21 minute read
Metastatic Pancreatic Cancer
Special Lecture (VIII)
THE ROLE OF OLAPARIB IN MANAGEMENT OF PATIENTS WITH GERMLINE BRCA-MUTATED METASTATIC PANCREATIC CANCER
Talia Golan Institute of Oncology, Sheba Medical Center, Ramat Gan, Israel
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid tumors. The median overall survival (OS) in patients with stage IV disease is limited. This disease is a challenge, with patients presenting with severe debilitating symptoms at diagnosis, including weight loss, radiating abdominal pain, loss of appetite, depression and deteriorated physical activity and well-being. Furthermore, PDAC is resistant to chemotherapy, with 4-6 months of progression free survival or less with first-line combination chemotherapy therapy. To improve the management of PDAC in an era of precision medicine, it is highly important to identify subsets of patients who can benefit from targeted treatments. In particular, BRCA 1/2 germline mutations (gBRCAm) affect up to 7% of patients with PDAC. The BRCA 1/2 proteins play a significant role in the repair of DNA double strand breaks (DSB). Tumors with homologous recombination repair (HRR) gene abnormalities such BRCA1/2 are sensitive to both platinum and poly (ADP-ribose) polymerase inhibitors (PARPi).
In the phase III POLO study (Pancreas Cancer Olaparib Ongoing), the PARPi olaparib was given as maintenance treatment following firstline platinum-based chemotherapy in gBRCAm patients with metastatic PDAC versus placebo. Patients had to receive at least 16 weeks of firstline, platinum-based chemotherapy, without tumor progression. 154 eligible patients for the POLO trial were randomized at a 3:2 ratio to olaparib tablets, 300 mg po bid, or placebo, and continued treatment until disease progression or unacceptable toxicity.
The primary endpoint was PFS and was measured from the time of randomization, which was after first-line chemotherapy had been completed. Key secondary endpoints included time to second progression, objective response rate, health-related quality of life, safety and tolerability, and overall survival.
The primary endpoint (PFS) was 7.4 months in the olaparib arm, and 3.8 months in the placebo arm, with a hazard ratio of 0.53, and a p value of 0.0038. Pre-specified analyses were performed of the proportion of patients who were progressionfree at 6, 12, 18 and 24 months. From 6 months onwards, more than twice the proportion of olaparib arm patients were progression-free compared to the placebo arm, which is consistent with the hazard ratio of 0.53. Although the secondary endpoint, OS did not demonstrate a statistically significant difference between olaparib and placebo (HR 0.83; p = 0.3487), the totality of the evidence (primary PFS and multiple key secondary endpoints) supports a clinically meaningful benefit of maintenance olaparib in patients with metastatic pancreatic cancer and a gBRCAm. At 3 years: 17.2% of patients remained on olaparib treatment vs 3.3% on placebo. 21.5% of patients in the olaparib arm remained free of subsequent cancer therapy vs 3.6% in the placebo arm (TFST: HR 0.44, nominal p < 0.0001) and 33.9% of patients receiving olaparib were alive compared with 17.8% on placebo. These results confirm the importance of testing for BRCA and other germline mutations in all PDAC patients, which is now recommended by both ASCO and NCCN guidelines. A strategic approach with first-line platinum-based chemotherapy followed by maintenance olaparib treatment should become a new standard of care for patients with metastatic PDAC patients who have a gBRCAm.
Prof. Teh-Hong Wang Memorial Lecture
CREATING NEW IDEAS BASED ON THE HISTORY – LOOKING BACK ON THE HISTORY OF JGES –
Hisao Tajiri Department of Innovative Interventional Endoscopy Research, Jikei University School of Medicine, Tokyo, Japan
Gastrointestinal endoscopy started with rigid gastroscope, and the early gastroscope was developed and improved mainly in Germany. This is because Germany was the most advanced country in the research and application of optics until the middle of the 20th century. Owing to the efforts by Dr. Tatsuo Uji of the University of Tokyo, and Mr. Fukami and Mr. Sugiura of Olympus Co., a gastrocamera was introduced in 1952. In 1955, the first gastrocamera research meeting was held. According to the memoir by Dr. Uji, it was in May 1949 that he knocked on the door of Olympus Co. with an idea. It was a time when domestic industries were finally recovering from the turmoil of the post-war period, when there was bright hope for the future, and when people began to search for new paths.
