Prognostic Performance of Albumin-Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib

Efficacy and safety of transarterial chemoembolization combined with targeted therapy and immunotherapy versus with targeted monotherapy in unresectable hepatocellular carcinoma: A systematic review and meta-analysis

BACKGROUND AND OBJECTIVE

The application of transarterial chemoembolization (TACE) in combination with targeted therapy and immunotherapy (TACE-T-I) for unresectable hepatocellular carcinoma (HCC) has gained increasing attention. However, there are variations in the efficacy and safety outcomes between TACE-T-I versus TACE combined with targeted drugs (TACE-T). This study aims to systematically evaluate the efficacy and safety of TACE-T-I versus TACE-T in unresectable HCC.

METHODS

PubMed, Embase, Cochrane Library, and Web of Science databases were searched from inception to August 21, 2023, for comparative studies on TACE-T-I versus TACE-T for unresectable HCC. Outcome measures included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR) and the incidence of treatment-related adverse events (TRAEs). OS was the primary outcome of this study. Weighted mean difference (WMD) or hazard ratio (HR) was used as the pooled statistic for OS and PFS. Relative risk (RR) was employed as the pooled statistic for ORR, DCR and the incidence of TRAEs. And 95% confidence intervals (CIs) were calculated for all effect measures. Data analysis was conducted using Stata 14.0 software.

RESULTS

The meta-analysis included 14 studies with 2144 patients. The pooled results showed that compared with patients in the TACE-T group, patients in the TACE-T-I group had higher ORR (RR = 1.61; 95%CI: 1.38-1.89) and DCR (RR = 1.17; 95%CI: 1.09-1.26). Patients in the TACE-T-I group experienced prolonged PFS (WMD = 3.08; 95%CI: 2.63-3.53) and OS (WMD = 5.76; 95%CI: 4.68-6.84). And the risk of disease progression (HR = 0.45; 95%CI: 0.37-0.55) and death (HR = 0.43; 95%CI: 0.38-0.49) was lower in the TACE-T-I group. Common TRAEs included fever, pain, abdominal pain, nausea, vomiting, elevated ALT, elevated AST, hypertension, hand-foot syndrome, proteinuria, and diarrhea. The incidence and severity of TRAEs in the TACE-T-I group were similar to those in the TACE-T group, with no significant differences (P > .05).

CONCLUSION

Current evidence suggests that, on the basis of TACE combined with targeted therapy, the addition of immunotherapy provides better clinical efficacy and survival benefits for unresectable HCC patients, with good tolerability.

A refined prediction model for survival in hepatocellular carcinoma patients treated with transarterial chemoembolization

Background/Aims

Transarterial chemoembolization (TACE) is widely performed as a major treatment for hepatocellular carcinoma (HCC) patients, and there is a need to stratify patients for whom the most benefit from the treatment. This study aimed to develop a refined prediction model for overall survival (OS) in patients undergoing TACE as a first-line treatment in a large cohort and validate its performance.

Methods

A total of 2,632 patients with HCC of Barcelona Clinic Liver Cancer stage A or B who underwent TACE between 2008 and 2017 were enrolled. The patients were randomly assigned to a training cohort (n = 1,304) or a validation cohort (n = 1,328). Independent predictors of OS were used to develop a prediction model.

Results

The median age of patients in the entire cohort was 63 years, with the majority having hepatitis B virus (56.6%) and being classified as Child-Pugh class A (82.4%). We developed a new prognostic model, called the TACE-prognostic (TP) score, based on tumor burden (sum of the largest tumor diameter and tumor number), alpha-fetoprotein, and Albumin-Bilirubin grade. Patients were classified into five risk groups according to TP scores, with median survival significantly differentiated in both training and validation cohorts (P < 0.001). The new model consistently outperformed other currently available models in both the training and validation cohorts.

Conclusion

This newly developed TP scoring system has the potential to be a useful tool in identifying ideal candidates of TACE and predicting OS with favorable performance and discrimination. However, further external validation is needed to confirm its effectiveness.

Prognostic Performance of the China Liver Cancer Staging System in Hepatocellular Carcinoma Following Transarterial Chemoembolization

Background and Aims

To validate prognostic performance of the China liver cancer (CNLC) staging system as well as to compare these parameters with those of the Barcelona Clinic Liver Cancer (BCLC) staging system for Chinese hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE).

