1
|
Reiberger T, Lens S, Cabibbo G, Nahon P, Zignego AL, Deterding K, Elsharkawy AM, Forns X. EASL position paper on clinical follow-up after HCV cure. J Hepatol 2024; 81:326-344. [PMID: 38845253 DOI: 10.1016/j.jhep.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 04/05/2024] [Indexed: 07/26/2024]
Abstract
Following the advent of direct-acting antivirals (DAAs), hepatitis C virus (HCV) infection can be cured in almost all infected patients. This has led to a number of clinical questions regarding the optimal management of the millions of patients cured of HCV. This position statement provides specific guidance on the appropriate follow-up after a sustained virological response in patients without advanced fibrosis, those with compensated advanced chronic liver disease, and those with decompensated cirrhosis. Guidance on hepatocellular carcinoma risk assessment and the management of extrahepatic manifestations of HCV is also provided. Finally, guidance is provided on the monitoring and treatment of reinfection in at-risk patients. The recommendations are based on the best available evidence and are intended to help healthcare professionals involved in the management of patients after treatment for HCV.
Collapse
Affiliation(s)
- Thomas Reiberger
- Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Sabela Lens
- Liver Unit, Hospital Clinic Barcelona. IDIBAPS. Liver and Digestive Diseases Networking Biomedical Research Centre (CIBERehd). University of Barcelona. Spain
| | - Giuseppe Cabibbo
- Section of Gastroenterology and Hepatology, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties PROMISE, University of Palermo, Italy
| | - Pierre Nahon
- AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Unit, Bobigny; Université Sorbonne Paris Nord, F-93000 Bobigny; Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de Recherche des Cordeliers, Université de Paris, France
| | - Anna Linda Zignego
- Department of Clinical and Experimental Medicine, University of Florence, Florence, Italy
| | - Katja Deterding
- Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School. Germany
| | - Ahmed M Elsharkawy
- Liver Unit, Queen Elizabeth Hospital Birmingham. NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham, United Kingdom
| | - Xavier Forns
- Liver Unit, Hospital Clinic Barcelona. IDIBAPS. Liver and Digestive Diseases Networking Biomedical Research Centre (CIBERehd). University of Barcelona. Spain.
| |
Collapse
|
2
|
Lani L, Stefanini B, Trevisani F. Surveillance for Hepatocellular Carcinoma in Patients with Successfully Treated Viral Disease of the Liver: A Systematic Review. Liver Cancer 2024; 13:376-388. [PMID: 39114761 PMCID: PMC11305665 DOI: 10.1159/000535497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/23/2023] [Indexed: 08/10/2024] Open
Abstract
Background Surveillance for hepatocellular carcinoma (HCC) has been proven to increase the proportion of tumors detected at early stages and the chance of receiving curative therapies, reducing mortality by about 30%. Summary Current recommendations consist of a semi-annual abdominal ultrasound with or without serum alpha-fetoprotein measurement in patients with cirrhosis and specific subgroups of populations with chronic viral hepatitis. Antiviral therapies, such as nucleot(s)ide analogs that efficiently suppress the replication of hepatitis B virus (HBV) and direct-acting antiviral drugs able to eliminate the hepatitis C virus (HCV) in >90% of patients, have radically changed the outcomes of viral liver disease and decreased, but not eliminated, the risk of HCC in both cirrhotic and non-cirrhotic patients. HCC risk is a key starting point for implementing a cost-effective surveillance and should also guide the decision-making process concerning its modality. As the global number of effectively treated viral patients continues to rise, there is a pressing need to identify those for whom the benefit-to-harm ratio of surveillance is favorable and to determine how to conduct cost-effective screening on such patients. Key Messages This article addresses this topic and attempts to determine which patients should continue HCC surveillance after HBV suppression or HCV eradication, based on cost-effectiveness principles and the fact that HCC risk declines over time. We also formulate a proposal for a surveillance algorithm that switches the use of surveillance for HCC from the "one-size-fits-all" approach to individualized programs based on oncologic risk (precision surveillance).
Collapse
Affiliation(s)
- Lorenzo Lani
- Unit of Semeiotics, Liver, and Alcohol-related diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Benedetta Stefanini
- Unit of Semeiotics, Liver, and Alcohol-related diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Franco Trevisani
- Unit of Semeiotics, Liver, and Alcohol-related diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| |
Collapse
|
3
|
Wang M, Zhuang B, Yu S, Li G. Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices. Front Oncol 2024; 14:1397505. [PMID: 38952558 PMCID: PMC11215019 DOI: 10.3389/fonc.2024.1397505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC.
Collapse
Affiliation(s)
- Mengxia Wang
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
| | - Bo Zhuang
- Department of Hepatobiliary Surgery, The Affliated Jinhua Hospital of Zhejiang University School of Medicine, Jinhua, Zhejiang, China
| | - Shian Yu
- Department of Hepatobiliary Surgery, The Affliated Jinhua Hospital of Zhejiang University School of Medicine, Jinhua, Zhejiang, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| |
Collapse
|
4
|
Zhao R, Gu L, Ke X, Deng X, Li D, Ma Z, Wang Q, Zheng H, Yang Y. Risk prediction of cholangitis after stent implantation based on machine learning. Sci Rep 2024; 14:13715. [PMID: 38877118 PMCID: PMC11178872 DOI: 10.1038/s41598-024-64734-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/12/2024] [Indexed: 06/16/2024] Open
Abstract
The risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial intelligence methods to predict cholangitis risk more accurately, according to patients after stent implantation in patients' MOJ clinical data. This retrospective study included 218 patients with MOJ undergoing ERCP surgery. A total of 27 clinical variables were collected as input variables. Seven models (including univariate analysis and six machine learning models) were trained and tested for classified prediction. The model' performance was measured by AUROC. The RFT model demonstrated excellent performances with accuracies up to 0.86 and AUROC up to 0.87. Feature selection in RF and SHAP was similar, and the choice of the best variable subset produced a high performance with an AUROC up to 0.89. We have developed a hybrid machine learning model with better predictive performance than traditional LR prediction models, as well as other machine learning models for cholangitis based on simple clinical data. The model can assist doctors in clinical diagnosis, adopt reasonable treatment plans, and improve the survival rate of patients.
Collapse
Affiliation(s)
- Rui Zhao
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Lin Gu
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Xiquan Ke
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Xiaojing Deng
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Dapeng Li
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Zhenzeng Ma
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Qizhi Wang
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China
| | - Hailun Zheng
- The First Affiliated Hospital of Bengbu Medical University, Yanhuai Road, Bengbu, 233000, China.
| | - Yong Yang
- School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China.
| |
Collapse
|
5
|
Nahon P, Layese R, Ganne-Carrié N, Moins C, N'Kontchou G, Chaffaut C, Ronot M, Audureau E, Durand-Zaleski I, Natella PA. The clinical and financial burden of nonhepatocellular carcinoma focal lesions detected during the surveillance of patients with cirrhosis. Hepatology 2024; 79:813-828. [PMID: 37774387 DOI: 10.1097/hep.0000000000000615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/01/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND AND AIMS HCC surveillance is challenged by the detection of hepatic focal lesions (HFLs) of other types. This study aimed to describe the incidence, characteristics, outcomes, and costs of non-HCC HFL detected during surveillance. APPROACH AND RESULTS We retrospectively analyzed nonstandardized workup performed in French patients included in HCC surveillance programs recruited in 57 French tertiary centers (ANRS CirVir and CIRRAL cohorts, HCC 2000 trial). The overall cost of workup was evaluated, with an estimation of an average cost per patient for the entire population and per lesion detected. A total of 3295 patients were followed up for 59.8 months, 391 (11.9%) patients developed HCCs (5-year incidence: 12.1%), and 633 (19.2%) developed non-HCC HFLs (5-year incidence: 21.8%). Characterization of non-HCC HFL required a median additional of 0.7 exams per year. A total of 11.8% of non-HCC HFLs were not confirmed on recall procedures, and 19.6% of non-HCC HFLs remained undetermined. A definite diagnosis of benign liver lesions was made in 65.1%, and malignant tumors were diagnosed in 3.5%. The survival of patients with benign or undetermined non-HCC HFL was similar to that of patients who never developed any HFL (5-year survival 92% vs. 88%, p = 0.07). The average cost of the diagnostic workup was 1087€ for non-HCC HFL and €1572 for HCC. CONCLUSIONS Non-HCC HFLs are frequently detected in patients with cirrhosis, and do not impact prognosis, but trigger substantial costs. This burden must be considered in cost-effectiveness analyses of future personalized surveillance strategies.