Japan Gastroenterological Endoscopy Society (JGES) was established as Japan Gastrocamera Society in 1959. In 1973, the title of the society was changed to Japan Gastroenterological Endoscopy Society to focus on the gastroenterological endoscopy. The society had a membership of only 280 at the time of establishment, but it has grown to over 34,820 members in 2021.
Looking back on the research activities of JGES to date, as its academic field is based on endoscopic instruments, the presentations of research at the society have been strongly related to the development, improvement, and popularization of endoscopic instruments of the time. From 1955 to the early 1960s was the era of the gastrocamera. One of the major achievements is the establishment of the early gastric cancer classification in 1962. In 1963, a locally-produced gastrocamera with fiberscope was introduced and widely accepted. In 1968-1972, colonoscope and duodenoscope were completed, and diagnostic studies of these organs were incorporated. The main research themes from 1980 to 1985 were the spread of pan-endoscopes, establishment of colorectal diagnostics and development of endoscopic mucosal resection (EMR). Since 1985, the electronic endoscopy and the ultrasound endoscopy have been introduced, and their widespread use has further advanced endoscopic diagnostics. After 1989, more precise diagnosis of early esophageal, gastric, and colorectal cancer, as well as early diagnosis of pancreatic cancer and endoscopic diagnosis of pancreato-biliary diseases were established. More than 20 years have passed since the development of Endoscopic Submucosal Dissection (ESD) in 1998-1999, and the technique is now widely used around the world. In addition, image enhanced endoscopy (IEE), represented by Narrow Band Imaging (NBI), has established a new diagnostic science. In the Reiwa era (2019~), AI-aided endoscopy, endoscopybased genomic medicine, submucosal endoscopy, and endoscopic full thickness resection (EFTR) are being developed as research themes for the society. JGES started Japan Endoscopy Database (JED) Project in 2015. The aim of this project is to construct a “dream” database to benefit both doctors and patients. This will realize an ambitious strategy to create the world’s leading database with approximately 17 million additional data every
year when it is fully operational. By JED, we seek to take an initiative in the construction of infrastructure to conduct international joint research, and we indeed have been promoting a lot of research of AI-assisted endoscopy with JED. The current AI technology is realized to decrease the rate of missed lesions during endoscopy and to decide the accurate endoscopic treatment strategy. AI is without any doubt an attractive option and has the potential to improve the quality of endoscopy and standardize endoscopy practice. However, almost all AI-related studies have been retrospective. Therefore, we must await the results of highquality clinical trials. In the future, to stay innovation, what we have to do is to promote cooperation of GI endoscopists and surgeons, as well as the promotion of research and development in a mid- to long-term perspective with the cooperation between industry and academia. In addition to the fusion of expert human skills and machinery (robot technology), it is important to evolve the next generation technology combining AI and information systems. As can be seen from the history of the first industrial revolution to the recent fourth industrial revolution, endoscopic instruments have been developed along with the progress of peripheral science and technology. Today’s endoscopy is not the result of the sudden appearance of something new, but rather the result of reviewing the research already done by predecessors and adding new ideas and improvements to it. Therefore, we should learn from the past to develop new ideas.