Methods

This multicenter retrospective study included 1,124 patients with HCC between January 2012 and December 2020 from six Chinese hospitals. Based on overall survival (OS), the prognostic performance outcomes for the CNLC and BCLC staging systems were compared by model discrimination [C statistic and Akaike information criterion (AIC)], monotonicity of the gradient (linear trend chi-square test), homogeneity (likelihood ratio chi-square test), and calibration (calibration plots). A prospective cohort of 44 patients receiving TACE-based therapy included between January 2021 and December 2022 was used to prospectively validate the outcomes.

Results

Median OS was 19.1 (18.2-20.0) months, with significant differences in OS between stages defined by the CNLC and BCLC observed (p<0.001). The CNLC performed better than the BCLC regarding model discrimination (C-index: 0.661 vs. 0.644; AIC: 10,583.28 vs. 10,583.72), model monotonicity of the gradient (linear trend chi-square test: 66.107 vs. 57.418; p<0.001), model homogeneity (159.2 vs. 158.7; p<0.001). Both staging systems had good model calibration. Similar results were observed in the prospective cohort.

Conclusions

Combining model discrimination, gradient monotonicity, homogeneity, and calibration, the CNLC performed better than the BCLC for Chinese HCC patients receiving TACE.

Transarterial Chemoembolization for Patients with Unresectable Hepatocellular Carcinoma with Child-Pugh B7

Background and Objectives

This study aimed to evaluate the efficacy and safety of transarterial chemoembolization (TACE) in patients with unresectable early or intermediate hepatocellular carcinoma (HCC) and Child-Pugh (CP)-B liver dysfunction.

Methods

This multicenter retrospective study enrolled patients with treatment-naïve HCC treated with TACE monotherapy between January 2012 and December 2020 at six Chinese hospitals. The primary outcome was overall survival (OS), and the secondary outcomes included the objective response rate (ORR) according to the modified RECIST and adverse events (AEs). Propensity score matching (PSM) was performed to reduce bias between the CP-B and CP-A groups.

Results

A total of 847 patients were included in the study. CP-A patients had significantly longer OS (median, 22.0 vs 19.3 months, P = 0.032) than CP-B (score of 7-9) patients, but a non-significant trend compared with CP-B (score of 7) patients (median, 22.0 vs 20.5 months, P = 0.254). After PSM, the median OS was 22.7 months for CP-A patients, while it was 19.3 months for CP-B (score of 7-9) patients (p = 0.026) and 20.5 months for CP-B (score of 7) patients (p = 0.155). CP-A patients achieved a significantly better ORR (53.0% vs 35.8%, P < 0.05) compared to CP-B (score of 7-9) patients, but a non-significant trend was observed in CP-B (score of 7) patients (53.0% vs 51.1%, P > 0.05). The post-embolization syndrome rates in the CP-A and CP-B (score of 7) cohorts were 52.1% and 53.3%, respectively. No new safety concerns were observed.

Conclusion

Patients with HCC with a CP score of 7 receiving TACE showed a similar prognosis and safety profile to CP-A patients.

Prognostic Significance of Serum Albumin Level and Albumin-Based Mono- and Combination Biomarkers in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer. Although many surgical and nonsurgical therapeutic options have been established for treating HCC, the overall prognosis for HCC patients receiving different treatment modalities remains inadequate, which causes HCC to remain among the most life-threatening human cancers worldwide. Therefore, it is vitally important and urgently needed to develop valuable and independent prognostic biomarkers for the early prediction of poor prognosis in HCC patients, allowing more time for more timely and appropriate treatment to improve the survival of patients. As the most abundant protein in plasma, human serum albumin (ALB) is predominantly expressed by the liver and exhibits a wide variety of essential biological functions. It has been well recognized that serum ALB level is a significant independent biomarker for a broad spectrum of human diseases including cancer. Moreover, ALB has been commonly used as a potent biomaterial and therapeutic agent in clinical settings for the treatment of various human diseases. This review provides a comprehensive summary of the evidence from the up-to-date published literature to underscore the prognostic significance of serum ALB level and various ALB-based mono- and combination biomarkers in the prediction of the prognosis of HCC patients after treatment with different surgical, locoregional, and systemic therapies.

