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Gastroenterology Research

Review

Volume 15, Number 6, December 2022, pages 297-307

An Open-Access, Interactive Decision-Support Tool to Facilitate Guideline-Driven Care for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide [1, 2]. Between 1990 and 2015, HCC incidence increased by 75% worldwide [3]. Globally, HCC results in over 800,000 annual deaths [4]; in the USA, it is the fastest-rising cause of cancer mortality [5, 6]. A recent study using data from the Global Burden of Disease Study reported that liver cancer deaths increased by 25% from 2010 to 2019, which was likely driven primarily by non-alcoholic steatohepatitis (NASH) and alcohol-related liver disease, the two etiologies with the most rapid increase in age-standardized death rates during this time period [7]. Cirrhosis is the primary risk factor for HCC, with one-third of patients with cirrhosis ultimately developing HCC [3, 8]. Non-alcoholic fatty liver disease (NAFLD) has become the leading cause of chronic liver disease (CLD) in most regions of the world, including the USA [9, 10], where it is the fastest-growing etiology among patients with HCC undergoing liver transplantation (LT) [9, 11]. In addition to disease-specific factors affecting HCC risk, data also demonstrate that disparities in access to care, social support, and lifestyle also contribute to differences in long-term HCC risk and outcomes [9, 12].

While HCC is associated with high morbidity and mortality [3, 11], early detection improves options for potentially curative treatment, which is associated with significantly improved 5-year survival. Conversely, patients diagnosed with advanced stages of HCC survive a median of 1 year [13] and up to 2 years with systemic therapy. Effective implementation of HCC screening and surveillance in patients with cirrhosis is associated with improved early-stage diagnosis of HCC, while sub-optimal HCC surveillance has led to a high proportion of HCC being detected at advanced stages [8]. In addition to sub-optimal HCC surveillance, other gaps along the HCC cascade of care, including delays in accurate HCC diagnosis and accurate tumor staging, as well as timely linkage to appropriate HCC-directed therapies, contribute to poor patient outcomes [10, 11]. The Chronic Liver Disease Foundation (CLDF), a nonprofit educational organization dedicated to increasing awareness of the impact of CLD in the USA, assembled a group of HCC experts, who are also members of the CLDF, to form the HCC Steering Committee. Recognizing unmet needs in the HCC cascade of care, a small working group containing Steering Committee members was assembled and tasked with creating a practical, point-of-care tool to assist healthcare professionals in the surveillance, diagnosis, staging, and treatment of patients with HCC. This review seeks to increase awareness of the existing gaps in the HCC cascade of care by reviewing data on surveillance rates, disparities associated with HCC surveillance, and targeted efforts to improve HCC surveillance. It also presents a digital, decision-support tool for HCC, which incorporates guideline-based recommendations on HCC surveillance and staging systems. The terminology used throughout this review is in accordance with that followed by the American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) (Fig. 1) [14].

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Figure 1. HCC terminology [14]. HCC: hepatocellular carcinoma.

Cancer surveillance programs aim to detect tumors at an early stage when curative options are feasible [6, 15]. Professional societies, including the National Comprehensive Cancer Network [16], European Association for the Study of the Liver (EASL) [17], and American Association for Study of Liver Diseases (AASLD) [18], recommend HCC surveillance every 6 months with abdominal imaging with or without alpha-fetoprotein (AFP) in patients with cirrhosis. However, existing data illustrate sub-optimal HCC surveillance, which translates into poor HCC outcomes. For example, the Hepatitis C Antiviral Long-Term Treatment against Cirrhosis Trial (HALT-C) prospectively collected HCC surveillance data on a cohort of 1,005 patients over a mean follow-up of 6.1 years and found that 31.1% of the patients did not receive consistent surveillance [19]. Another study using data from the Surveillance, Epidemiology, and End Results (SEER) Program-Medicare database, which included 13,714 patients diagnosed with HCC from 2003 through 2013, observed that over 51% of patients with cirrhosis did not receive any screening in the 3 years before an HCC diagnosis and only 6.8% of patients underwent consistent annual screening. After correction for lead- and length-time biases, it was found that a higher proportion of patients with consistent (23%; 95% confidence interval (CI), 21-25%) and inconsistent screening (19%; 95% CI, 19-20%) survived for 3 years, compared to patients without screening (13%; 95% CI, 12-14%) [20]. Most recently, Wolf et al performed a systematic review and meta-analysis of studies published from 2010 through 2018. A total of 29 studies, inclusive of 118,799 patients, met the inclusion criteria. The authors observed that the pooled estimate for HCC surveillance was 24.0% (95% CI, 18.4-30.1%). Furthermore, patients with alcohol-associated or NASH-related cirrhosis and those not followed in subspecialty gastroenterology clinics had the lowest surveillance rates [21].

