Consensus statements on endoscopic ultrasound-guided tissue acquisition. Guidelines from the Asian Endoscopic Ultrasound Group.

OBJECTIVES: This consensus was developed by the Asian EUS Group (AEG), who aimed to formulate a set of practice guidelines addressing various aspects of endoscopic ultrasound-guided tissue acquisition (EUS-TA). METHODS: The AEG initiated the development of consensus statements and formed an expert panel comprising surgeons, gastroenterologists, and pathologists. Three online consensus meetings were conducted to consolidate the statements and votes. The statements were presented and discussed in the first two consensus meetings and revised according to comments. Final voting was conducted at a third consensus meeting. The Grading of Recommendations, Assessment, Development, and Evaluation system was adopted to define the strength of the recommendations and quality of evidence. RESULTS: A total of 20 clinical questions and statements regarding EUS-TA were formulated. The committee recommended that fine-needle biopsy (FNB) needles be preferred over conventional fine-needle aspiration (FNA) needles for EUS-TA of subepithelial lesions. For solid pancreatic masses, rapid on-site evaluation is not routinely recommended when FNB needles are used. For dedicated FNB needles, fork-tip and Franseen-tip needles have essentially equivalent performance. CONCLUSION: This consensus provides guidance for EUS-TA, thereby enhancing the quality of EUS-TA.

Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis.

BACKGROUND & AIMS: aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance. METHODS: A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125). RESULTS: The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively). CONCLUSIONS: The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.

MAFLD fibrosis score: Using routine measures to identify advanced fibrosis in metabolic-associated fatty liver disease.

BACKGROUND: Early screening may prevent fibrosis progression in metabolic-associated fatty liver disease (MAFLD). AIMS: We developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) among MAFLD patients. METHODS: This cross-sectional, multicentre study consecutively recruited MAFLD patients receiving tertiary care (Malaysia as training cohort [n = 276] and Hong Kong and Wenzhou as validation cohort [n = 431]). Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select 'highly important' predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula. RESULTS: MFS was composed of seven variables: age, body mass index, international normalised ratio, aspartate aminotransferase, gamma-glutamyl transpeptidase, platelet count, and history of type 2 diabetes. MFS demonstrated an area under the receiver-operating characteristic curve of 0.848 [95% CI 0.800-898] and 0.823 [0.760-0.886] in training and validation cohorts, significantly higher than aminotransferase-to-platelet ratio index (0.684 [0.603-0.765], 0.663 [0.588-0.738]), Fibrosis-4 index (0.793 [0.735-0.854], 0.737 [0.660-0.814]), and non-alcoholic fatty liver disease fibrosis score (0.785 [0.731-0.844], 0.750 [0.674-0.827]) (DeLong's test p < 0.05). MFS could include 92.3% of patients using dual cut-offs of 14 and 15, with a correct prediction rate of 90.4%, resulting in a larger number of patients with correct diagnosis compared to other scores. A two-step MFS-VCTE screening algorithm demonstrated positive and negative predictive values and overall diagnostic accuracy of 93.4%, 89.5%, and 93.2%, respectively, with only 4.0% of patients classified into grey zone. CONCLUSION: MFS outperforms conventional non-invasive scores in predicting advanced fibrosis, contributing to screening in MAFLD patients.

aMAP Score and Its Combination With Liver Stiffness Measurement Accurately Assess Liver Fibrosis in Chronic Hepatitis B Patients.

BACKGROUND & AIMS: The changes in liver stiffness measurement (LSM) are unreliable to estimate regression of fibrosis during antiviral treatment for chronic hepatitis B (CHB) patients. The age-male-albumin-bilirubin-platelets score (aMAP), as an accurate hepatocellular carcinoma risk score, may reflect the liver fibrosis stage. Here, we aimed to evaluate the performance of aMAP for diagnosing liver fibrosis in CHB patients with or without treatment. METHODS: A total of 2053 patients from 2 real-world cohorts and 2 multicentric randomized controlled trials in China were enrolled, among which 2053 CHB patients were included in the cross-sectional analysis, and 889 CHB patients with paired liver biopsies before and after 72 or 104 weeks of treatment were included in the longitudinal analysis. RESULTS: In the cross-sectional analysis, the areas under the receiver operating characteristic curve of aMAP in diagnosing cirrhosis and advanced fibrosis were 0.788 and 0.757, which were comparable with or significantly higher than those of the fibrosis index based on 4 factors and the aspartate aminotransferase-platelet ratio. The stepwise approach using aMAP and LSM further improved performance in detecting cirrhosis and advanced fibrosis with the smallest uncertainty area (29.7% and 46.2%, respectively) and high accuracy (82.3% and 79.8%, respectively). In the longitudinal analysis, we established a novel model (aMAP-LSM model) by calculating aMAP and LSM results before and after treatment, which had satisfactory performance in diagnosing cirrhosis and advanced fibrosis after treatment (area under the receiver operating characteristic curve, 0.839 and 0.840, respectively), especially for those with a significant decrease in LSM after treatment (vs LSM alone, 0.828 vs 0.748; P < .001 [cirrhosis]; 0.825 vs 0.750; P < .001 [advanced fibrosis]). CONCLUSIONS: The aMAP score is a promising noninvasive tool for diagnosing fibrosis in CHB patients. The aMAP-LSM model could accurately estimate fibrosis stage for treated CHB patients.