GEST-KASID Symposium
DEEP-LEARNING SYSTEM FOR REAL-TIME DIFFERENTIATION BETWEEN CROHN’S DISEASE, INTESTINAL BEHÇET’S DISEASE, AND INTESTINAL TUBERCULOSIS
Jae Hee Cheon Department of Internal Medicine and Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Korea
Behçet’s disease (BD) is a chronic, recurrent, systemic inflammatory disease characterized by oral ulcers, genital ulcers, and eye inflammation. When BD patient has predominant gastrointestinal symptoms and an intestinal ulcer in the endoscope, it can be classified as “intestinal BD”. Intestinal BD is more common in East Asia than in Eastern Mediterranean countries. Crohn’s disease (CD) is a chronic inflammatory bowel disease characterized by transmural inflammation and granuloma formation, affecting the gastrointestinal tract. Intestinal tuberculosis (ITB) occurs when tuberculosis bacteria enter the gastrointestinal tract. ITB diagnosis requires colonoscopy using multiple biopsies on the ulcer margins and confirmation by histology, smear, and culture. The most commonly observed area of ITB is also the ileocecal area, comprising >75% of total gastrointestinal tuberculosis. In East Asia, where intestinal BD, CD, and ITB are prevalent, their differential diagnosis can be difficult owing to limited diagnostic accuracy and limitations of clinical or endoscopic predictive models. The finding that the most commonly associated area in the gastrointestinal tract is the ileocecal area and similarity of ileocecal ulcer shapes between the diseases cause difficulty for clinicians to differentiate between the three diseases and determine the optimal treatment strategy. Accurate diagnosis is a prerequisite because treatment strategies for these diseases are different and steroid or immunosuppressive treatments can be fatal for ITB patients. Recently, there has been increasing interest in applying artificial intelligence to the medical field through different machine-learning models. Machine learning is a data analysis method that learns descriptive or predictive models by iterating over models that gradually improve the performance of a specific task by itself. Deep learning is a machine-learning method comprising complex architectures of deep neural networks (DNNs). DNN research comprises a training process, which defines the network architecture and determines different weights between the neural nodes, and a test process, which evaluates DNN’s ability to predict the desired output. Accumulation of an enormous number of digital images and medical records has become the basis for further improving the performance of the deeplearning-based model. Moreover, improvement in computing performance through improved graphic processing units made it possible to create deeplearning models in a reasonable time, which was previously impossible. Prior to emergence of deep learning, studies focused on extracting key features from colonoscopy images, yielding an algorithm by
expert endoscopists. Currently, the automated diagnosis of various diseases using the deeplearning method has become a major research area in the gastroenterology field. However, there has been limited research differentiating the morphology of multiple colorectal ulcers of a single patient. Majority of the current work has concentrated on detecting and differentiating colorectal polyps, as it is relatively easier to obtain images through screening; traditional machinelearning methods such as support vector machine alone have demonstrated acceptable performance in detecting these polyps. Majority of studies to date designed deep-learning models and concurrent evaluation techniques that facilitated the detection and differentiation of a single disease entity in the endoscopic images. Differential diagnosis of intestinal BD, CD, and ITB is clinically important because of its high prevalence in East Asia and difficulty in determining treatment strategies. Previous studies have begun to report the usefulness of deep learning for the diagnosis and differentiation of diseases through colonoscopy images; however, this is not so for the differential diagnosis of these diseases. Therefore, we investigated the usefulness of differential diagnosis through deep-learning algorithms using colonoscopic images of intestinal BD, CD, and ITB patients.
GEST-KASID Symposium
Chih-Sheng Hung Department of Gastroenterology, Cathay General Hospital, Taipei, Taiwan
Artificial intelligence (AI): An emerging technology in the past decades, has been applied in clinical studies to improve the medical care of patients with gastroenterological diseases. For example: detect polyps, early cancer lesions, facilitate the analysis of inflammatory lesions of bowel, liver fibrosis and medical response of medications.
From deep neurological learning and large data base input for machine learning, several clinical studies comprising inflammatory bowel disease (IBD) patients to determine the differential diagnosis of ulcerative colitis and crohn’s disease, and to predict the response and clinical outcomes of medications used to treat IBD. AI-assisted endoscopy in IBD progress rapidly with promising technical results when tested in an experimental clinical application. We collect and present recent advances of AI to improve diagnosis of inflammatory bowel disease.
GEST-KASID Symposium
Yen-Nien Chen Department of Internal Medicine, National Taiwan University Cancer Center, Taipei, Taiwan
Complexity of inflammatory bowel disease (IBD) etiology and heterogeneity among IBD patients make the disease course become difficult to be evaluated. Current risk stratification profiles or tools of IBD are not yet satisfactory. Clinical information from IBD patients include clinical data, chronicity of gastrointestinal and extraintestinal symptoms, laboratory values (such as C-reactive protein and fecal calprotectin), imaging, endoscopy, and histopathology findings. In addition, immunophenotyping using whole genome gene expression datasets, progress of gut microbiome, and data from multi-omics analyses might play important roles on predicting the disease course. Such condition has inevitably led to vast arrays of high dimensional data that pose significant challenges with traditional statistical and computational methods. Advances in artificial intelligence (AI) provide clinicians and researchers chances to process, analyze, and interpret high dimensional data and large datasets. Adequate disease risk assessment is crucial for early and effective treatment intervention. So far, there are several studies focused on predicting risk of IBD. Random forest (RF) and support vector machines (SVM), both are examples of supervised machine learning (ML), were used to deal with microarray, RNA-seq data sets and micro-RNA profiles. Gradient boosted trees and artificial neural networks were used to analyze gene expression profiles. Convolutional neural networks (CNNs) analysis of endoscopic images also applied in assessment of disease severity in IBD. These examples highlight the clinical utility, versatility, and performance of AI in disease risk assessment at the clinical, endoscopic, and histologic level.