Data in Table 1 [22-27] support the benefits of early HCC surveillance, which includes improved early-stage tumor detection, curative treatment rates, and improved long-term survival [20, 22-26, 28]. Because there are no data from randomized controlled trials in patients with cirrhosis, the overall value of HCC screening in patients with cirrhosis has been questioned. Singal et al performed a systematic literature review from January 2014 through July 2020 for studies reporting early-stage HCC detection, curative treatment receipt, or overall survival, stratified by HCC surveillance status, among patients with cirrhosis. They identified 59 studies, including 145,396 patients with HCC that was detected by surveillance in 41,052 (28.2%) cases. HCC surveillance was associated with improved early-stage detection (relative risk (RR) 1.86, 95% CI, 1.73 - 1.98; I² = 82%), curative treatment receipt (RR 1.83, 95% CI, 1.69 - 1.97; I² = 75%), and overall survival (hazard ratio 0.67, 95% CI, 0.61 - 0.72; I² = 78%) after adjusting for lead-time bias. Although there was notable heterogeneity in all pooled estimates and little data quantifying potential screening-related harms, the authors concluded that screening is of high value in patients with cirrhosis [29].

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Table 1. Data Demonstrating the Benefits of Early HCC Surveillance and/or Screening

In order to improve HCC surveillance rates and meet individualized patient needs, it is important to understand that surveillance may be impacted by certain factors, which will be explored in this section.

Sub-optimal HCC surveillance is affected by multiple factors. An important and perhaps underrecognized factor that may contribute to lower rates of HCC surveillance is patient distress and/or patient stigma. Surveillance may result in depression or anxiety from the screening process; financial burdens and/or physical harms due to false-positives or inconclusive results; and adverse effects from overdiagnoses [8]. Patient-perceived barriers also negatively impact surveillance rates; in a patient survey, perceived barriers included costs (28.9%), difficulty scheduling (24.1%), and transportation (17.8%). In addition, although these patients had high levels of knowledge about HCC, there was a misconception that surveillance was unnecessary when physical examination and laboratory results were normal [30].

Significant racial and ethnic disparities in HCC surveillance have been reported. For example, existing studies have identified significantly lower rates of HCC surveillance in Black patients when compared with other race/ethnic groups [31-33]. These are concerning findings, given that there are significant racial and ethnic disparities in HCC prognosis in the USA, especially in Black patients, as detailed in Table 2 [33-36]. Black patients demonstrate higher HCC incidence, more advanced tumor stage at diagnosis, lower rates of surgical resection, and higher mortality rates as compared to non-Blacks. These disparities may be attributed to increased risk factors as well as disparities in timely access to healthcare resources and differences in barriers, medical mistrust, and health literacy [33, 37]. Racial and ethnic disparities may also be impacted by socioeconomic status, as the two are often closely intertwined [38]. As detailed in Table 2 [33-36], one study concluded that Black patients with hepatitis C virus (HCV) exposure develop HCC at earlier stages of liver disease than members of other racial groups [34]. Interventions are needed to better understand and reduce disparities in early HCC detection to improve HCC prognosis [31].