N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and fibrosis in MAFLD.

BACKGROUND AND AIMS: With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately screen high-risk MAFLD patients for liver inflammation and fibrosis. We aimed to develop a novel sequential algorithm based on N-terminal propeptide of type 3 collagen (PRO-C3) for disease risk stratification in patients with MAFLD. METHODS: A derivation and independent validation cohort of 327 and 142 patients with biopsy-confirmed MAFLD were studied. We compared the diagnostic performances of various non-invasive scores in different disease states, and a novel sequential algorithm was constructed by combining the best performing non-invasive scores. RESULTS: For patients with high-risk progressive steatohepatitis (i.e., steatohepatitis + NAFLD activity score ≥ 4 + F ≥ 2), the AUROC of FAST score was 0.801 (95% confidence interval (CI): 0.739-0.863), and the negative predictive value (NPV) was 0.951. For advanced fibrosis (≥ F3) and cirrhosis (F4), the AUROCs of ADAPT and Agile 4 were 0.879 (95%CI 0.825-0.933) and 0.943 (95%CI 0.892-0.994), and the NPV were 0.972 and 0.992. Sequential algorithm of ADAPT + Agile 4 combination was better than other combinations for risk stratification of patients with severe fibrosis (AUROC = 0.88), with similar results in the validation cohort. Meanwhile, in all subgroup analyses (stratifying by sex, age, diabetes, NAS, BMI and ALT), ADAPT + Agile 4 had a good diagnostic performance. CONCLUSIONS: The new sequential algorithm reliably identifies liver inflammation and fibrosis in MAFLD, making it easier to exclude low-risk patients and recommending high-risk MAFLD patients for clinical trials and emerging pharmacotherapies.

Nestin as a diagnostic and prognostic marker for combined hepatocellular-cholangiocarcinoma.

BACKGROUND & AIMS: Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) associated with a poor prognosis. Given the challenges in its identification and its clinical implications, biomarkers are critically needed. We aimed to investigate the diagnostic and prognostic value of the immunohistochemical expression of Nestin, a progenitor cell marker, in a large multicentric series of PLCs. METHODS: We collected 461 cHCC-CCA samples from 32 different clinical centers. Control cases included 368 hepatocellular carcinomas (HCCs) and 221 intrahepatic cholangiocarcinomas (iCCAs). Nestin immunohistochemistry was performed on whole tumor sections. Diagnostic and prognostic performances of Nestin expression were determined using receiver-operating characteristic curves and Cox regression modeling. RESULTS: Nestin was able to distinguish cHCC-CCA from HCC with AUCs of 0.85 and 0.86 on surgical and biopsy samples, respectively. Performance was lower for the distinction of cHCC-CCA from iCCA (AUCs of 0.59 and 0.60). Nestin, however, showed a high prognostic value, allowing identification of the subset of cHCC-CCA ("Nestin High", >30% neoplastic cells with positive staining) associated with the worst clinical outcome (shorter disease-free and overall survival) after surgical resection and liver transplantation, as well as when assessment was performed on biopsies. CONCLUSION: We show in different clinical settings that Nestin has diagnostic value and that it is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy. LAY SUMMARY: There are different types of primary liver cancers (i.e. cancers that originate in the liver). Accurately identifying a specific subtype of primary liver cancer (and determining its associated prognosis) is important as it can have a major impact on treatment allocation. Herein, we show that a protein called Nestin could be used to refine risk stratification and improve treatment allocation for patients with combined hepatocellular carcinoma, a rare but highly aggressive subtype of primary liver cancer.

Validation model of fibrosis-8 index score to predict significant fibrosis among patients with nonalcoholic fatty liver disease.