Collapse
Affiliation(s)
- Pierre Nahon
- AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Department, Bobigny; Université Sorbonne Paris Nord, Bobigny, France
- Inserm, UMR-1138 Functional Genomics of Solid Tumors department, Centre de recherche des Cordeliers, Université de Paris, Paris, France
| | - Richard Layese
- Univ Paris Est Créteil, INSERM, IMRB, Equipe CEpiA (Clinical Epidemiology and Ageing), Unité de Recherche Clinique (URC Mondor), Public health department, Assistance Publique Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Créteil, France
| | - Nathalie Ganne-Carrié
- AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Department, Bobigny; Université Sorbonne Paris Nord, Bobigny, France
- Inserm, UMR-1138 Functional Genomics of Solid Tumors department, Centre de recherche des Cordeliers, Université de Paris, Paris, France
| | - Cécile Moins
- Clinical Research Department, ANRS | Emerging Infectious Diseases, Paris, France
| | - Gisèle N'Kontchou
- AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Department, Bobigny; Université Sorbonne Paris Nord, Bobigny, France
- Inserm, UMR-1138 Functional Genomics of Solid Tumors department, Centre de recherche des Cordeliers, Université de Paris, Paris, France
| | - Cendrine Chaffaut
- SBIM, APHP, Hôpital Saint-Louis, Paris, Inserm, UMR-1153, ECSTRA department, Paris, France
| | - Maxime Ronot
- APHP, Hôpital Beaujon, Radiology department, Hôpital Beaujon, APHP. Nord, Clichy-Sous-Bois, & Université Paris Cité, Paris, France
| | - Etienne Audureau
- Univ Paris Est Créteil, INSERM, IMRB, Equipe CEpiA (Clinical Epidemiology and Ageing), Unité de Recherche Clinique (URC Mondor), Public health department, Assistance Publique Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Créteil, France
| | - Isabelle Durand-Zaleski
- Université de Paris, CRESS, INSERM, INRA, URCECo department, AP-HP, Hôpital de l'Hôtel Dieu, Paris, France
| | - Pierre-André Natella
- Univ Paris Est Créteil, INSERM, IMRB, Equipe CEpiA (Clinical Epidemiology and Ageing), Unité de Recherche Clinique (URC Mondor), Public health department, Assistance Publique Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Créteil, France
| |
Collapse
|
6
|
Nahon P, Ronot M, Sutter O, Natella PA, Baloul S, Durand-Zaleski I, Audureau E. Study protocol for FASTRAK: a randomised controlled trial evaluating the cost impact and effectiveness of FAST-MRI for HCC suRveillance in pAtients with high risK of liver cancer. BMJ Open 2024; 14:e083701. [PMID: 38367972 PMCID: PMC10875554 DOI: 10.1136/bmjopen-2023-083701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/19/2024] Open
Abstract
INTRODUCTION The surveillance of hepatocellular carcinoma (HCC) using semi-annual liver ultrasound (US) is justified in patients with cirrhosis. In this context, US has a low sensitivity (<30%) for the detection of HCC at the very early stage (ie, Barcelona clinic liver cancer (BCLC) 0, uninodular tumour <2 cm). The sensitivity of abbreviated liver MRI (AMRI) is reported to exceed 80%, but its use is hampered by costs and availability. Our hypothesis is that AMRI used as a screening examination in patients at high risk of HCC (>3% per year) could increase the rates of patients with a tumour detected at an early stage accessible to curative-intent treatment, and demonstrate its cost-effectiveness in this population. METHODS AND ANALYSIS The FASTRAK trial is a multicentre, randomised controlled trial with two parallel arms, aiming for superiority and conducted on patients at high risk for HCC (yearly HCC incidence >3%). Randomisation will be conducted on an individual basis with a centralised approach and stratification by centre. After inclusion in the trial, each patient will be randomly assigned to the experimental group (semi-annual US and AMRI) or the control group (semi-annual US alone). The main objective is to assess the cost/quality-adjusted life year and cost/patient detected with a BCLC 0 HCC in both arms. A total of 944 patients will be recruited in 37 tertiary French centres during a 36-month period and will be followed-up during 36 months. ETHICS AND DISSEMINATION The FASTRAK trial received ethical approval on 4 April 2022. Results will be disseminated via publication in peer-reviewed journals as well as presentation at international conferences. TRIAL REGISTRATION NUMBER Clinical trial number (ClinicaTrials.gov) NCT05095714.
Collapse
Affiliation(s)
| | | | | | - Pierre-André Natella
- Clinical Epidemiology and Ageing, Hôpitaux Universitaires Henri Mondor, Creteil, France
| | - Samia Baloul
- Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Isabelle Durand-Zaleski
- University of Paris, Paris, France
- URCEco, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Etienne Audureau
- CEPIA EA7376, Universite Paris-Est Creteil Val de Marne, Creteil, France
| |
Collapse
|
7
|
Fraile-López M, Alvarez-Navascués C, González-Diéguez ML, Cadahía V, Chiminazzo V, Castaño A, Varela M, Rodríguez M. Predictive models for hepatocellular carcinoma development after sustained virological response in advanced hepatitis C. GASTROENTEROLOGIA Y HEPATOLOGIA 2023; 46:754-763. [PMID: 36716928 DOI: 10.1016/j.gastrohep.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/07/2022] [Accepted: 01/21/2023] [Indexed: 02/01/2023]
Abstract
BACKGROUND & AIMS Life-long hepatocellular carcinoma (HCC) surveillance is recommended after sustained virological response (SVR) in patients with advanced hepatitis C. Since the identification of patients who could be safely discontinued for surveillance is essential, we aimed to identify subsets of patients with low-risk HCC. METHODS 491 patients with advanced and compensated fibrosis (≥F3) were prospectively followed after achieving SVR with interferon-free therapies. Clinical-biological parameters and liver stiffness measurement (LSM) were performed before starting treatment (ST) and at SVR, and HCC surveillance was carried out. RESULTS During a median follow-up of 49.8 months, 29 (5.9%) patients developed HCC [incidence rate: 1.6/100 patient-years (PYs)]. Two predictive models based on LSM (Model-A) or FIB-4 score (Model-B) were proposed. Only SVR parameters were included in the models, because they showed a higher accuracy for predicting HCC than ST measurements. Variables independently associated with HCC were LSM (HR, 1.03; 95% CI, 1.01-1.05), age (HR, 1.04; 95% CI, 1.01-1.08) and albumin levels (HR, 0.90; 95% CI, 0.84-0.97) in Model-A, and FIB-4 (HR, 1.22; 95% CI, 1.08-1.37) and albumin (HR, 0.90; 95% CI, 0.84-0.97) in model-B. Both models allow HCC risk stratification, identifying low-risk groups with an HCC incidence rate of 0.16/100 and 0.25/100 PYs, respectively. An overall increased hazard of HCC was observed over time. CONCLUSION Simple models based on non-invasive markers of liver fibrosis, LSM or FIB-4, together with age and albumin levels at SVR permit to identify subsets of patients with HCC risk clearly <1%/year, for whom HCC surveillance might not be cost-effective.
Collapse
Affiliation(s)
- Miguel Fraile-López
- Liver Unit, Division of Gastroenterology & Hepatology, Hospital Universitario Central de Asturias, Oviedo, Spain.
| | - Carmen Alvarez-Navascués
- Liver Unit, Division of Gastroenterology & Hepatology, Hospital Universitario Central de Asturias, Oviedo, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - María Luisa González-Diéguez
- Liver Unit, Division of Gastroenterology & Hepatology, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Valle Cadahía
- Liver Unit, Division of Gastroenterology & Hepatology, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Valentina Chiminazzo
- Plataforma de Bioestadística y Epidemiología del Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
| | - Andrés Castaño
- Liver Unit, Division of Gastroenterology & Hepatology, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - María Varela
- Liver Unit, Division of Gastroenterology & Hepatology, Hospital Universitario Central de Asturias, Oviedo, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain; Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Oviedo, Spain; University of Oviedo, Oviedo, Spain
| | - Manuel Rodríguez
- Liver Unit, Division of Gastroenterology & Hepatology, Hospital Universitario Central de Asturias, Oviedo, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain; University of Oviedo, Oviedo, Spain
| |
Collapse
|
8
|
Zou Y, Yue M, Jia L, Wang Y, Chen H, Zhang A, Xia X, Liu W, Yu R, Yang S, Huang P. Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data. BMC Cancer 2023; 23:1147. [PMID: 38007418 PMCID: PMC10676612 DOI: 10.1186/s12885-023-11628-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 11/09/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model. METHODS A total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison. RESULTS During a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838-0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively). CONCLUSIONS Our longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice.
Collapse
Affiliation(s)
- Yanzheng Zou
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Ming Yue
- Department of Infectious Diseases, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Linna Jia
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yifan Wang
- Department of Infectious Disease, Jurong Hospital Affiliated to Jiangsu University, Jurong, China
| | - Hongbo Chen
- Department of Infectious Disease, Jurong Hospital Affiliated to Jiangsu University, Jurong, China
| | - Amei Zhang
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Yunnan, China
| | - Xueshan Xia
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Yunnan, China
- Kunming Medical University, Kunming, China
| | - Wei Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
- Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Rongbin Yu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Sheng Yang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Peng Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| |
Collapse
|
9
|
Innes H, Nahon P. Statistical perspectives on using hepatocellular carcinoma risk models to inform surveillance decisions. J Hepatol 2023; 79:1332-1337. [PMID: 37210001 DOI: 10.1016/j.jhep.2023.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/18/2023] [Accepted: 05/03/2023] [Indexed: 05/22/2023]
Abstract
More than 50,000 people are diagnosed with hepatocellular carcinoma (HCC) every year in Europe. Many cases are known to specialist liver centres years before they present with HCC. Despite this, HCC is usually detected at a late stage, when prognosis is very poor. For more than two decades, clinical guidelines have recommended uniform surveillance for all patients with cirrhosis. However, studies continue to show that this broad-based approach is inefficient and poorly implemented in practice. A "personalised" approach, where the surveillance regimen is customised to the needs of the patient, is gaining growing support in the clinical community. The cornerstone of personalised surveillance is the HCC risk model - a mathematical equation predicting a patient's individualised probability of developing HCC within a specific time window. However, although numerous risk models have now been published, few are being used in routine care to inform HCC surveillance decisions. In this article, we discuss methodological issues stymieing the use of HCC risk models in routine practice - highlighting biases, evidence gaps and misconceptions that future research must address.