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Table 2. HCC Disproportionally Affects Racial and Ethnic Subgroups

Disparities in HCC surveillance and the HCC cascade of care may also be affected by where a patient lives, as those in more rural areas may not have timely access to specialty care or may have difficulty accessing services for HCC surveillance. Wong et al retrospectively evaluated the 2004 - 2017 SEER registry and found that, among men and women, overall HCC incidence was highest in large metropolitan regions and decreased with less populated/more rural regions; however, less populated/more rural regions demonstrated the greatest increase in HCC incidence over time. Investigators also observed higher HCC incidence and higher average annual percent change in HCC incidence among lower income households and higher HCC incidence in lower income groups. These disparities were attributed to differences in risk factors, health-related behaviors, and barriers in access to healthcare services inherent among underserved, rural, immigrant-rich communities [39]. In a separate analysis of the same database, trends in HCC incidence by rural-urban geography and household income and how these surrogate markers of socioeconomic status affect HCC tumor stage at diagnosis and overall survival were evaluated. Investigators found that patients from rural regions and lower-income households had a more advanced tumor stage at diagnosis and significantly higher HCC mortality, likely reflecting suboptimal access to consistent high-quality liver disease care, including HCC surveillance [40]. While the rural-urban disparities in HCC outcomes are complex and likely reflect multiple factors, limited access to specialty care services is one important modifiable factor that could be addressed. An easy to use, digital, decision support tool has the potential to help providers in rural areas with limited access to specialty care to better identify high-risk patients that need HCC surveillance, what surveillance modalities to use, and how frequently to image, and provide guidance on interpretation and follow-up of liver lesions that are identified.

In a retrospective study conducted by Marquardt et al [41], the most common barrier to HCC screening was lack of routine outpatient care before HCC presentation, with nearly two-thirds of patients failing to receive regular care from a primary care provider (PCP) or gastroenterologist. In a separate Veterans Health Administration study that analyzed 26,577 patients with cirrhosis, the strongest predictor that increased the percentage of time up-to-date with HCC surveillance was the number of visits to a specialist (gastroenterologist/hepatologist and/or infectious disease specialist) in the first year after cirrhosis was diagnosed [42]. In other studies, linkage to specialist care was strongly associated with increased surveillance rates [43-47], and this was attributed to the specialist having the ability to focus solely on the liver during visits [47].

As demonstrated throughout this review, low rates of HCC surveillance are multi-factorial and likely reflect complex interactions with patient, provider, and overall health system factors [42].

In this digital age, automation is revolutionizing healthcare by making processes more efficient and effective, thereby improving patient outcomes [48]. Automation empowers practitioners to offer better services to patients, and this has been demonstrated with HCC surveillance. Patients with cirrhosis (n = 355) were prospectively enrolled into a chronic disease management program that integrates nursing-based protocols with automatic reminders when patients are due for surveillance. Patients enrolled in this program between March 2010 and April 2011 were compared to a prior cohort in 2008 - 2009. Automatic reminders of surveillance status demonstrated significant increases in the rate of HCC surveillance among patients with cirrhosis; the results indicated that 331 (93%) had imaging performed for HCC surveillance, compared to 119/160 (74%) patients in the previous cohort (P < 0.001). The most common reasons for failure to undergo surveillance were patients’ lack of insurance and lack of follow-up on studies ordered at outside institutions [49].

Another study examined the efficiency and validity of using automated data to identify HCC cases. Investigators used a cohort of 1,138 veterans with International Classification of Diseases, ninth Revision (ICD-9) codes for HCC during 2005 - 2010 to validate HCC codes and evaluate whether natural language processing by the Automated Retrieval Console (ARC) for document classification improves HCC identification. The HCC ICD-9 code algorithm had a positive predictive value (PPV) of 0.67, sensitivity of 0.95, and specificity of 0.93. For a random subset of 619 patients, 471 pathology reports were identified for 323 patients and 943 radiology reports were identified for 557 patients. The pathology ARC algorithm had a PPV of 0.96, sensitivity of 0.96, and specificity of 0.97. The radiology ARC algorithm had a PPV of 0.75, sensitivity of 0.94, and specificity of 0.68. These results indicate that HCC case identification improved with utilization of automated data in comparison to a combined approach of ICD-9 codes and natural language processing of pathology and radiology reports [50].

