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Background/Aims Identifying patients with intrahepatic cholangiocarcinoma (ICC) likely to benefit from immunochemotherapy, the new front-line treatment, remains challenging. We aimed to unveil a novel radiotranscriptomic signature that can facilitate treatment response prediction by multi-omics integration and multiscale modelling.
Methods We analyzed bulk, single-cell and spatial transcriptomic data comprising 457 ICC patients to identify an immune-related score (IRS), followed by decoding its spatial immune context. We mapped radiomics profiles onto spatial-specific IRS using machine learning to define a novel radiotranscriptomic signature, followed by multi-scale and multi-cohort validation covering 331 ICC patients. The signature was further explored for the potential therapeutic target from in vitro to in vivo.
Results We revealed a novel 3-gene (PLAUR, CD40LG, and FGFR4) IRS whose down-regulation correlated with better survival and improved sensitivity to immunochemotherapy. We highlighted functional IRS-immune interactions within tumor epithelium, rather than stromal compartment, irrespective of geospatial locations. Machine learning pipeline identified the optimal 3-feature radiotranscriptomic signature that was well-validated by immunohistochemical assays in molecular cohort, exhibited favorable external prognostic validity with C-index over 0.64 in resection cohort, and predicted treatment response with an area under the curve of up to 0.84 in immunochemotherapy cohort. We also showed that anti-uPAR/PLAUR alone or in combination with anti-programmed cell death protein 1 therapy remarkably curbed tumor growth, using in vitro ICC cell lines and in vivo humanized ICC patient-derived xenograft mouse models.
Conclusions This proof-of-concept study sheds light on the spatially-resolved radiotranscriptomic signature to improve patient selection for emerging immunochemotherapy and high-order immunotherapy combinations in ICC.
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Backgrounds/Aims Despite advances in antiviral therapy for hepatitis C virus (HCV) infection, hepatocellular carcinoma (HCC) still develops even after sustained viral response (SVR) in patients with advanced liver fibrosis or cirrhosis. This meta-analysis investigated the predictive performance of vibration-controlled transient elastography (VCTE) and fibrosis 4-index (FIB-4) for the development of HCC after SVR.
Methods We searched PubMed, MEDLINE, EMBASE, and the Cochrane Library for studies examining the predictive performance of these tests in adult patients with HCV. Two authors independently screened the studies’ methodological quality and extracted data. Pooled estimates of sensitivity, specificity, and area under the curve (AUC) were calculated for HCC development using random-effects bivariate logit normal and linear-mixed effect models.
Results We included 27 studies (169,911 patients). Meta-analysis of HCC after SVR was possible in nine VCTE and 15 FIB-4 studies. Regarding the prediction of HCC development after SVR, the pooled AUCs of pre-treatment VCTE >9.2–13 kPa and FIB-4 >3.25 were 0.79 and 0.73, respectively. VCTE >8.4–11 kPa and FIB-4 >3.25 measured after SVR maintained good predictive performance, albeit slightly reduced (pooled AUCs: 0.77 and 0.70, respectively). The identified optimal cut-off value for HCC development after SVR was 12.6 kPa for pre-treatment VCTE. That of VCTE measured after the SVR was 11.2 kPa.
Conclusions VCTE and FIB-4 showed acceptable predictive performance for HCC development in patients with HCV who achieved SVR, underscoring their utility in clinical practice for guiding surveillance strategies. Future studies are needed to validate these findings prospectively and validate their clinical impact.
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2025 KASL clinical practice guidelines for management of hepatitis C Eun Sun Jang, Nae Yun Heo, Jae Yoon Jeong, Jung Gil Park, Do Seon Song, Eun Ju Cho, Chang Hun Lee, Jae Seung Lee, Jae Hyun Yoon, Seul Ki Han, Young Kul Jung Clinical and Molecular Hepatology.2026; 32(1): 1. CrossRef
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Background/Aims Existing hepatocellular carcinoma (HCC) prediction models are derived mainly from pretreatment or early on-treatment parameters. We reassessed the dynamic changes in the performance of 17 HCC models in patients with chronic hepatitis B (CHB) during long-term antiviral therapy (AVT).
Methods Among 987 CHB patients administered long-term entecavir therapy, 660 patients had 8 years of follow-up data. Model scores were calculated using on-treatment values at 2.5, 3, 3.5, 4, 4.5, and 5 years of AVT to predict threeyear HCC occurrence. Model performance was assessed with the area under the receiver operating curve (AUROC). The original model cutoffs to distinguish different levels of HCC risk were evaluated by the log-rank test.