Validation of the easy-to-use lenvatinib prognostic index to predict prognosis in advanced hepatocellular carcinoma patients treated with lenvatinib

AIM

The identification of new prognostic factors able to stratify hepatocellular carcinoma patients candidate to first-line therapy is urgent. In the present work we validated the prognostic value of the lenvatinib prognostic index.

METHODS

Data of Eastern and Western patients treated with lenvatinib as first-line for Barcelona Clinic Liver Cancer stage B or C hepatocellular carcinoma were recollected. The lenvatinib prognostic index was composed by three classes of risk according with our previous study. The "low risk" group includes patients with prognostic nutritional index (PNI) >43.3 and with previous transarterial chemoembolization. The "medium risk" group includes patients with PNI >43.3, but without previous transarterial chemoembolization and patients with PNI <43.3, albumin-bilirubin grade 1 and Barcelona Clinic Liver Cancer stage B. The "high risk" group includes patients with PNI <43.3, albumin-bilirubin grade 2, and patients with PNI <43.3, albumin-bilirubin grade 1 and Barcelona Clinic Liver Cancer stage C.

RESULTS

A total of 717 patients were included. The median overall survival was 20.7 months (95% CI 16.1-51.6) in patients with low risk (n = 223), 16.7 months (95% CI 13.3-47.0) in patients with medium risk (n = 264), and 10.7 months (95% CI 9.3-12.2) in patients with high risk (n = 230; HR 1, 1.29, and 1.92, respectively; p < 0.0001). Median progression-free survival was 7.3 months (95% CI 6.3-46.5) in patients with low risk, 6.4 months (95% CI 5.3-8.0) in patients with medium risk ,and 4.9 months (95% CI 4.3-5.5) in patients with high risk (HR 1, 1.07, 1.47 respectively; p = 0.0009).

CONCLUSION

The lenvatinib prognostic index confirms its prognostic value on an external cohort of hepatocellular carcinoma patients treated with Lenvatinib.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Deep Learning-Based Computed Tomography Perfusion Imaging to Evaluate the Effectiveness and Safety of Thrombolytic Therapy for Cerebral Infarct with Unknown Time of Onset

This study was aimed to discuss the effectiveness and safety of deep learning-based computed tomography perfusion (CTP) imaging in the thrombolytic therapy for acute cerebral infarct with unknown time of onset. A total of 100 patients with acute cerebral infarct with unknown time of onset were selected as the research objects. All patients received thrombolytic therapy. According to different image processing methods, they were divided into the algorithm group (artificial intelligence algorithm-based image processing group) and the control group (conventional method-based image processing group). After that, the evaluations of effectiveness and safety of thrombolytic therapy for the patients with acute cerebral infarct in the two groups were compared. The research results demonstrated that artificial intelligence algorithm-based CTP imaging showed significant diagnostic effects and the image quality in the algorithm group was remarkably higher than that in the control group (P < 0.05). Besides, the overall image quality of algorithm group was relatively higher. The differences in the National Institute of Health stroke scale (NIHSS) scores for the two groups indicated that the thrombolytic effect on the algorithm group was superior to that on the control group. Thrombolytic therapy for the algorithm group showed therapeutic effects on neurologic impairment. The symptomatic intracranial hemorrhage rate of the algorithm group within 24 hours was lower than the hemorrhage conversion rate of the control group, and the difference between the two groups was 14%. The data differences between the two groups showed statistical significance (P < 0.05). The results demonstrated that the safety of guided thrombolytic therapy for the algorithm group was higher than that in the control group. To sum up, deep learning-based CTP images showed the clinical application values in the diagnosis of cerebral infarct.

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

The Combination of Albumin-Bilirubin Score and Prothrombin Time Is a Useful Tool for Predicting Liver Dysfunction after Transcatheter Arterial Chemoembolization in Child-Pugh Class A Patients with Hepatocellular Carcinoma within Up-to-Seven Criteria