HCC surveillance is improved when specialists are involved in the care of patients with cirrhosis. On the other hand, in regions and/or systems where specialists are not readily available (e.g., rural areas) [47], PCPs may have the opportunity to take the lead on implementing effective HCC surveillance. However, existing survey-based studies focused on PCPs indicate that lack of awareness of the HCC surveillance guidelines, sub-optimal knowledge regarding proper HCC surveillance, failure to recognize liver disease in patients that warrant HCC surveillance, and time constraints were observed to be important limiting factors that contributed to provider-specific barriers to timely HCC surveillance [51, 52]. On the contrary, PCPs felt that, if medical societies endorsed evidence-based guidelines for HCC surveillance, they would be motivated to comply [52].

Efforts are needed to improve HCC surveillance. For HCC surveillance to reduce mortality in clinical practice, it must be effectively implemented [52]. Data suggest that decision-support tools for HCC can help standardize the diagnosis, staging, linkage-to-care, and treatment pathways, thereby improving overall patient care and outcomes [4]. The CLDF HCC committee developed a practical, interactive, digital decision-support tool to improve the HCC cascade of care. This web-based tool is designed to assist healthcare professionals in screening and surveillance, tumor staging, and treatment of patients with HCC, based on the following resources: 1) The most recently updated guidance on HCC management from the AASLD and EASL, which deliver data-supported approaches to the diagnosis, staging, and treatment of patients diagnosed with HCC [17, 18]; 2) The American College of Radiology LI-RADS criteria, which standardize terminology, technique, interpretation, reporting, and data collection of liver imaging in patients at risk for or with HCC [53]; 3) Incorporates most commonly used HCC staging systems, including the Barcelona Clinic Liver Cancer (BCLC) staging system [54] and the United Network for Organ Sharing-Down Staging (UNOS-DS) criteria [55].

We aim for this to be a live decision-support tool that will be updated as HCC management guidelines evolve and will also serve as a platform to build out future CLDF HCC initiatives to improve the care of patients with CLDs and HCC.

The HCC screening and surveillance algorithm is depicted in Figure 2 [56]. According to the AASLD guidance, the decision to enter a patient into a surveillance program for HCC is risk-stratified. The AASLD recommends surveillance of adults with cirrhosis as well as certain individuals with chronic hepatitis B virus (HBV) who are determined to be at high risk of HCC [18]. Thus, the first and one of the most important steps in the HCC cascade of care is accurate identification of which patients are eligible for routine HCC surveillance (Fig. 2) [56]. Among patients with cirrhosis or high-risk non-cirrhotic chronic HBV (Asian women over age of 50 years, Asian or Black/African American men over age of 40 years, family history of HCC, co-infection with hepatitis D virus (HDV)), the AASLD recommends surveillance using ultrasound, with or without AFP, every 6 months [18]. If a liver lesion is not detected on ultrasound or measures < 1 cm, or AFP is < 20 ng/mL, continued screening for HCC with abdominal ultrasound examination with or without AFP every 6 months is recommended [18]. If a liver lesion is detected and measures ≥ 1 cm or AFP > 20 ng/mL, further evaluation is required. The AASLD recommends either multiphase computed tomography (CT) or magnetic resonance imaging (MRI), which have similar performance characteristics for initial diagnostic testing. It is important to note that the accuracy of ultrasound results relies on high-quality examinations with adequate visualization and assessment of lesions seen. If ultrasound visualization quality is sub-optimal, subsequent imaging with multi-phase CT or MRI is recommended.

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Figure 2. HCC screening and surveillance algorithm. *For more information on “The Liver Imaging Reporting and Data System (LI-RADS),” visit [56]. AFP: alpha fetoprotein; CLD: chronic liver disease; CT: computed tomography; HBV: hepatitis B virus; HCC: hepatocellular carcinoma; HDV: hepatitis D virus; LR: Liver Imaging Reporting and Data System score; M: malignancy; MRI: magnetic resonance imaging; NC: not categorizable; TIV: tumor in vein.