Collapse
Affiliation(s)
- Hamish Innes
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK; Lifespan and Population Health, University of Nottingham, Nottingham, UK; Public Health Scotland, Glasgow, UK.
| | - Pierre Nahon
- APHP, Liver Unit, Bobigny, France; Université Sorbonne Paris Nord, F-93000, Bobigny, France; Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de Recherche des Cordeliers, Université de Paris, Paris, France
| |
Collapse
|
10
|
Bhattacharya S, Mahato RK, Singh S, Bhatti GK, Mastana SS, Bhatti JS. Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches. Life Sci 2023; 332:122110. [PMID: 37734434 DOI: 10.1016/j.lfs.2023.122110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023]
Abstract
Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.
Collapse
Affiliation(s)
- Srinjan Bhattacharya
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Rahul Kumar Mahato
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Satwinder Singh
- Department of Computer Science and Technology, Central University of Punjab, Bathinda 151401, Punjab, India.
| | - Gurjit Kaur Bhatti
- Department of Medical Lab Technology, University Institute of Applied Health Sciences, Chandigarh University, Mohali, India
| | - Sarabjit Singh Mastana
- School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Leicestershire, Loughborough LE11 3TU, UK.
| | - Jasvinder Singh Bhatti
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India.
| |
Collapse
|
11
|
Ajuwon BI, Awotundun ON, Richardson A, Roper K, Sheel M, Rahman N, Salako A, Lidbury BA. Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact. Int J Med Inform 2023; 179:105244. [PMID: 37820561 DOI: 10.1016/j.ijmedinf.2023.105244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 09/08/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited. OBJECTIVE This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections. METHODS We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV). RESULTS We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done. CONCLUSION Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.
Collapse
Affiliation(s)
- Busayo I Ajuwon
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia; Department of Biosciences and Biotechnology, Faculty of Pure and Applied Sciences, Kwara State University, Malete, Nigeria.
| | - Oluwatosin N Awotundun
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Alice Richardson
- Statistical Support Network, The Australian National University, Acton, ACT, Australia
| | - Katrina Roper
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia
| | - Meru Sheel
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, Australia
| | - Nurudeen Rahman
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Abideen Salako
- Department of Clinical Sciences, Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | - Brett A Lidbury
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia
| |
Collapse
|
12
|
He Z, Tang D. Perioperative predictors of outcome of hepatectomy for HBV-related hepatocellular carcinoma. Front Oncol 2023; 13:1230164. [PMID: 37519791 PMCID: PMC10373594 DOI: 10.3389/fonc.2023.1230164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Hepatitis B virus (HBV) is identified as a major risk factor for hepatocellular carcinoma (HCC), resulting in so-called hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC). Hepatectomy for HCC is acknowledged as an efficient treatment strategy, especially for early HCC. Furthermore, patients with advanced HCC can still obtain survival benefits through surgical treatment combined with neoadjuvant therapy, adjuvant therapy, transcatheter arterial chemoembolization, and radiofrequency ablation. Therefore, preoperative and postoperative predictors of HBV-related HCC have crucial indicative functions for the follow-up treatment of patients with feasible hepatectomy. This review covers a variety of research results on preoperative and postoperative predictors of hepatectomy for HBV-related HCC over the past decade and in previous landmark studies. The relevant contents of Hepatitis C virus-related HCC, non-HBV non-HCV HCC, and the artificial intelligence application in this field are briefly addressed in the extended content. Through the integration of this review, a large number of preoperative and postoperative factors can predict the prognosis of HBV-related HCC, while most of the predictors have no standardized thresholds. According to the characteristics, detection methods, and application of predictors, the predictors can be divided into the following categories: 1. serological and hematological predictors, 2. genetic, pathological predictors, 3. imaging predictors, 4. other predictors, 5. analysis models and indexes. Similar results appear in HCV-related HCC, non-HBV non-HCV HCC. Predictions based on AI and big biological data are actively being applied. A reasonable prediction model should be established based on the economic, health, and other levels in specific countries and regions.
Collapse
|
13
|
Singal AG, Sanduzzi-Zamparelli M, Nahon P, Ronot M, Hoshida Y, Rich N, Reig M, Vilgrain V, Marrero J, Llovet JM, Parikh ND, Villanueva A. International Liver Cancer Association (ILCA) white paper on hepatocellular carcinoma risk stratification and surveillance. J Hepatol 2023; 79:226-239. [PMID: 36854345 DOI: 10.1016/j.jhep.2023.02.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/16/2023] [Accepted: 02/04/2023] [Indexed: 03/02/2023]
Abstract
Major research efforts in liver cancer have been devoted to increasing the efficacy and effectiveness of surveillance for hepatocellular carcinoma (HCC). As with other cancers, surveillance programmes aim to detect tumours at an early stage, facilitate curative-intent treatment, and reduce cancer-related mortality. HCC surveillance is supported by a large randomised-controlled trial in patients with chronic HBV infection and several cohort studies in cirrhosis; however, effectiveness in clinical practice is limited by several barriers, including inadequate risk stratification, underuse of surveillance, and suboptimal accuracy of screening tests. There are several proposed strategies to address these limitations, including risk stratification algorithms and biomarkers to better identity at-risk individuals, interventions to increase surveillance, and emerging imaging- and blood-based surveillance tests with improved sensitivity and specificity for early HCC detection. Beyond clinical validation, data are needed to establish clinical utility, i.e. increased early tumour detection and reduced HCC-related mortality. If successful, these data could facilitate a precision screening paradigm in which surveillance strategies are tailored to individual HCC risk to maximise overall surveillance value. However, practical and logistical considerations must be considered when designing and implementing these validation efforts. To address these issues, ILCA (the International Liver Cancer Association) adjourned a single topic workshop on HCC risk stratification and surveillance in June 2022. Herein, we present a white paper on these topics, including the status of the field, ongoing research efforts, and barriers to the translation of emerging strategies.
Collapse
Affiliation(s)
- Amit G Singal
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Marco Sanduzzi-Zamparelli
- BCLC Group, Liver Oncology Unit, Liver Unit, Hospital Clinic of Barcelona, Institut d'Investigacions, Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERehd, University of Barcelona, Barcelona, Spain
| | - Pierre Nahon
- APHP, Liver Unit, Bobigny, Université Sorbonne Paris Nord, F-93000 Bobigny, France; Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de recherche des Cordeliers, Université de Paris, Paris, France
| | - Maxime Ronot
- Université Paris Cité, CRI INSERM UMR 1149, Paris & Department of radiology, Hôpital Beaujon, APHP. Nord, Clichy, France
| | - Yujin Hoshida
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nicole Rich
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Maria Reig
- BCLC Group, Liver Oncology Unit, Liver Unit, Hospital Clinic of Barcelona, Institut d'Investigacions, Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERehd, University of Barcelona, Barcelona, Spain
| | - Valerie Vilgrain
- Université Paris Cité, CRI INSERM UMR 1149, Paris & Department of radiology, Hôpital Beaujon, APHP. Nord, Clichy, France
| | - Jorge Marrero
- Department of Internal Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Josep M Llovet
- Mount Sinai Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Translational Research in Hepatic Oncology, Liver Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Hospital Clinic, University of Barcelona, Barcelona, Catalonia, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Neehar D Parikh
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Augusto Villanueva
- Mount Sinai Liver Cancer Program, Division of Liver Diseases, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
14
|
Ishido S, Tamaki N, Kurosaki M, Mori N, Tsuji K, Hasebe C, Mashiba T, Ochi H, Yasui Y, Akahane T, Furuta K, Kobashi H, Fujii H, Ishii T, Marusawa H, Kondo M, Kusakabe A, Yoshida H, Uchida Y, Tada T, Nakamura S, Mitsuda A, Ogawa C, Arai H, Murohisa T, Uebayashi M, Izumi N. Necessity for surveillance for hepatocellualr carcinoma in older patients with chronic hepatitis C who achieved sustained virological response. JGH Open 2023; 7:424-430. [PMID: 37359109 PMCID: PMC10290273 DOI: 10.1002/jgh3.12914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/29/2023] [Accepted: 05/11/2023] [Indexed: 06/28/2023]
Abstract
Background and Aim Hepatocellular carcinoma (HCC) surveillance in low-risk patients (annual incidence <1.5%) is not recommended per the American Association for the Study of Liver Diseases guidelines. Because patients with chronic hepatitis C with non-advanced fibrosis who have achieved sustained virological response (SVR) have a low risk of HCC, HCC surveillance is not recommended for them. However, aging is a risk factor for HCC; threfore, the necessity for HCC surveillance in older patients with non-advanced fibrosis needs to be verified. Methods This multicenter, prospective study enrolled 4993 patients with SVR (1998 patients with advanced fibrosis and 2995 patients with non-advanced fibrosis). The HCC incidence was examined with particular attention to age. Results The 3-year incidence of HCC in patients with advanced and non-advanced fibrosis was 9.2% (95% CI: 7.8-10.9) and 2.9% (95% CI: 2.1-3.7), respectively. HCC incidence was significantly higher in patients with advanced fibrosis (P < 0.001). HCC incidence stratified by age and sex was investigated in patients with non-advanced fibrosis. The HCC incidence in the 18-49, 50s, 60s, 70s, and ≥80 age groups were 0.26, 1.3, 1.8, 1.7, and 2.9 per 100 person-years in men, and 0.00, 0.32, 0.58, 0.49, and 0.57 per 100 person-years in women, respectively. Conclusions Male patients with non-advanced fibrosis aged ≥60 years have a higher risk of developing HCC and, thus, require HCC surveillance.