Results The AUROCs of the 17 HCC models varied from 0.51 to 0.78 when using on-treatment scores from years 2.5 to 5. Models with a cirrhosis variable showed numerically higher AUROCs (pooled at 0.65–0.73 for treated, untreated, or mixed treatment models) than models without (treated or mixed models: 0.61–0.68; untreated models: 0.51–0.59). Stratification into low, intermediate, and high-risk levels using the original cutoff values could no longer reflect the true HCC incidence using scores after 3.5 years of AVT for models without cirrhosis and after 4 years of AVT for models with cirrhosis.
Conclusions The performance of existing HCC prediction models, especially models without the cirrhosis variable, decreased in CHB patients on long-term AVT. The optimization of existing models or the development of novel models for better HCC prediction during long-term AVT is warranted.
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Background/Aims A comprehensive analysis of trends in the incidence of hepatocellular carcinoma (HCC) is important for planning public health initiatives. We aimed to analyze the trends in HCC incidence in South Korea over 10 years and to predict the incidence for the year 2028.
Methods Data from patients with newly diagnosed HCC between 2008 and 2018 were obtained from Korean National Health Insurance Service database. Age-standardized incidence rates (ASRs) were calculated to compare HCC incidence. A poisson regression model was used to predict the future incidence of HCC.
Results The average crude incidence rate (CR) was 22.4 per 100,000 person-years, and the average ASR was 17.6 per 100,000 person-years between 2008 and 2018. The CR (from 23.9 to 21.2 per 100,000 person-years) and ASR (from 21.9 to 14.3 per 100,000 person-years) of HCC incidence decreased during the past ten years in all age groups, except in the elderly. The ASR of patients aged ≥80 years increased significantly (from 70.0 to 160.2/100,000 person-years; average annual percent change, +9.00%; P<0.001). The estimated CR (17.9 per 100,000 person-years) and ASR (9.7 per 100,000 person-years) of HCC incidence in 2028 was declined, but the number of HCC patients aged ≥80 years in 2028 will be quadruple greater than the number of HCC patients in 2008 (from 521 to 2,055), comprising 21.3% of all HCC patients in 2028.
Conclusions The ASRs of HCC in Korea have gradually declined over the past 10 years, but the number, CR, and ASR are increasing in patients aged ≥80 years.
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Clin Mol Hepatol 2022;28(1):105-116. Published online October 15, 2021
Background/Aims To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model.
Methods This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10–14 weeks and screened them for GDM at 24–28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks.
Results Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1–4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563–0.697 in settings 1–3 vs. 0.740–0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719–0.819 in setting 5, P=not significant between settings 4 and 5).
Conclusions We developed an early prediction model for GDM using machine learning. The inclusion of NAFLDassociated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144)
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Jae Youn Cheong, M.D., Kwang-Hyub Han, M.D., Dong Kee Kim, Ph.D.*, Sang Hoon Ahn, M.D.,
Ki Jun Song, M.S.*, Yong Han Paik, M.D., Chang Hwan Choi, M.D., Hyun Woong Lee, M.D.,
Young Soo Park, M.D., Chae Yoon Chon, M.D., and Young Myoung Moon, M.D.,
Background / Aim : We identified risk factors for hepatocellular carcinoma(HCC)through a nine-year follow-up study, ending last year, of 4,339 patients with chronic liver disease. The aim of this study was to establish an individual prediction model according to risk factors for the development of HCC. Methods : We studied a total of 1994 patients who had regular check-ups from January 1990 to December 1998. We analyzed the risk factors and established the individual prediction model to predict the risk rate for HCC using logistic regression analysis. We applied the model to patients who were enrolled over the next two years. Results : 90(9.05%) out of 994 patients developed HCC during a mean of 33 months of follow-up. The risk index for individual patients was made by considering the relative risk level of statistically significant risk factors. From 1999 to 2000, 480 patients were newly enrolled and divided into a low risk group(less than 5% probability), an intermediate risk group(5% to 10% probability), and a high risk group(more than 10% probability). According to this classification, 1 of 191 patients in the low risk group(0.523%), 5 of 176 patients intermediate risk group(2.84%), and 21 of 113 patients in the high risk group(18.6%) were diagnosed with HCC. Conclusion : We confirmed the reliability of the newly established individual prediction model for the screening of HCC. This model may help screening programs to be done effectively by focusing on high risk groups for HCC. (Korean J Hepatol 2001;7 :449- 458)
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