Mortality and recurrence rates of hepatocellular carcinoma (HCC) are high. Recent studies show that for patients with HCC beyond up-to-seven criteria, treatment with molecular-targeted agents (MTAs) is recommended because the treatment efficiency of transcatheter arterial chemoembolization (TACE) is poor; further, TACE increases decline in liver function. However, the relationship between TACE and liver function decline in patients with HCC within up-to-seven criteria has not been clarified. Hence, we aimed to investigate this relationship. This retrospective observational study included 189 HCC tumors within up-to-seven criteria in 114 Child–Pugh class A patients. Twenty-four (12.7%) tumors were changed from Child–Pugh class A to B after TACE, and 116 (61.4%) tumors exhibited recurrence within 6 months after TACE. Prothrombin time (PT) and albumin–bilirubin (ALBI) score before TACE were significantly associated with liver dysfunction from Child–Pugh class A to B. The combination of PT and ALBI score before TACE had high predictive ability for liver dysfunction from Child–Pugh class A to B after TACE (specificity = 100%, sensitivity = 91.7%). The combined use of pre-TACE PT and ALBI score has a high predictive ability for liver dysfunction after TACE for Child–Pugh class A patients with HCC within up-to-seven criteria.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Identification of Regorafenib Prognostic Index (REP Index) via Recursive Partitioning Analysis in Patients with Advanced Hepatocellular Carcinoma Receiving Systemic Treatment: A Real-World Multi-Institutional Experience

BACKGROUND

The results of the pivotal RESORCE trial led to the approval of the tyrosine kinase inhibitor regorafenib as second-line treatment in advanced hepatocellular carcinoma (HCC) after sorafenib failure. Data about prognostic factors in a second-line HCC setting are scarce.

OBJECTIVE

The aim of the present study was to investigate prognostic factors in a cohort of patients with advanced HCC treated with regorafenib after progressing on sorafenib.

METHODS

We retrieved the data of 259 patients affected by advanced HCC treated with regorafenib as second-line treatment from four different Italian institutions and one South Korean institution and performed a recursive partitioning analysis to build a score system.

RESULTS

At the first-step univariate analysis for overall survival (OS), alkaline phosphatase (ALP) was the most significant parameter and was chosen as the first node in our tree model. In the subpopulation of patients presenting with ALP ≤122 U/L (n=155) at baseline, the most statistically significant split was by progression-free survival (PFS) on previous sorafenib treatment, between patients with a PFS ≥ 6 months (n  =  59) and patients with a PFS < 6 months (n = 96). In the subpopulation of patients with ALP ≤ 122 U/L and PFS to sorafenib ≥ 6 months, the final split was determined between patients with hepatitis B virus (HBV)-related liver disease (n = 22) and patients with no HBV-related liver disease (n = 37). In the subpopulation of patients presenting ALP >122 U/L (n = 104) at baseline, the most statistically significant split was by aspartate aminotransferase (AST) value, between patients with AST ≤ 56 U/L (n = 48) and patients with AST > 56 U/L (n = 56). We built the Regorafenib Prognostic Index (REP index) stratifying the population into "low-risk," "medium-risk," and "high-risk" groups. The difference in median OS between the three risk groups was statistically significant, being 20.8 months (95% confidence interval [CI] 10.0-46.3) in the "low-risk" group, 8.4 months (95% CI 7.2-1435.8) in the "medium-risk" group, and 5.5 months (95% CI 3.5-13.2) in the "high risk" group. The median PFS was 7.7 months (95% CI 3.7-19.3), 2.5 months (95% CI 2.1-28.8), and 2.4 months (95% CI 1.6-9.1) for the "low-risk," "medium-risk," and "high-risk" groups, respectively.

CONCLUSION

The REP index is an independent prognostic factor for OS and PFS in patients with advanced HCC treated with regorafenib.

Refining Prognosis in Chemoembolization for Hepatocellular Carcinoma: Immunonutrition and Liver Function

A combination of albumin-bilirubin (ALBI) grading and the Prognostic Nutritional Index (PNI) was identified recently as a highly predictive tool for patients with hepatocellular carcinoma (HCC) undergoing tumor ablation. The present study evaluated this combination in patients undergoing transarterial chemoembolization (TACE). Between 2010 and 2020, 280 treatment-naïve patients were retrospectively identified. The influence of ALBI grade, PNI and the novel ALBI-PNI on the median overall survival (OS) was assessed. In the next step, the prognostic ability of the combined approach was compared to established scoring systems. Both ALBI grade 2-3 and a low PNI were highly predictive for median OS (ALBI grade 1-3: 39.0 vs. 16.3 vs. 5.4 months, p < 0.001; high vs. low PNI: 21.4 vs. 7.5, p < 0.001). The combination of both resulted in a median OS of 39.0, 20.1, 10.3, and 5.4 months (p < 0.001). With a Concordance Index (C-Index) of 0.69, ALBI-PNI outperformed each individual score (ALBI 0.65, PNI 0.64) and was also better than BCLC, HAP, mHAP-II, and the Six-and-Twelve score (C-Indices 0.66, 0.60, 0.59, and 0.55). Thus, the easy-to-calculate ALBI-PNI may be a promising stratification tool for patients with HCC undergoing TACE, reflecting both immunonutritive status and liver function.