GEST-KASID Symposium
Soo-Kyung Park Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. Artificial intelligence, in particular, the deeplearning subtype has emerged as a breakthrough in computer technology enabled by the application of labeled big data across all the sectors. AI allows computers to identify, quantify, and interpret relationships among variables by algorithmically learning the efficient data representations, which is a formidable task for physicians. AI was considered in medical research when its ability to learn complex patterns and make predictions was noticed. In the entire field of gastroenterology, AI is mainly used in image recognition and statistical analysis of diagnosis or prediction of prognosis. Currently, the goal of treatment of IBD has changed from the traditional clinical remission to a more specific, integrated, and complete deep remission or mucosal healing. The advent of new biologics and small molecules such as anti-TNF agents has significantly changed the treatment strategies of IBD. Clinical decisions are more difficult for not only patients but also clinicians. At present, the action targets and predictable tolerability of novel treatment options usually serve as the driving factors for clinical decisions. However, many difficulties remain to be solved in optimizing treatment strategies, improving longterm prognosis, and changing the natural history of IBD.
Machine learning (ML) is thus feasible because of its ability to extract information from existing medical records and digital images for predicting the progression of IBD or the efficacy of certain medications. One study applied random forest (RF) algorithms for predicting the likelihood of patients with IBD experiencing disease flares over a certain period. Corticosteroid use and hospitalizations were considered as a surrogate for IBD flares in this study. This study initially limited the predictors, including age, sex, and five other features, for the diagnosis of IBD and used these finite factors to establish models for predicting the IBD flares. Finally, the best prediction performance was achieved by the RF longitudinal model anticipated with previous hospitalization or steroid use, and its AUC reached 0.87. Another study performed a case-cohort study utilising a paediatric cohort. Applying machine learning to a paediatric Crohn’s disease inception cohort, they have identified protein biomarkers that can compositely predict the development of complications when assayed in the blood at time of diagnosis. Distinct proteins were selected for B2 and B3, highlighting the differential underlying biologic processes behind these complications. Protein-based models performed as well as or better than clinical feature and serology-based models at predicting Crohn’s disease complications and selected proteins were not correlated with other biomarkers of disease
activity or prognosis. A recent study aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. Patients with CD were included from 15 tertiary hospitals in South Korea that participated in the multicenter, retrospective case-control study (IMPACT study: identification of the mechanism of the occurrence and progression of Crohn’s disease through integrated analysis on both genetic and environmental factors). The single-nucleotide polymorphism (SNP) genotype data for 337 CD patients were typed using the Korea Biobank Array. For external validation, an additional 126 CD patients were genotyped. The predictive model was trained using the 102 candidate SNPs and seven sets of clinical information (age, sex, cigarette smoking, disease location, disease behavior, upper gastrointestinal involvement, and perianal disease) by employing a tree-based machine learning method (CatBoost). The importance of each feature was measured using the Shapley Additive Explanations (SHAP) model. The final model comprised two clinical parameters (age and disease behavior) and four SNPs (rs28785174, rs60532570, rs13056955, and rs7660164). The combined clinical–genetic model predicted early surgery more accurately than a clinical-only model in both internal (area under the receiver operating characteristic (AUROC), 0.878 vs. 0.782; n = 51; p < 0.001) and external validation (AUROC, 0.836 vs. 0.805; n = 126; p < 0.001).