Collapse
Affiliation(s)
- Shun Ishido
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| | - Nobuharu Tamaki
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| | - Masayuki Kurosaki
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| | - Nami Mori
- Department of GastroenterologyHiroshima Red Cross Hospital and Atomic Bomb Survivors' HospitalHiroshimaJapan
| | - Keiji Tsuji
- Department of GastroenterologyHiroshima Red Cross Hospital and Atomic Bomb Survivors' HospitalHiroshimaJapan
| | - Chitomi Hasebe
- Department of GastroenterologyAsahikawa Red Cross HospitalAsahikawaJapan
| | - Toshie Mashiba
- Center for Liver‐Biliary‐Pancreatic DiseaseMatsuyama Red Cross HospitalMatsuyamaJapan
| | - Hironori Ochi
- Center for Liver‐Biliary‐Pancreatic DiseaseMatsuyama Red Cross HospitalMatsuyamaJapan
| | - Yutaka Yasui
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| | - Takehiro Akahane
- Department of GastroenterologyIshinomaki Red Cross HospitalIshinomakiJapan
| | - Koichiro Furuta
- Department of GastroenterologyMasuda Red Cross HospitalMasudaJapan
| | - Haruhiko Kobashi
- Department of GastroenterologyJapanese Red Cross Okayama HospitalOkayamaJapan
| | - Hideki Fujii
- Department of GastroenterologyJapanese Red Cross Kyoto Daiichi HospitalKyotoJapan
| | - Toru Ishii
- Department of GastroenterologyJapanese Red Cross Akita HospitalAkitaJapan
| | - Hiroyuki Marusawa
- Department of Gastroenterology and HepatologyOsaka Red Cross HospitalOsakaJapan
| | - Masahiko Kondo
- Department of GastroenterologyOtsu Red Cross HospitalOtsuJapan
| | - Atsunori Kusakabe
- Department of GastroenterologyJapanese Red Cross Aichi Medical Center Nagoya Daini HospitalNagoyaJapan
| | - Hideo Yoshida
- Department of GastroenterologyJapanese Red Cross Medical CenterTokyoJapan
| | - Yasushi Uchida
- Department of GastroenterologyMatsue Red Cross HospitalMatsueJapan
| | - Toshifumi Tada
- Department of Internal MedicineHimeji Red Cross HospitalHimejiJapan
| | | | - Akari Mitsuda
- Department of GastroenterologyTottori Red Cross HospitalTottoriJapan
| | - Chikara Ogawa
- Department of GastroenterologyTakamatsu Red Cross HospitalTakamatsuJapan
| | - Hirotaka Arai
- Department of GastroenterologyMaebashi Red Cross HospitalMaebashiJapan
| | - Toshimitsu Murohisa
- Department of GastroenterologyJapanese Red Cross Ashikaga HospitalAshikagaJapan
| | - Minoru Uebayashi
- Department of GastroenterologyKitami Red Cross HospitalKitamiJapan
| | - Namiki Izumi
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| |
Collapse
|
15
|
Khalifa A, Obeid JS, Erno J, Rockey DC. The role of artificial intelligence in hepatology research and practice. Curr Opin Gastroenterol 2023; 39:175-180. [PMID: 37144534 DOI: 10.1097/mog.0000000000000926] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
PURPOSE OF REVIEW The use of artificial intelligence (AI) in examining large data sets has recently gained considerable attention to evaluate disease epidemiology, management approaches, and disease outcomes. The purpose of this review is to summarize the current role of AI in contemporary hepatology practice. RECENT FINDINGS AI was found to be diagnostically valuable in the evaluation of liver fibrosis, detection of cirrhosis, differentiation between compensated and decompensated cirrhosis, evaluation of portal hypertension, detection and differentiation of particular liver masses, preoperative evaluation of hepatocellular carcinoma as well as response to treatment and estimation of graft survival in patients undergoing liver transplantation. AI additionally holds great promise in examination of structured electronic health records data as well as in examination of clinical text (using various natural language processing approaches). Despite its contributions, AI has several limitations, including the quality of existing data, small cohorts with possible sampling bias and the lack of well validated easily reproducible models. SUMMARY AI and deep learning models have extensive applicability in assessing liver disease. However, multicenter randomized controlled trials are indispensable to validate their utility.
Collapse
Affiliation(s)
- Ali Khalifa
- Medical University of South Carolina Digestive Disease Research Center
| | - Jihad S Obeid
- Department of Biomedical Informatics, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jason Erno
- Medical University of South Carolina Digestive Disease Research Center
| | - Don C Rockey
- Medical University of South Carolina Digestive Disease Research Center
| |
Collapse
|
16
|
Ouyang Y, Cheng M, He B, Zhang F, Ouyang W, Zhao J, Qu Y. Interpretable machine learning models for predicting in-hospital death in patients in the intensive care unit with cerebral infarction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107431. [PMID: 36827826 DOI: 10.1016/j.cmpb.2023.107431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 07/20/2022] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Research on patients with cerebral infarction in the Intensive Care Unit (ICU) is still lacking. Our study aims to develop and validate multiple machine-learning (ML) models using two large ICU databases-Medical Information Mart for Intensive Care version III (MIMIC-III) and eICU Research Institute Database (eRI)-to guide clinical practice. METHODS We collected clinical data from patients with cerebral infarction in the MIMIC-III and eRI databases within 24 h of admission. The opinion of neurologists and the Least Absolute Shrinkage and Selection Operator regression was used to screen for relevant clinical features. Using eRI as the training set and MIMIC-III as the test set, we developed and validated six ML models. Based on the results of the model validation, we select the best model and perform the interpretability analysis on it. RESULTS A total of 4,338 patients were included in the study (eRI:3002, MIMIC-III:1336), resulting in a total of 18 clinical characteristics through screening. Model validation results showed that random forest (RF) was the best model, with AUC and F1 scores of 0.799 and 0.417 in internal validation and 0.733 and 0.498 in external validation, respectively; moreover, its sensitivity and recall were the highest of the six algorithms for both the internal and external validation. The explanatory analysis of the model showed that the three most important variables in the RF model were Acute Physiology Score-III, Glasgow Coma Scale score, and heart rate, and that the influence of each variable on the judgement of the model was consistent with medical knowledge. CONCLUSION Based on a large sample of patients and advanced algorithms, our study bridges the limitations of studies on this area. With our model, physicians can use the admission information of cerebral infarction patients in the ICU to identify high-risk groups among them who are prone to in-hospital death, so that they could be more alert to this group of patients and upgrade medical measures early to minimize the mortality of patients.
Collapse
Affiliation(s)
- Yang Ouyang
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Meng Cheng
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Bingqing He
- Department of Neurology, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Fengjuan Zhang
- Department of Neurology, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China
| | - Wen Ouyang
- Department of Endocrinology, First People's Hospital of Changde City, 818 renmin Street, Changde 415000, China
| | - Jianwu Zhao
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China.
| | - Yang Qu
- Department of Orthopedics, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun 130041, China.
| |
Collapse
|
17
|
Nahon P, Bamba-Funck J, Layese R, Trépo E, Zucman-Rossi J, Cagnot C, Ganne-Carrié N, Chaffaut C, Guyot E, Ziol M, Sutton A, Audureau E. Integrating genetic variants into clinical models for hepatocellular carcinoma risk stratification in cirrhosis. J Hepatol 2023; 78:584-595. [PMID: 36427656 DOI: 10.1016/j.jhep.2022.11.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 10/06/2022] [Accepted: 11/01/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND & AIMS Identifying individuals at higher risk of developing hepatocellular carcinoma (HCC) is pivotal to improve the performance of surveillance strategies. Herein, we aimed to evaluate the ability of single nucleotide polymorphisms (SNPs) to refine HCC risk stratification. METHODS Six SNPs in PNPLA3, TM6SF2, HSD17B13, APOE, and MBOAT7 affecting lipid turnover and one variant involved in the Wnt-β-catenin pathway (WNT3A-WNT9A rs708113) were assessed in patients with alcohol-related and/or HCV-cured cirrhosis included in HCC surveillance programmes (prospective CirVir and CIRRAL cohorts). Their prognostic value for HCC occurrence was assessed using Fine-Gray models combined into a 7-SNP genetic risk score (GRS). The predictive ability of two clinical scores (a routine non-genetic model determined by multivariate analysis and the external aMAP score) with/without the GRS was evaluated by C-indices. The standardised net benefit was derived from decision curves. RESULTS Among 1,145 patients, 86 (7.5%) developed HCC after 43.7 months. PNPLA3 and WNT3A-WNT9A variants were independently associated with HCC occurrence. The GRS stratified the population into three groups with progressively increased 5-year HCC incidence (Group 1 [n = 627, 5.4%], Group 2 [n = 276, 10.7%], and Group 3 [n = 242, 15.3%]; p <0.001). The multivariate model identified age, male sex, diabetes, platelet count, gamma-glutamyltransferase levels, albuminemia and the GRS as independent risk factors. The clinical model performance for 5-year HCC prediction was similar to that of the aMAP score (C-Index 0.769). The addition of the GRS to both scores modestly improved their performance (C-Indices of 0.786 and 0.783, respectively). This finding was confirmed by decision curve analyses showing only fair clinical net benefit. CONCLUSIONS Patients with cirrhosis can be stratified into HCC risk classes by variants affecting lipid turnover and the Wnt-β-catenin pathway. The incorporation of this genetic information modestly improves the performance of clinical scores. IMPACT AND IMPLICATIONS The identification of patients at higher risk of developing liver cancer is pivotal to improve the performance of surveillance. Risk assessment can be achieved by combining several clinical and biological parameters used in routine practice. The addition of patients' genetic characteristics can modestly improve this prediction and will ultimately pave the way for precision medicine in patients eligible for HCC surveillance, allowing physicians to trigger personalised screening strategies.