Once HCC diagnosis is confirmed, accurate tumor staging is needed to help guide the most effective evidenced-based therapy (Fig. 3) [54]. This staging algorithm was adapted from the BCLC staging system and uses variables related to tumor stage, liver functional status/severity of hepatic decompensation, performance status, and cancer-related symptoms, and links each stage with guideline-recommended treatment options [54]. The BCLC staging system incorporates the Eastern Cooperative Oncology Group (ECOG) scale (Table 3) [57] to assess for patient functional status [57].

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Figure 3. HCC staging algorithm [54]. HCC: hepatocellular carcinoma; ALBI: albumin-bilirubin; PS: ECOG performance status.

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Table 3. ECOG Performance Scale [57]

Implementing effective screening is important to diagnose patients with HCC at an earlier stage, which improves eligibility for potentially curative treatment options, such as surgical resection and LT. LT is thought to be the better oncologic option, as it replaces the diseased liver and thus restores normal hepatic function, but it is limited by organ shortages. The Milan criteria [58] remains an important factor in identifying patients with HCC with favorable characteristics associated with good outcomes following LT. However, data have demonstrated that additional factors, beyond tumor size and number, play a role in post-LT outcomes, and even among patients that have HCC beyond Milan criteria at diagnosis, LT is not completely eliminated as a treatment option. (i.e., UNOS downstaging for HCC beyond Milan criteria) [55, 59, 60]. Patients with initial tumor burden meeting UNOS-DS inclusion criteria who achieve successful down-staging to within Milan criteria are eligible to receive automatic approval for Model for End-Stage Liver Disease (MELD) exception for LT whereas patients initially beyond UNOS-DS criteria are considered on a case-by-case basis. Furthermore, additional refinements based on AFP cutoffs may further improve post-LT outcomes [61-63]. Therefore, this staging algorithm recommends considering these additional criteria (Table 4) [55, 58, 61] based on the BCLC stage when making decisions on next steps in certain patients.

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Table 4. Liver Transplantation Criteria in Early to Intermediate Stage HCC Additional Staging Criteria to Consider in BCLC-Staged Patients

These proposed algorithms, based on published literature and an iterative process that included discussions among expert clinicians, and updates to the algorithms, are currently posted on the CLDF website [64]. The algorithms will be updated on a regular basis (i.e., at least annually depending on the publication of relevant updates in the management of HCC) as appropriate and will be used as educational tools for health care providers. As discussed, the current digital, interactive, HCC decision-support tool aims to improve the cascade of care for patients at risk for HCC. The live and real-time version of the HCC tool will allow providers to seamlessly navigate through a decision-making algorithm that integrates HCC screening and surveillance with accurate tumor staging followed by linking tumor stage to recommended HCC-directed therapies based on the BCLC guidance. Future iterations of this interactive tool have enormous potential to link patients with HCC to specific providers that provide the recommended HCC-directed therapies in their respective regions. This next step may further address existing gaps in the HCC cascade of care and reduce HCC treatment disparities.

Acknowledgments

We would like to thank Rachel E. Bejarano, PharmD for providing medical writing assistance.

Financial Disclosure

This manuscript was supported by an unrestricted educational grant to the Chronic Liver Disease Foundation from AstraZeneca and Exelixis, Inc. The selection of the authors and the creation of this manuscript were done independently, and AstraZeneca and Exelixis, Inc. did not play a role.