These studies showed the application of complex data in predicting disease progression, which was followed by the use of ML methods to predict the prognosis of disease. The evolvement of transcriptomics, proteomics, and metabolomics can be further accelerated with the support of analytical approaches such as ML. When considering that precision medicine involves accurate diagnosis, credible risk assessment, and individual treatment, AI seems to be a technique that can boost its development and also benefit from its broadscale application. Considering the development of research about IBD, its combination with artificial intelligence (AI) technology is not simply a result of interdisciplinary thinking but an inevitable merging of the treatments.
Reference:
1. John Gubatan, Steven Levitte, Akshar Patel, et el. Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions World J
Gastroenterol 2021 May 7;27(17):1920-1935. 2. Shirley Cohen-Mekelburg, Sameer Berry, Ryan W Stidham et el. Clinical applications of artificial intelligence and machine learningbased methods in inflammatory bowel disease J Gastroenterol Hepatol 2021 Feb;36(2):279285. 3. Akbar K Waljee, Rachel Lipson, Wyndy L Wiitala et el. Predicting Hospitalization and Outpatient Corticosteroid Use in Inflammatory Bowel Disease Patients Using Machine Learning Inflamm Bowel Dis 2017 Dec 19;24(1):45-53. 4. Ryan C Ungaro, Liangyuan Hu, Jiayi Ji et el. Machine learning identifies novel blood protein predictors of penetrating and stricturing complications in newly diagnosed paediatric
Crohn’s diseas. Aliment Pharmacol Ther. 2021 Jan;53(2):281-290. 5. Kang EA, Jang J, Choi CH et el. Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with
Crohn’s Disease: Results from the IMPACT
Study. J Clin Med. 2021 Feb 7;10(4):633.
Prof. Juei-Low Sung’s Research Foundation
34TH ANNUAL ACADEMIC MEETING
WHEN RECEPTOR TYROSINE KINASES MEET HEPATITIS: A WAY OF CARCINOGENESIS AND TREATMENTS
Sen-Yung Hsieh Department of Gastroenterology and Hepatology, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
Receptor-tyrosine-kinase (RTK) inhibitors have successfully treated many human cancers. However, it has not yet been very successful in the treatment of hepatocellular carcinoma (HCC). A comprehensive screening of RTK members concluded that multiple RTKs are co-expressed in the liver and are involved in different steps of hepatocyte carcinogenesis during the evolution of chronic hepatitis and cirrhosis. Among these, TAM-R expression was strongly associated with Ishak’s hepatitis activity indices and poor clinical outcomes. Mechanistically, TAM-Rs are not only a downstream target but also an inducer of STAT3mediated signaling, which forms a STAT3-TAMSTAT3 signaling loop to promote hepatocellular carcinogenesis. The silencing of TAM expression or inhibition of its kinase activity suppressed tumor growth in vitro and in vivo. Interestingly, TAMRs are also expressed on liver Kupffer cells and tumor-associated macrophages. Their role in the interplay between tumor cells and the hepatitis microenvironment is intriguing.
Prof. Juei-Low Sung’s Research Foundation
34TH ANNUAL ACADEMIC MEETING
CELL-FREE VIRUS-HOST CHIMERA DNA FORM HEPATITIS B VIRUS INTEGRATION SITES AS A CIRCULATING BIOMARKER OF HEPATOCELLULAR CANCER
Chiao-Ling Li Department and Graduate Institute of Medical Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
Early recurrence of hepatocellular carcinoma (HCC) after surgical resection compromises the patient survival. Timely detection of HCC recurrence and its clonality is required to implement salvage therapies appropriately. This study examined the feasibility of virus-host chimera DNA (vh-DNA), generated from junctions of hepatitis B virus (HBV) integration in the HCC chromosome, as a circulating biomarker for this clinical setting. HBV integration in 50 HBV-related HCC patients was determined by the capture-next generation sequencing (NGS) platform. For individual HCC, the vh-DNA was quantified by specific droplet digital PCR (ddPCR) assay in plasma samples collected before and two months after surgery. HBV integrations were identified in 44 out of 50 HBV-related HCC patients. Tumor-specific ddPCR was developed to measure the corresponding vhDNA copy number in baseline plasma from each patient immediately before surgery. vh-DNA was detected in 43 patients (97.7%), and the levels correlated with the tumor sizes (detection limit at 1.5 cm). Among the plasma collected at 2 months after surgery, 10 cases (23.3%) still contained the same signature vh-DNA detected at baseline, indicating the presence of residual tumor cells. Nine of them (90%) experienced HCC recurrence within one year, supporting vh-DNA as an independent risk factor in predicting early recurrence. Analysis of circulating vh-DNA at recurrence further helped identify the clonal origin. 81.8% of recurrences came from original HCC clones sharing the same plasma vh-DNA, whereas 18.2% were from de novo HCC. Vh-DNA was shown to be a new circulating biomarker for detecting the tumor load in majority of HBV-related HCC patients and aided in monitoring residual tumor and recurrence clonality after tumor resection.