Collapse
Affiliation(s)
- Pierre Nahon
- APHP, Liver Unit, Bobigny, Université Sorbonne Paris Nord, F-93000 Bobigny, France; Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de Recherche des Cordeliers, Université de Paris, Paris, France.
| | - Jessica Bamba-Funck
- APHP, Biochemistry Unit, Bobigny, Université Sorbonne Paris Nord, and Inserm, UMR-1148 "Laboratory for Vascular Translational Science" Université Sorbonne Paris Nord, F-93000 Bobigny, France
| | - Richard Layese
- Univ Paris Est Créteil, INSERM, IMRB, Equipe CEpiA (Clinical Epidemiology and Ageing), Unité de Recherche Clinique (URC Mondor), Service de Santé Publique, Assistance Publique Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, F-94000, Créteil, France
| | - Eric Trépo
- Department of Gastroenterology, Hepatopancreatology and Digestive Oncology, CUB Hopital Erasme, and Laboratory of Experimental Gastroenterology, Université Libre de Bruxelles, Brussels, Belgium
| | - Jessica Zucman-Rossi
- Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de Recherche des Cordeliers, Université de Paris, Paris, France
| | - Carole Cagnot
- Clinical Research Department, ANRS|Emerging Infectious Diseases, Paris, France
| | | | - Cendrine Chaffaut
- SBIM, APHP, Hôpital Saint-Louis, Paris, Inserm, UMR-1153, ECSTRA Team, Paris, France
| | - Erwan Guyot
- APHP, Biochemistry Unit, Bobigny, Université Sorbonne Paris Nord, and Inserm, UMR-1148 "Laboratory for Vascular Translational Science" Université Sorbonne Paris Nord, F-93000 Bobigny, France
| | - Marianne Ziol
- APHP, Pathology Unit, Bobigny, Université Sorbonne Paris Nord, F-93000 Bobigny, France; Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de Recherche des Cordeliers, Université de Paris, Paris, France
| | - Angela Sutton
- APHP, Biochemistry Unit, Bobigny, Université Sorbonne Paris Nord, and Inserm, UMR-1148 "Laboratory for Vascular Translational Science" Université Sorbonne Paris Nord, F-93000 Bobigny, France
| | - Etienne Audureau
- Univ Paris Est Créteil, INSERM, IMRB, Equipe CEpiA (Clinical Epidemiology and Ageing), Unité de Recherche Clinique (URC Mondor), Service de Santé Publique, Assistance Publique Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, F-94000, Créteil, France
| |
Collapse
|
18
|
Fu Y, Li X, Yang Z, Li S, Pan Y, Chen J, Wang J, Hu D, Zhou Z, Xu L, Chen M, Zhang Y. A risk-based postresection follow-up strategy for hepatocellular carcinoma patients. Cancer 2023; 129:569-579. [PMID: 36541017 DOI: 10.1002/cncr.34601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/17/2022] [Accepted: 09/22/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND The optimal intervals for follow-up after hepatocellular carcinoma (HCC) patients undergo curative liver resection (LR) remain unclear. This study aimed to establish a risk-based post-resection follow-up strategy. METHODS Patients that were diagnosed with HCC and received LR from three hospitals in China were included. The risk-based strategy was established based on the random survival forest model and compared with a fixed strategy both internally and externally. RESULTS In total, 3447 patients from three hospitals were included. The authors' strategy showed superiority in the early detection of tumor relapse compared with fixed surveillance. Under fewer total visits, risk-based strategy achieved analogous survival time compared to the total 20 times follow-ups based on fixed strategy. Twelve total visits (five, three, one, two, and one visits in years 1-5, respectively) for American Joint Committee on Cancer/International Union Against Cancer T1a stage patients, 13 total visits (five, four, one, two, and one visits in years 1-5, respectively) for T1b stage patients, 15 total visits (eight, three, three, zero, and one visits in years 1-5, respectively) for T2 stage patients, and 15 total visits (eight, four, one, one, and one visits in years 1-5, respectively) for T3 stage patients were advocated. The detailed follow-up arrangements were available to the public through an interactive website (https://sysuccfyz.shinyapps.io/RiskBasedFollowUp/). CONCLUSION This risk-based surveillance strategy was demonstrated to detect relapse earlier and reduce the total number of follow-ups without compromising on survival. Based on the strategy and methodology of the authors, surgeons or patients could choose more intensive or flexible schedules depending on the requirements and economic conditions. PLAIN LANGUAGE SUMMARY A risk-based post-resection follow-up strategy was established by random survival forest model using a larger hepatocellular carcinoma population The strategy was demonstrated to detect tumor relapse earlier and reduce the total number of follow-ups without compromising on survival Our strategy and methodology could be widely applied by other surgeons and patients.
Collapse
Affiliation(s)
- Yizhen Fu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China.,Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Xia Li
- Department of Hepatobiliary Surgery, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, People's Republic of China
| | - Zhenyun Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China.,Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Shaoqiang Li
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Yangxun Pan
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China.,Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Jinbin Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China.,Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Juncheng Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China.,Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Dandan Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China.,Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Zhongguo Zhou
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Li Xu
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Minshan Chen
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| | - Yaojun Zhang
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
| |
Collapse
|
19
|
Kondili LA, Quaranta MG, Cavalletto L, Calvaruso V, Ferrigno L, D'Ambrosio R, Simonelli I, Brancaccio G, Raimondo G, Brunetto MR, Zignego AL, Coppola C, Iannone A, Biliotti E, Verucchi G, Massari M, Licata A, Barbaro F, Persico M, Russo FP, Morisco F, Pompili M, Viganò M, Puoti M, Santantonio T, Villa E, Craxì A, Chemello L. Profiling the risk of hepatocellular carcinoma after long-term HCV eradication in patients with liver cirrhosis in the PITER cohort. Dig Liver Dis 2023:S1590-8658(23)00164-0. [PMID: 36775720 DOI: 10.1016/j.dld.2023.01.153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/15/2022] [Accepted: 01/16/2023] [Indexed: 02/14/2023]
Abstract
BACKGROUND AND AIMS Severe liver disease markers assessed before HCV eradication are acknowledged to usually improve after the SVR. We prospectively evaluated, in the PITER cohort, the long-term HCC risk profile based on predictors monitored after HCV eradication by direct-acting antivirals in patients with cirrhosis. METHODS HCC occurrence was evaluated by Kaplan-Meier analysis. Cox regression analysis identified the post-treatment variables associated with de-novo HCC; their predictive power was presented in a nomogram. RESULTS After the end of therapy (median follow-up:28.47 months), among 2064 SVR patients, 119 (5.8%) developed de-novo HCC. The HCC incidence was 1.90%, 4.21%, 6.47% at 12-, 24- and 36-months from end-of-therapy, respectively (incidence rate 2.45/100 person-years). Age, genotype 3, diabetes, platelets (PLT)≤120,000/µl and albumin ≤3.5g/dl levels were identified as pre-treatment HCC independent predictors. Adjusting for age, the post-treatment PLT≤120,000/µl (AdjHR 1.92; 95%CI:1.06-3.45) and albumin≤3.5g/dl (AdjHR 4.38; 95%CI 2.48-7.75) values were independently associated with HCC occurrence. Two different risk profiles were identified by combining long-term post-therapy evaluation of PLT ≤ vs. >120,000/µl and albumin ≤ vs. >3.5g/dl showing a significant different HCC incidence rate of 1.35 vs. 3.77/100 p-y, respectively. CONCLUSIONS The nomogram score based on age, PLT and albumin levels after SVR showed an accurate prediction capability and may support the customizing management for early HCC detection.
Collapse
Affiliation(s)
- Loreta A Kondili
- Center for Global Health, Istituto Superiore Di Sanità (ISS), Rome, Italy; UniCamillus-Saint Camillus International University of Health Sciences, Rome, Italy.
| | | | - Luisa Cavalletto
- Department of Medicine-DIMED, Padua University, University Hospital, Clinica Medica 5, Refering Regional Center for Liver Diseases, Padova, Italy
| | - Vincenza Calvaruso
- Gastroenterology and Hepatology Unit, PROMISE, University of Palermo, Palermo, Italy
| | - Luigina Ferrigno
- Center for Global Health, Istituto Superiore Di Sanità (ISS), Rome, Italy
| | - Roberta D'Ambrosio
- Division of Gastroenterology and Hepatology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Ilaria Simonelli
- L'altrastatistica srl, Consultancy & Training, Biostatistics Office, Rome, Italy
| | - Giuseppina Brancaccio
- Department of Molecular Medicine, Infectious Diseases Unit, University of Padua, Padua, Italy
| | - Giovanni Raimondo
- Department of Internal Medicine, University Hospital of Messina, Messina, Italy
| | - Maurizia R Brunetto
- Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Anna Linda Zignego
- Department of Experimental and Clinical Medicine, Interdepartmental Centre MASVE, University of Florence, Florence, Italy
| | - Carmine Coppola
- Department of Hepatology, Gragnano Hospital, Gragnano, NA, Italy
| | - Andrea Iannone
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Elisa Biliotti
- Infectious and Tropical Medicine Unit, Department of Public Health and Infectious Diseases, "Policlinico Umberto I" Hospital, Sapienza University of Rome, Rome, Italy
| | - Gabriella Verucchi
- Clinic of Infectious Diseases and Microbiology Unit, Alma Mater Studiorum, Bologna University, Bologna, Italy
| | - Marco Massari
- Malattie Infettive, Azienda Unità Sanitaria Locale, IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Anna Licata
- Infectious Diseases Unit, DIBIMIS, University of Palermo, Palermo, Italy
| | - Francesco Barbaro
- Department of Medicine, Infectious Diseases Unit, University of Padua, Padua, Italy
| | - Marcello Persico
- Internal Medicine and Hepatology Division, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Baronissi, Italy
| | - Francesco Paolo Russo
- Department of Surgery, Oncology and Gastroenterology, Gastroenterology Unit, University of Padua, Padua, Italy
| | | | - Maurizio Pompili
- Internal Medicine and Gastroenterology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Agostino Gemelli, Rome, Italy
| | - Mauro Viganò
- Hepatology Unit, San Giuseppe Hospital, Milan, Italy
| | - Massimo Puoti
- Infectious Disease Unit, Niguarda Hospital, Milan, Italy; Università degli Studi di Milano-Bicocca, School of Medicine, Milan, Italy
| | - Teresa Santantonio
- Infectious Diseases Unit, Department of Clinical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Erica Villa
- Gastroenterology Unit, Department of Medical Specialties, University of Modena & Reggio Emilia and Modena University-Hospital, Modena, Italy
| | - Antonio Craxì
- Gastroenterology and Hepatology Unit, PROMISE, University of Palermo, Palermo, Italy
| | - Liliana Chemello
- Department of Medicine-DIMED, Padua University, University Hospital, Clinica Medica 5, Refering Regional Center for Liver Diseases, Padova, Italy.