Conflict of Interest

Robert J. Wong, MD, Consultant: Gilead Sciences; Research funding: Gilead Sciences (to my institution), Exact Sciences (to my institution). Patricia Jones, MD: Gilead Sciences (to my institution), NIH grants that pay some salary support: 1R01MD012565-03, 1U01DK130185-01, 1R01MD017063-01, 1K08CA255413-10A1; V Foundation provides some salary support. Fasiha Kanwal, MD is supported by National Cancer Institute (NCI U01 CA230999, and R01CA186566), Cancer Prevention & Research Institute of Texas grant (RP150587), VA Merit Grant (IIR 21-230-2) and is an investigator at the Veterans Administration Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413), Michael E. DeBakey VA Medical Center, Houston, Texas; Research funding: Gilead Sciences (to my institution), Exact Sciences (to my institution). Amit Singal, MD, Consultant: Genentech, AstraZeneca, Bayer, Eisai, Exelixis, Glycotest, FujiFilm Medical Sciences, Exact Sciences, Roche, Freenome, GRAIL, TARGET RWE; Research funding: supported by NCI U01 CA271888, U01 CA230694, R01 CA212008, R01 CA222900, R01 MD012565, and R01 CA256977. Zobair Younossi, MD, Research funding and/or Consultant: Gilead Sciences, Intercept, BMS, Novo Nordisk, Viking, Terns, Siemens, Shionogi, AbbVie, Merck, and Novartis. Laura Kulik, MD, Advisory role: Exelixis; Consultant: Bayer, Eisai, Genetec, Hepion, Merck; Speaker: Eisai, Roche; Research funding: Glycotest, HCC Target. Neil Mehta, MD, Consultant: FujiFilm Medical Sciences, Exact Sciences; Research funding: Glycotest, TARGET RWE, FujiFilm Medical Sciences, Exact Sciences (all to my institution). Channa Jayasekera, MD, Aijaz Ahmed, MD, and Robert Taglienti have nothing to disclose.

Author Contributions

Robert J. Wong, MD, developed the HCC decision-support tool algorithm and other practical recommendations in the manuscript, co-led the group of expert hepatologists and drafted the proposed outline, identified the most recent updates in HCC data that pertain to the HCC screening and staging, drafted the manuscript, finalized the submitted draft, and revised the resubmitted draft and responded to journal reviewers. Channa Jayasekera, MD, Patricia Jones, MD, Fasiha Kanwal, MD, Amit G. Singal, MD, Zobair Younossi, MD, Laura Kulik, MD, and Neil Mehta, MD reviewed and provided feedback on the HCC decision-support tool algorithm and other practical recommendations in the manuscript, contributed to the most recent updates in HCC data that pertain to the HCC screening and staging, provided expert review and added content to the manuscript, and approved the final draft of the submitted manuscript. Aijaz Ahmed, MD, developed the HCC decision-support tool algorithm and other practical recommendations in the manuscript, co-led the group of expert hepatologists, identified the most recent updates in HCC data that pertain to the HCC screening and staging, drafted the manuscript, and approved the final draft of the submitted manuscript. Robert Taglienti led the programming of the online HCC decision-support tool algorithm with collaboration from the expert hepatologist panel, launched the live online support tool, and approved the final draft of the submitted manuscript.

Data Availability

The authors declare that data supporting what is stated in this paper are available within the article

Abbreviations

ALBI: albumin-bilirubin; AASLD: American Association for Study of Liver Diseases; AFP: alpha-fetoprotein; ARC: Automated Retrieval Console; BCLC: Barcelona Clinic Liver Cancer; CLD: chronic liver disease; CLDF: Chronic Liver Disease Foundation; CT: computed tomography; DS: down-staging; EASL: European Association for the Study of the Liver; ECOG: Eastern Cooperative Oncology Group; HALT-C: Hepatitis C Antiviral Long-Term Treatment against Cirrhosis Trial; HCC: hepatocellular carcinoma; HCV: hepatitis C virus; ICD-9: International Classification of Diseases, Ninth Revision; LI-RADS: American College of Radiology Liver Imaging Reporting and Data System; LRT: locoregional therapy; LT: liver transplantation; MRI: magnetic resonance imaging; MELD: Model for End-Stage Liver Disease; NAFLD: nonalcoholic fatty liver disease; NASH: non-alcoholic steatohepatitis; OS: overall survival; PCP: primary care provider; PS: ECOG performance status; SEER: Surveillance, Epidemiology, and End Results; TTD: total tumor diameter; UNOS: United Network of Organ Sharing; VA: Veterans’ Affairs

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