Prof. Juei-Low Sung’s Research Foundation
34TH ANNUAL ACADEMIC MEETING
GLUTATHIONE PEROXIDASE 8 NEGATIVELY REGULATES CASPASE-4/11 TO PROTECT AGAINST COLITIS
Jye-Lin Hsu Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
Human caspase-4 and its mouse homologue caspase-11 are receptors for cytoplasmic lipopolysaccharide. Activation of caspase-4/11dependent NLRP3 inflammasome is required for innate defenses and endotoxic shock, but how caspase-4/11 is regulated remains elusive. Here, we show that caspase-4/11 activity is restrained by the oxidative stress sensor Glutathione peroxidase 8 (GPx8), and that GPx8-inhibition or -deficiency relieves caspase-4/11 to cause inflammation during colitis and septic shock. GPx8-/- mice exhibited exacerbated dextran sulfate sodium-induced colitis and reduced richness and diversity of gut microbiome. Mice that received adoptive cell transfer of GPx8-/- macrophages displayed accelerated colitis. GPx8-deficiency in macrophage potentiated caspase-11-dependent pyroptosis and exacerbated endotoxic shock. Mechanistically, GPx8 compromised the activation of caspase-4/11 directly through disulfide bonding mediated by cysteine 79 of GPx8 and cysteine 118 of caspase-4. Consistently, treatment with either N-acetylcysteine, a strong antioxidant, or VX-765, a caspase-4 inhibitor, suppressed caspase-4/11 activation and reduced colitis in Gpx8-deficient mice. Importantly, a positive correlation was found between lower Gpx8 and higher caspase-4 expression in the colon tissue of patients with ulcerative colitis. Taken together, these results indicate that GPx8 negatively regulates caspase-4/11 activity to protect against colitis and highlight the importance of this regulation in ulcerative colitis patients.
Prof. Juei-Low Sung’s Research Foundation
34TH ANNUAL ACADEMIC MEETING
LONG-TERM EFFECTIVENESS OF POPULATION-WIDE MULTIFACETED INTERVENTIONS FOR HEPATOCELLULAR CARCINOMA IN TAIWAN
Sih-Han Liao Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
Taiwan has launched a series of populationwide interventions to prevent hepatocellular carcinoma (HCC) related to hepatitis B and C virus infection since 1984. We took this opportunity to investigate the impact of each intervention on the incidence and case-fatality rate of HCC, and assessed their relative contributions to the overall reduction in mortality during this period. Populationbased registry data on HCC mortality and incidence from individuals aged 0 to 84 years between 1979 and 2016 were collected before (Period 1) and after universal hepatitis B vaccination from 1984 (Period 2), universal health care from 1995 (Period 3), and viral hepatitis therapy from 2003 (Period 4). A Bayesian Poisson regression model was used for mortality decomposition analysis to estimate the respective contributions of these interventions to the reduction in age-specific incidence and case-fatality rates. Mortality declined substantially in children, young- and middle-aged groups, but only slightly decreased in the elderly group. The declining trends in mortality were in part explained by incidence reduction and in part by a remarkable decline in case-fatality rate attributed to universal health care. Hepatitis B vaccination led to a 35.9% (26.8% to 44.4%) reduction in incidence for individuals aged 30 years or below, whereas antiviral therapy reduced the incidence of HCC by 14.9% (11.8% to 17.9%) and 15.4% (14.1% to 16.6%) for individuals aged 30–49 years and 50–69 years, respectively. Vaccination and antiviral therapy were effective in reducing HCC incidence and mortality for the young and middle-aged groups, while the case-fatality rate was improved by universal health care for all age groups.