| |
Collapse
|
20
|
Liu YB, Chen MK. Epidemiology of liver cirrhosis and associated complications: Current knowledge and future directions. World J Gastroenterol 2022; 28:5910-5930. [PMID: 36405106 PMCID: PMC9669831 DOI: 10.3748/wjg.v28.i41.5910] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 09/30/2022] [Accepted: 10/20/2022] [Indexed: 02/06/2023] Open
Abstract
Cirrhosis causes a heavy global burden. In this review, we summarized up-to-date epidemiological features of cirrhosis and its complications. Recent epidemiological studies reported an increase in the prevalence of cirrhosis in 2017 compared to in 1990 in both men and women, with 5.2 million cases of cirrhosis and chronic liver disease occurring in 2017. Cirrhosis caused 1.48 million deaths in 2019, an increase of 8.1% compared to 2017. Disability-adjusted life-years due to cirrhosis ranked 16th among all diseases and 7th in people aged 50-74 years in 2019. The global burden of hepatitis B virus and hepatitis C virus-associated cirrhosis is decreasing, while the burden of cirrhosis due to alcohol and nonalcoholic fatty liver disease (NAFLD) is increasing rapidly. We described the current epidemiology of the major complications of cirrhosis, including ascites, variceal bleeding, hepatic encephalopathy, renal disorders, and infections. We also summarized the epidemiology of hepatocellular carcinoma in patients with cirrhosis. In the future, NAFLD-related cirrhosis will likely become more common due to the prevalence of metabolic diseases such as obesity and diabetes, and the prevalence of alcohol-induced cirrhosis is increasing. This altered epidemiology should be clinically noted, and relevant interventions should be undertaken.
Collapse
Affiliation(s)
- Yuan-Bin Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei Province, China
| | - Ming-Kai Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430000, Hubei Province, China
| |
Collapse
|
21
|
Is There a Place for Somatostatin Analogues for the Systemic Treatment of Hepatocellular Carcinoma in the Immunotherapy Era? LIVERS 2022. [DOI: 10.3390/livers2040024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Patients with advanced hepatocellular carcinoma (HCC) have a very limited survival rate even after the recent inclusion of kinase inhibitors or immune checkpoint inhibitors in the therapeutic armamentarium. A significant problem with the current proposed therapies is the considerable cost of treatment that may be a serious obstacle in low- and middle-income countries. Implementation of somatostatin analogues (SSAs) has the potential to overcome this obstacle, but due to some negative studies their extensive evaluation came to a halt. However, experimental evidence, both in vitro and in vivo, has revealed various mechanisms of the anti-tumor effects of these analogues, including inhibition of cancer cell proliferation and angiogenesis and induction of apoptosis. Favorable indirect effects such as inhibition of liver inflammation and fibrosis and influence on macrophage-mediated innate immunity have also been noted and are presented in this review. Furthermore, the clinical application of SSAs is both presented and compared with clinical trials of kinase and immune checkpoint inhibitors (ICIs). No direct trials have been performed to compare survival in the same cohort of patients, but the cost of treatment with SSAs is a fraction compared to the other modalities and with significantly less serious side effects. As in immunotherapy, patients with viral HCC (excluding alcoholics), as well as Barcelona stage B or C and Child A patients, are the best candidates, since they usually have a survival prospect of at least 6 months, necessary for optimum results. Reasons for treatment failures are also discussed and further research is proposed.
Collapse
|
22
|
Evaluation of the aMAP score for hepatocellular carcinoma surveillance: a realistic opportunity to risk stratify. Br J Cancer 2022; 127:1263-1269. [PMID: 35798825 PMCID: PMC9519948 DOI: 10.1038/s41416-022-01851-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/13/2022] [Accepted: 05/09/2022] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND AND AIMS The aMAP score is a model that predicts risk of hepatocellular carcinoma (HCC) development in patients with chronic hepatitis. Its performance in a 'real world' surveillance setting has not yet been ascertained. PATIENTS AND METHODS We had access to a cohort of 3473 individuals enrolled in a rigorously implemented and prospectively accrued surveillance programme (patients undergoing regular ultrasound and biomarker examination between 1998 and 2021). During this period 445 had HCC detected. Of these, 77.8% had early stage disease (within Milan criteria), permitting potentially curative therapy to be implemented in nearly 70% of cases. We applied the recently developed aMAP score to classify patients according to their initial aMAP score in to low, medium and high-risk groups as proposed in the original publication. The performance of the aMAP score was assessed according to the concordance-index and calibration (i.e. agreement between observed and predicted risk). Allowance was made for competing causes of death. RESULTS The aMAP score achieved an overall C-index of 0.81 (95% CI: 0.79-0.82) consistent with the initial report and was unaffected by allowance for competing causes of death. Sub-group analysis showed that the results did not change significantly according to gender, or aetiology. However, aMAP discrimination was greater for younger individuals (versus older individuals), and also for individuals without cirrhosis. The HCC incidence rate was 0.98, 7.05 and 29.1 events per 1000 person-years in the low-, moderate- and high-risk aMAP groups, respectively. CONCLUSIONS The results from this 'real-world' cohort demonstrate that risk stratification is a realistic prospect and that identification of a subgroup of chronic liver disease patients who have a very low risk of HCC is feasible.
Collapse
|
23
|
Lockart I, Yeo MGH, Hajarizadeh B, Dore G, Danta M, Abe K, Carrat F, Lusivika‐Nzinga C, Degasperi E, Di Marco V, Hou J, Howell J, Janjua NZ, Wong S, Kumada T, Lleo A, Persico M, Lok AS, Wei L, Yang M, Nabatchikova E, Nguyen MH, Antonio Pineda J, Reig M, Shiha G, Yu M, Tsai P. HCC incidence after hepatitis C cure among patients with advanced fibrosis or cirrhosis: A meta-analysis. Hepatology 2022; 76:139-154. [PMID: 35030279 PMCID: PMC9303770 DOI: 10.1002/hep.32341] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/28/2021] [Accepted: 01/04/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND AIMS HCV cure reduces but does not eliminate the risk of HCC. HCC surveillance is recommended in populations where the incidence exceeds 1.5% per year. In cirrhosis, HCC surveillance should continue after HCV cure, although it is uncertain if this should be indefinite. For patients with advanced fibrosis (F3), guidelines are inconsistent in their recommendations. We evaluated the incidence of HCC after HCV cure among patients with F3 fibrosis or cirrhosis. APPROACH AND RESULTS This systematic review and meta-analysis identified 44 studies (107,548 person-years of follow-up) assessing the incidence of HCC after HCV cure among patients with F3 fibrosis or cirrhosis. The incidence of HCC was 2.1 per 100 person-years (95% CI, 1.9-2.4) among patients with cirrhosis and 0.5 per 100 person-years (95% CI, 0.3-0.7) among patients with F3 fibrosis. In a meta-regression analysis among patients with cirrhosis, older age (adjusted rate ratio [aRR] per 10-year increase in mean/median age, 1.32; 95% CI, 1.00-1.73) and prior decompensation (aRR per 10% increase in the proportion of patients with prior decompensation, 1.06; 95% CI, 1.01-1.12) were associated with an increased incidence of HCC. Longer follow-up after HCV cure was associated with a decreased incidence of HCC (aRR per year increase in mean/median follow-up, 0.87; 95% CI, 0.79-0.96). CONCLUSIONS Among patients with cirrhosis, the incidence of HCC decreases over time after HCV cure and is lowest in patients with younger age and compensated cirrhosis. The substantially lower incidence in F3 fibrosis is below the recommended threshold for cost-effective screening. The results should encourage the development of validated predictive models that better identify at-risk individuals, especially among patients with F3 fibrosis.