BACKGROUND: Identifying hepatic fibrosis is crucial for nonalcoholic fatty liver disease (NAFLD) management. The fibrosis-8 (FIB-8) score, recently developed by incorporating four additional variables into the fibrosis-4 (FIB-4) score, showed better performance in predicting significant fibrosis in NAFLD. AIM: To validate the FIB-8 score in a biopsy-proven NAFLD cohort and compare the diagnostic performance of the FIB-8 and FIB-4 scores and NAFLD fibrosis score (NFS) for predicting significant fibrosis. METHODS: We collected the data of biopsy-proven NAFLD patients from three Asian centers in three countries. All the patients with available variables for the FIB-4 score (age, platelet count, and aspartate and alanine aminotransferase levels) and FIB-8 score (the FIB-4 variables plus 4 additional parameters: The body mass index (BMI), albumin to globulin ratio, gamma-glutamyl transferase level, and presence of diabetes mellitus) were included. The fibrosis stage was scored using nonalcoholic steatohepatitis CRN criteria, and significant fibrosis was defined as at least fibrosis stage 2. RESULTS: A total of 511 patients with biopsy-proven NAFLD and complete data were included for validation. Of these 511 patients, 271 (53.0%) were female, with a median age of 51 (interquartile range: 41, 58) years. The median BMI was 29 (26.3, 32.6) kg/m2, and 268 (52.4%) had diabetes. Among the 511 NAFLD patients, 157 (30.7%) had significant fibrosis (≥ F2). The areas under the receiver operating characteristic curves of the FIB-8 and FIB-4 scores and NFS for predicting significant fibrosis were 0.774, 0.743, and 0.680, respectively. The FIB-8 score demonstrated significantly better performance for predicting significant fibrosis than the NFS (P = 0.001) and was also clinically superior to FIB-4, although statistical significance was not reached (P = 0.073). The low cutoff point of the FIB-8 score for predicting significant fibrosis of 0.88 showed 92.36% sensitivity, and the high cutoff point of the FIB-8 score for predicting significant fibrosis of 1.77 showed 67.51% specificity. CONCLUSION: We demonstrated that the FIB-8 score had significantly better performance for predicting significant fibrosis in NAFLD patients than the NFS, as well as clinically superior performance vs the FIB-4 score in an Asian population. A novel simple fibrosis score comprising commonly accessible basic laboratories may be beneficial to use for an initial assessment in primary care units, excluding patients with significant liver fibrosis and aiding in patient selection for further hepatologist referral.

Reliability of the nonalcoholic steatohepatitis clinical research network and steatosis activity fibrosis histological scoring systems.

BACKGROUND AND AIM: We aimed to determine whether lobular inflammation and ballooning grades in the Non-alcoholic Steatohepatitis Clinical Research Network (NASH CRN) scoring system can be directly translated into the same for the Steatosis Activity Fibrosis scoring system (SAF) and to look at intra-observer and inter-observer agreement for each individual histological component and for diagnosis of non-alcoholic steatohepatitis (NASH) using the two scoring systems. METHODS: Four pathologists from two Asian centers scored 20 digitalized slides, twice using the NASH CRN, twice using the SAF. Intra-observer and inter-observer agreement was analyzed using Fleiss' kappa, weighted kappa, or Cohen kappa, where appropriate. RESULTS: The intra-observer discrepancy rate when using the NASH CRN compared with the SAF was higher than when using the individual scoring system for lobular inflammation (15% comparing both scoring systems vs 10% and 1.8% for the NASH CRN and the SAF, respectively) and hepatocyte ballooning (33.8% vs 12.5% and 5%, respectively), but not for diagnosis of NASH (6.3% vs 6.3% and 0%, respectively). Intra-observer and inter-observer agreement was substantial to almost perfect, except for inter-observer agreement for lobular inflammation and diagnosis of NASH, which was only fair to moderate in most instances. CONCLUSION: These findings do not support the direct inter-translation between the NASH CRN and the SAF. However, the diagnosis of NASH during examinations using the NASH CRN may be comparable with diagnosis of NASH using the SAF, vice versa. The inter-observer agreement for lobular inflammation and NASH diagnosis needs to be improved.

Unique molecular characteristics of NAFLD-associated liver cancer accentuate ß-catenin/TNFRSF19-mediated immune evasion.