Identification of lenvatinib prognostic index via recursive partitioning analysis in advanced hepatocellular carcinoma

BACKGROUND

After the advent of new treatment options for advanced hepatocellular carcinoma (HCC), the identification of prognostic factors is crucial for the selection of the most appropriate therapy for each patient.

PATIENTS AND METHODS

With the aim to fill this gap, we applied recursive partitioning analysis (RPA) to a cohort of 404 patients treated with lenvatinib.

RESULTS

The application of RPA resulted in a classification based on five variables that originated a new prognostic score, the lenvatinib prognostic index (LEP) index, identifying three groups: low risk [patients with prognostic nutritional index (PNI) >43.3 and previous trans-arterial chemoembolization (TACE)]; medium risk [patients with PNI >43.3 but without previous TACE and patients with PNI <43.3, albumin-bilirubin (ALBI) grade 1 and Barcelona Clinic Liver Cancer stage B (BCLC-B)]; high risk [patients with PNI <43.3 and ALBI grade 2 and patients with PNI <43.3, albumin-bilirubin (ALBI) grade 1 and Barcelona Clinic Liver Cancer stage C (BCLC-C)]. Median overall survival was 29.8 months [95% confidence interval (CI) 22.8-29.8 months] in low risk patients (n = 128), 17.0 months (95% CI 15.0-24.0 months) in medium risk (n = 162) and 8.9 months (95% CI 8.0-10.7 months) in high risk (n = 114); low risk hazard ratio (HR) 1 (reference group), medium risk HR 1.95 (95% CI 1.38-2.74), high risk HR 4.84 (95% CI 3.16-7.43); P < 0.0001. The LEP index was validated in a cohort of 127 Italian patients treated with lenvatinib. While the same classification did not show a prognostic value in a cohort of 311 patients treated with sorafenib, we also show a possible predictive role in favor of lenvatinib in the low risk group.

CONCLUSIONS

LEP index is a promising, easy-to-use tool that may be used to stratify patients undergoing systemic treatment of advanced HCC.

Single-Centre Retrospective Training Cohort Using Artificial Intelligence for Prognostic Prediction of Encephalopathy, Mortality, and Liver Dysfunction after Early TIPS Creation

OBJECTIVES

Based on an artificial intelligence approach, this study attempted to establish prognostic models to predict 3-month overt hepatic encephalopathy (OHE) occurrence, 1-year mortality, and liver dysfunction for cirrhotic patients with acute variceal bleeding (AVB) treated with early transjugular intrahepatic portosystemic shunt (TIPS) creation.

MATERIALS AND METHODS

This retrospective study included patients treated with early TIPS between January 2016 and November 2019. Independent risk factors associated with occurrence of OHE within 3 months, 1-year mortality, and liver dysfunction after early TIPS were identified using univariate and multivariate logistic analyses. Artificial neural network (ANN) models and prognostic nomograms based on the independent risk factors were established and validated internally.

RESULTS

A total of 207 patients were included, with 33 (15.9%) experienced OHE within 3 months after TIPS creation. The albumin-bilirubin grade (P = 0.015), age (≤ 65, > 65 years) (P < 0.001), gender (P = 0.002), and alcoholic cirrhosis (P = 0.013) was identified as independent risk factors associated with 3-month OHE. Presence of portal vein thrombosis (P = 0.034) and model for end-stage liver disease score (P = 0.063) were identified as independent risk factors associated with 1-year mortality. The platelet-albumin-bilirubin grade (P = 0.041) and a history of hepatic encephalopathy (P = 0.018) were identified as independent risk factors associated with liver dysfunction after TIPS creation. Three ANN models and three nomograms were then established and validated with high accuracy.

CONCLUSIONS

The ANN and nomogram models have potential to accurately predict early occurrence of OHE, mortality, and liver dysfunction after early TIPS creation for cirrhotic patients with AVB.