Collapse
Affiliation(s)
- Ian Lockart
- Faculty of MedicineSt. Vincent's Clinical SchoolUniversity of New South WalesSydneyNew South WalesAustralia,St. Vincent’s HospitalSydneyNew South WalesAustralia
| | - Malcolm G. H. Yeo
- Faculty of MedicineSt. Vincent's Clinical SchoolUniversity of New South WalesSydneyNew South WalesAustralia
| | - Behzad Hajarizadeh
- The Kirby InstituteUniversity of New South WalesSydneyNew South WalesAustralia
| | - Gregory J. Dore
- St. Vincent’s HospitalSydneyNew South WalesAustralia,The Kirby InstituteUniversity of New South WalesSydneyNew South WalesAustralia
| | - Mark Danta
- Faculty of MedicineSt. Vincent's Clinical SchoolUniversity of New South WalesSydneyNew South WalesAustralia,St. Vincent’s HospitalSydneyNew South WalesAustralia
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
24
|
Ge J, Kim WR, Lai JC, Kwong AJ. "Beyond MELD" - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation. J Hepatol 2022; 76:1318-1329. [PMID: 35589253 DOI: 10.1016/j.jhep.2022.03.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/24/2022] [Accepted: 03/04/2022] [Indexed: 02/06/2023]
Abstract
In this review article, we discuss the model for end-stage liver disease (MELD) score and its dual purpose in general and transplant hepatology. As the landscape of liver disease and transplantation has evolved considerably since the advent of the MELD score, we summarise emerging concepts, methodologies, and technologies that may improve mortality prognostication in the future. Finally, we explore how these novel concepts and technologies may be incorporated into clinical practice.
Collapse
Affiliation(s)
- Jin Ge
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA
| | - W Ray Kim
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Jennifer C Lai
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA
| | - Allison J Kwong
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| |
Collapse
|
25
|
Poynard T, Lacombe JM, Deckmyn O, Peta V, Akhavan S, Zoulim F, de Ledinghen V, Samuel D, Mathurin P, Ratziu V, Thabut D, Housset C, Fontaine H, Pol S, Carrat F. External Validation of LCR1-LCR2, a Multivariable Hepatocellular Carcinoma Risk Calculator, in a Multiethnic Cohort of Patients With Chronic Hepatitis B. GASTRO HEP ADVANCES 2022; 1:604-617. [PMID: 39132068 PMCID: PMC11308549 DOI: 10.1016/j.gastha.2022.02.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 02/09/2022] [Indexed: 08/13/2024]
Abstract
Background and Aims The liver cancer risk test (LCR1-LCR2) is a multianalyte blood test combining proteins involved in liver cell repair (apolipoprotein A1, haptoglobin), hepatocellular carcinoma (HCC) risk factors (gender, age, gamma glutamyl transpeptidase), a marker of fibrosis (alpha2-macroglobulin), and alpha-fetoprotein, a specific marker of HCC. The aim was to externally validate LCR1-LCR2 in hepatitis B. Methods Preincluded patients were from the Hepather cohort, a multicenter, multiethnic prospective study in 6071 patients. The coprimary study outcome was the negative predictive value of LCR1-LCR2 at 5 years for the occurrence of HCC and survival without HCC according to the predetermined LCR1-LCR2 cutoffs, adjusted for risk covariables and for chronic hepatitis B treatment and quantified using time-dependent Cox proportional hazards models. A post hoc analysis compared the number of patients needed to screen one cancer by LCR1-LCR2 and PAGE-B. Results A total of 3520 patients, 191 (5.4%) with cirrhosis, with at least 1 year of follow-up were included. A total of 76 HCCs occurred over a median (interquartile range) of 6.0 years (4.8-7.3) of follow-up. Among the 3367 patients with low-risk LCR1-LCR2, the 5-year negative predictive value was 99.3% (95% confidence interval = 99.0-99.6), with a significant Cox hazard ratio (6.4, 3.1-13.0; P < .001) obtained after adjustment for exposure to antivirals, age, gender, geographical origin, HBe-Ag status, alcohol consumption, and type-2 diabetes. LCR1-LCR2 outperformed PAGE-B for number of patients needed to screen mean (95% CI), 8.5 (3.2-8.1) vs 26.3 (17.5-38.5; P < .0001), respectively. Conclusion The performance of LCR1-LCR2 to identify patients with chronic hepatitis B at very low risk of HCC at 5 years was externally validated. ClinicalTrials.gov: NCT01953458.
Collapse
Affiliation(s)
- Thierry Poynard
- Department of Hepato-Gastroenterology, Assistance Publique-Hôpitaux de Paris (AP-HP), Pitié-Salpêtrière Hospital, Paris, France
- INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne Université, Paris, France
| | - Jean Marc Lacombe
- INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Sorbonne Université, Paris, France
| | | | - Valentina Peta
- INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne Université, Paris, France
- Research Unit, BioPredictive, Paris, France
| | - Sepideh Akhavan
- Department of Hepato-Gastroenterology, Assistance Publique-Hôpitaux de Paris (AP-HP), Pitié-Salpêtrière Hospital, Paris, France
| | - Fabien Zoulim
- Hepatology Unit Hôpital Haut-Lévêque, Pessac, and INSERM U1053, Université Bordeaux Segalen, Bordeaux, France
| | - Victor de Ledinghen
- Department of Hepatology, Hospices civils de Lyon, Hôpital Croix Rousse, INSERM U1052, Université de Lyon, Lyon, France
| | - Didier Samuel
- Hepatology Department, AP-HP, Hospital Paul Brousse, UMR-S1193, Villejuif, Université Paris-Saclay, and Hepatinov, Villejuif, France
| | | | - Vlad Ratziu
- Department of Hepato-Gastroenterology, Assistance Publique-Hôpitaux de Paris (AP-HP), Pitié-Salpêtrière Hospital, Paris, France
- INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne Université, Paris, France
| | - Dominique Thabut
- Department of Hepato-Gastroenterology, Assistance Publique-Hôpitaux de Paris (AP-HP), Pitié-Salpêtrière Hospital, Paris, France
- INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne Université, Paris, France
| | - Chantal Housset
- INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne Université, Paris, France
| | - Hélène Fontaine
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, AP-HP, Hôpital Cochin, Hepatology Department, Paris, France
| | - Stanislas Pol
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, AP-HP, Hôpital Cochin, Hepatology Department, Paris, France
| | - Fabrice Carrat
- INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Sorbonne Université, Paris, France
| |
Collapse
|
26
|
Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| |
Collapse
|
27
|
Sanduzzi-Zamparelli M, Mariño Z, Lens S, Sapena V, Iserte G, Pla A, Granel N, Bartres C, Llarch N, Vilana R, Nuñez I, Darnell A, Belmonte E, García-Criado A, Díaz A, Muñoz-Martinez S, Ayuso C, Bianchi L, Fuster-Anglada C, Rimola J, Forner A, Torres F, Bruix J, Forns X, Reig M. Liver cancer risk after HCV cure in patients with advanced liver disease without non-characterized nodules. J Hepatol 2022; 76:874-882. [PMID: 34856322 DOI: 10.1016/j.jhep.2021.11.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 11/14/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND & AIMS Recognition of non-characterized liver nodules (NCLN) prior to direct-acting antivirals (DAAs) is associated with increased hepatocellular carcinoma (HCC) risk in patients with HCV. The risk of HCC has not been defined in F3/F4 patients in whom NCLN have been ruled-out before starting DAAs and at sustained virological response (SVR). This study aimed to estimate HCC incidence in this population. METHODS We performed a prospective study including HCV-infected patients with F3/F4 fibrosis, without a history of HCC, and who achieved SVR after DAAs. Patients were only included if they had undergone ultrasound imaging that excluded the presence of HCC/NCLN within 30 days after SVR. All patients were evaluated every 6 months until developing primary liver cancer, death or withdrawal of informed consent. HCC incidence was expressed per 100 patient-years (/100PY). Adherence to screening program was calculated every 6 months for the first 48 months. RESULTS A total of 185 patients (63/122, F3/F4) were included. Among those with cirrhosis, 92% were Child-Pugh A and 42.7% had clinically significant portal hypertension (CSPH). Albumin-bilirubin score was 1 in 84.9% and 2 in 15.1% of patients, respectively. The median clinical and radiologic follow-up was 52.4 months and 48 months, respectively. Ten patients developed HCC: HCC incidence was 1.46/100PY (95% CI 0.79-2.71) in the whole cohort, 2.24/100PY (95% CI 1.21-4.17) in F4 only and 3.63/100PY (95% CI 1.95-6.74) in patients with CSPH. No HCC was registered in patients with F3. Median time between SVR and HCC occurrence was 28.1 months; 12 non-primary liver cancers were also identified. CONCLUSIONS Patients with cirrhosis without NCLN at SVR remain at risk of HCC development. The absence of HCC in patients with F3 reinforces their marginal cancer risk, but prospective studies are needed to exclude them from screening programs. LAY SUMMARY Patients with HCV-related cirrhosis, without non-characterized liver nodules at sustained virologic response, remain at risk of hepatocellular carcinoma despite viral cure. However, the cancer risk after successful direct-acting antiviral treatment is marginal in patients with F3 fibrosis without non-characterized liver nodules. If confirmed in larger prospective studies, current screening recommendations may need to be revisited in this group of patients.
