62. Goossens N, Sun X, Hoshida Y. Molecular classification of hepatocellular carcinoma: potential therapeutic implications. Hepat Oncol 2015;2(4):371-379. https://doi.org/10.2217/hep.15.26 PMid:26617981 PMCid:PMC4662420 | ||||
63. Goossens N, Bian CB, Hoshida Y. Tailored algorithms for hepatocellular carcinoma surveillance: Is one-size-fits-all strategy outdated? Curr Hepatol Rep 2017;16(1):64-71. https://doi.org/10.1007/s11901-017-0336-z PMid:28337405 PMCid:PMC5358664 | ||||
64. Tanabe KK, Lemoine A, Finkelstein DM, Kawasaki H, Fujii T, Chung RT, et al. Epidermal growth factor gene functional polymorphism and the risk of hepatocellular carcinoma in patients with cirrhosis. JAMA 2008;299(1):53-60. https://doi.org/10.1001/jama.2007.65 PMid:18167406 | ||||
65. Abu Dayyeh BK, Yang M, Fuchs BC, Karl DL, Yamada S, Sninsky JJ, et al; HALT-C Trial Group. A functional polymorphism in the epidermal growth factor gene is associated with risk for hepatocellular carcinoma. Gastroenterology 2011;141(1):141-149. https://doi.org/10.1053/j.gastro.2011.03.045 PMid:21440548 PMCid:PMC3129453 | ||||
66. 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(5):1024-1030. https://doi.org/10.1053/j.gastro.2013.01.021 PMid:23333348 PMCid:PMC3633736 | ||||
67. King LY, Canasto-Chibuque C, Johnson KB, Yip S, Chen X, Kojima K, et al. A genomic and clinical prognostic index for hepatitis C-related early-stage cirrhosis that predicts clinical deterioration. Gut 2015;64(8):1296-1302. https://doi.org/10.1136/gutjnl-2014-307862 PMid:25143343 PMCid:PMC4336233 |