BACKGROUND & AIMS: The hepatic manifestation of the metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), can lead to the development of hepatocellular carcinoma (HCC). Despite a strong causative link, NAFLD-HCC is often underrepresented in systematic genome explorations. METHODS: Herein, tumor-normal pairs from 100 patients diagnosed with NAFLD-HCC were subject to next-generation sequencing. Bioinformatic analyses were performed to identify key genomic, epigenomic and transcriptomic events associated with the pathogenesis of NAFLD-HCC. Establishment of primary patient-derived NAFLD-HCC culture was used as a representative human model for downstream in vitro investigations of the underlying CTNNB1 S45P driver mutation. A syngeneic immunocompetent mouse model was used to further test the involvement of CTNNB1mutand TNFRSF19 in reshaping the tumor microenvironment. RESULTS: Mutational processes operative in the livers of patients with NAFLD inferred susceptibility to tumor formation through defective DNA repair pathways. Dense promoter mutations and dysregulated transcription factors accentuated activated transcriptional regulation in NAFLD-HCC, in particular the enrichment of MAZ-MYC activities. Somatic events common in HCCs arising from NAFLD and viral hepatitis B infection underscore similar driver pathways, although an incidence shift highlights CTNNB1mut dominance in NAFLD-HCC (33%). Immune exclusion correlated evidently with CTNNB1mut. Chromatin immunoprecipitation-sequencing integrated with transcriptome and immune profiling revealed a unique transcriptional axis, wherein CTNNB1mut leads to an upregulation of TNFRSF19 which subsequently represses senescence-associated secretory phenotype-like cytokines (including IL6 and CXCL8). This phenomenon could be reverted by the Wnt-modulator ICG001. CONCLUSIONS: The unique mutational processes in the livers of patients with NAFLD and NAFLD-HCC allude to a "field effect" involving a gain-of-function role of CTNNB1 mutations in immune exclusion. LAY SUMMARY: The increasing prevalence of metabolic syndrome in adult populations means that NAFLD is poised to be the major cause of liver cancer in the 21st century. We showed a strong "field effect" in the livers of patients with NAFLD, wherein activated ß-catenin was involved in reshaping the tumor-immune microenvironment.

Among simple non-invasive scores, Pro-C3 and ADAPT best exclude advanced fibrosis in Asian patients with MAFLD.

BACKGROUND: With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence increasing, it is necessary to identify patients with advanced fibrosis (F3-F4 stages). We evaluated the performance of new biomarkers and algorithms for diagnosing advanced fibrosis in an Asian population. METHODS: Data from two Asian cohorts (including 851 biopsy-proven MAFLD [578 from Wenzhou, 273 from Hong Kong]) were studied. The association between N-terminal propeptide of type 3 collagen (PRO-C3) and the histologic stage of liver fibrosis was analyzed by multivariable linear regression. The area under the receiver operating characteristic curve (AUROC) was used to test the diagnostic performance of serum PRO-C3 and the ADAPT score for advanced fibrosis and compared them to other established non-invasive tests. RESULTS: Serum PRO-C3 levels increased progressively across liver fibrosis stages and correlated with advanced fibrosis (P < 0.001). The ADAPT score had an AUROC of 0.865 (95% confidence interval 0.829-0.901) for advanced fibrosis; the accuracy, sensitivity and negative predictive values were 81.4%, 82.2% and 96.1%, respectively. This result was better compared to that of PRO-C3 alone or other non-invasive fibrosis biomarkers (aspartate aminotransferase-to-platelet ratio index, Fibrosis-4, BARD, and NAFLD fibrosis score). In subgroup analyses (including sex, age, diabetes, NAFLD activity score, body mass index or serum alanine aminotransferase levels), the ADAPT score had good diagnostic performance. CONCLUSION: PRO-C3 and the ADAPT score reliably exclude advanced fibrosis in MAFLD patients and reduce the need for liver biopsy.

Ablative-Transarterial Radioembolization resulting in complete histopathological response of hepatocellular carcinoma in the resected liver specimen after salvage hepatectomy.

INTRODUCTION: Hepatocellular carcinoma (HCC) is a common disease. Many patients at the time of diagnosis of HCC are in advanced stages and cannot benefit from curative treatment. Palliative treatments remain the only treatment option. Advances in palliative treatment can occasionally downstage HCC and induce enough liver hypertrophy to allow salvage hepatectomy to be performed on patients with initially unresectable HCC. We herein present a patient who underwent salvage hepatectomy after successful Ablative-Transarterial Radioembolization (A-TARE) with complete histopathologic response in the resected liver specimen. CASE REPORT: A 67-year old obese patient presented with a 9.7 cm HCC at liver segment 8, with local tumour extension to involve segments 4,5 and 7. Initial workup suggested the tumour to be unresectable. A-TARE with yttrium-90 microspheres was given. Further workup 4 months after A-TARE showed the tumour to be downstaged with adequate hypertrophy of future liver remnant. Salvage hepatectomy became possible and the patient underwent salvage trisectionectomy 5 months after A-TARE. He recovered uneventfully from the operation. Histopathological examination of the resected liver specimen showed no viable tumour cells inside a fibrous mass which corresponded to the radiologic residual tumour. DISCUSSION: Salvage hepatectomy should be offered to patients after tumour downstaging with A-TARE as viable malignant cells are likely to persist. Complete response with no viable tumour cells in the resected liver specimen, to our knowledge, has never been reported in literature. CONCLUSION: A-TARE was able to induce complete histopathological response in a patient who initially presented with a large and unresectable HCC mass.

Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis.

Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non-alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.