Collapse
Affiliation(s)
- Marco Sanduzzi-Zamparelli
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Zoe Mariño
- Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain
| | - Sabela Lens
- Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain
| | - Victor Sapena
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Gemma Iserte
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Anna Pla
- Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain
| | - Núria Granel
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Concepció Bartres
- Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain
| | - Neus Llarch
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Ramón Vilana
- BCLC Group. Radiology Department Hospital Clinic of Barcelona, University of Barcelona, Spain
| | - Isabel Nuñez
- Radiology Department, Hospital Clinic of Barcelona, Spain
| | - Anna Darnell
- BCLC Group. Radiology Department Hospital Clinic of Barcelona, University of Barcelona, Spain
| | - Ernest Belmonte
- BCLC Group. Radiology Department Hospital Clinic of Barcelona, University of Barcelona, Spain
| | - Angeles García-Criado
- BCLC Group. Radiology Department Hospital Clinic of Barcelona, University of Barcelona, Spain
| | - Alba Díaz
- BCLC Group. Department of Pathology, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain
| | - Sergio Muñoz-Martinez
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Carmen Ayuso
- BCLC Group. Radiology Department Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain
| | - Luis Bianchi
- BCLC Group. Radiology Department Hospital Clinic of Barcelona, University of Barcelona, Spain
| | - Carla Fuster-Anglada
- BCLC Group. Department of Pathology, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain
| | - Jordi Rimola
- BCLC Group. Radiology Department Hospital Clinic of Barcelona, CIBERehd, University of Barcelona, Spain
| | - Alejandro Forner
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Ferran Torres
- Medical Statistics Core Facility, IDIBAPS, Hospital Clinic of Barcelona, Spain; Biostatistics Unit, Faculty of Medicine, Universitat Autònoma of Barcelona, Barcelona, Spain
| | - Jordi Bruix
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain
| | - Xavier Forns
- Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Spain.
| | - Maria Reig
- BCLC Group. Liver Unit, Hospital Clinic of Barcelona, IDIBAPS. CIBERehd, University of Barcelona, Barcelona, Spain.
| |
Collapse
|
28
|
Wu D, Yang Y, Jiang M, Yao R. Competing risk of the specific mortality among Asian-American patients with prostate cancer: a surveillance, epidemiology, and end results analysis. BMC Urol 2022; 22:42. [PMID: 35331219 PMCID: PMC8952266 DOI: 10.1186/s12894-022-00992-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/23/2021] [Indexed: 11/10/2022] Open
Abstract
Background Adopted the competing-risk model to investigate the relevant factors affecting the prostate cancer (PCa)-specific mortality among Asian-American PCa patients based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods The information of 26,293 Asian-American patients diagnosed with PCa between 2004 and 2015 were extracted from the SEER 18 database. Subjects were divided into three groups: died of PCa, died of other causes, survival based on the outcomes at the end of 155 months’ follow-up. Multivariate analysis was performed by the Fine-gray proportional model. Meanwhile, subgroup analyses were conducted risk stratification by race and age. Results Age ≥ 65 years [Hazard ratio (HR) = 1.509, 95% confidence interval (CI) 1.299–1.754], race (HR = 1.220, 95% CI 1.028–1.448), marital status (unmarried, single or widowed, HR = 1.264, 95% CI 1.098–1.454), tumor grade II (HR = 3.520, 95% CI 2.915–4.250), the American Joint Committee on Cancer (AJCC) stage (T3: HR = 1.597, 95% CI 1.286–1.984; T4: HR = 2.446, 95% CI 1.796–3.331; N1: HR = 1.504, 95% CI 1.176–1.924; M1: HR = 9.875, 95% CI 8.204–11.887) at diagnosis, radiotherapy (HR = 1.892, 95% CI 1.365–2.623), regional nodes positive (HR = 2.498, 95% CI 1.906–3.274) increased risk of PCa-specific mortality for Asian-American PCa patients, while surgical (HR = 0.716, 95% CI 0.586–0.874) reduced the risk. Conclusion The study findings showed that age, race, marital status, tumor grade (II), AJCC stages (T3, T4, N1, M1) at diagnosis, radiotherapy, regional nodes positive and surgery was associated with the specific mortality of PCa patients among Asian-Americans.
Collapse
Affiliation(s)
- Di Wu
- Department of Urology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, No. 16 Jichang Road, Baiyun District, Guangzhou, 510405, Guangdong, People's Republic of China.
| | - Yaming Yang
- Department of Urology, The First Clinical Medical College of Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510405, Guangdong, People's Republic of China
| | - Mingjuan Jiang
- Department of Urology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, No. 16 Jichang Road, Baiyun District, Guangzhou, 510405, Guangdong, People's Republic of China
| | - Ruizhi Yao
- Department of Urology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, No. 16 Jichang Road, Baiyun District, Guangzhou, 510405, Guangdong, People's Republic of China
| |
Collapse
|
29
|
Tamaki N, Kurosaki M, Yasui Y, Mori N, Tsuji K, Hasebe C, Joko K, Akahane T, Furuta K, Kobashi H, Kimura H, Yagisawa H, Marusawa H, Kondo M, Kojima Y, Yoshida H, Uchida Y, Tada T, Nakamura S, Yasuda S, Toyoda H, Loomba R, Izumi N. Hepatocellular Carcinoma Risk Assessment for Patients With Advanced Fibrosis After Eradication of Hepatitis C Virus. Hepatol Commun 2022; 6:461-472. [PMID: 34676692 PMCID: PMC8870028 DOI: 10.1002/hep4.1833] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The identification of patients with advanced fibrosis who do not need any further hepatocellular carcinoma (HCC) surveillance after the eradication of hepatitis C is pivotal. In this study, we developed a simple serum-based risk model that could identify patients with low-risk HCC. This was a nationwide multicenter study involving 16 Hospitals in Japan. Patients with advanced fibrosis (1,325 in a derivation cohort and 508 in a validation cohort) who achieved sustained virological responses at 24 weeks after treatment (SVR24) were enrolled. The HCC risk model at any point after SVR24 and its change were evaluated, and subsequent HCC development was analyzed. Based on the multivariable analysis, patients fulfilling all of the factors (GAF4 criteria: gamma-glutamyl transferase < 28 IU/L, alpha-fetoprotein < 4.0 ng/mL, and Fibrosis-4 Index < 4.28) were classified as low-risk and others were classified as high-risk. When patients were stratified at the SVR24, and 1 year, and 2 years after SVR24, subsequent HCC development was significantly lower in low-risk patients (0.5-1.1 per 100 person-years in the derivation cohort and 0.9-1.1 per 100 person-years in the validation cohort) than in high-risk patients at each point. HCC risk from 1 year after SVR24 decreased in patients whose risk improved from high-risk to low-risk (HCC incidence: 0.6 per 100 person-years [hazard ratio (HR) = 0.163 in the derivation cohort] and 1.3 per 100 person-years [HR = 0.239 in the validation cohort]) than in those with sustained high risk. Conclusion: The HCC risk model based on simple serum markers at any point after SVR and its change can identify patients with advanced fibrosis who are at low HCC risk, and these patients may be able to reduce HCC surveillance.
Collapse
Affiliation(s)
- Nobuharu Tamaki
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan.,NAFLD Research CenterDivision of Gastroenterology and HepatologyDepartment of MedicineUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Masayuki Kurosaki
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| | - Yutaka Yasui
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| | - Nami Mori
- Department of GastroenterologyHiroshima Red Cross Hospital and Atomic-bomb Survivors HospitalHiroshimaJapan
| | - Keiji Tsuji
- Department of GastroenterologyHiroshima Red Cross Hospital and Atomic-bomb Survivors HospitalHiroshimaJapan
| | - Chitomi Hasebe
- Department of GastroenterologyJapanese Red Cross Asahikawa HospitalAsahikawaHokkaidoJapan
| | - Kouji Joko
- Center for Liver-Biliary-Pancreatic DiseaseMatsuyama Red Cross HospitalMatsuyamaEhimeJapan
| | - Takehiro Akahane
- Department of GastroenterologyJapanese Red Cross Ishinomaki HospitalIshinomakiMiyagiJapan
| | - Koichiro Furuta
- Department of GastroenterologyMasuda Red Cross HospitalMasudaShimaneJapan
| | - Haruhiko Kobashi
- Department of GastroenterologyJapanese Red Cross Okayama HospitalOkayamaOkayamaJapan
| | - Hiroyuki Kimura
- Department of GastroenterologyJapanese Red Cross Kyoto Daiichi HospitalKyotoJapan
| | - Hitoshi Yagisawa
- Department of GastroenterologyJapanese Red Cross Akita HospitalAkitaJapan
| | - Hiroyuki Marusawa
- Department of Gastroenterology and HepatologyOsaka Red Cross HospitalOsakaJapan
| | - Masahiko Kondo
- Department of GastroenterologyJapanese Red Cross Otsu HospitalOtsuShigaJapan
| | - Yuji Kojima
- Department of HepatologyJapanese Red Cross Ise HospitalIseMieJapan
| | - Hideo Yoshida
- Department of GastroenterologyJapanese Red Cross Medical CenterTokyoJapan
| | - Yasushi Uchida
- Department of GastroenterologyMatsue Red Cross HospitalMatsueShimaneJapan
| | - Toshifumi Tada
- Department of Internal MedicineJapanese Red Cross Society Himeji HospitalHimejiJapan
| | - Shinichiro Nakamura
- Department of Internal MedicineJapanese Red Cross Society Himeji HospitalHimejiJapan
| | - Satoshi Yasuda
- Department of Gastroenterology and HepatologyOgaki Municipal HospitalOgakiJapan
| | - Hidenori Toyoda
- Department of Gastroenterology and HepatologyOgaki Municipal HospitalOgakiJapan
| | - Rohit Loomba
- NAFLD Research CenterDivision of Gastroenterology and HepatologyDepartment of MedicineUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Namiki Izumi
- Department of Gastroenterology and HepatologyMusashino Red Cross HospitalTokyoJapan
| |
Collapse
|
30
|
Innes H, Johnson P, McDonald SA, Hamill V, Yeung A, Dillon JF, Hayes PC, Went A, Barclay ST, Fraser A, Bathgate A, Goldberg DJ, Hutchinson SJ. Competing Risk Bias in Prognostic Models Predicting Hepatocellular Carcinoma Occurrence: Impact on Clinical Decision-making. GASTRO HEP ADVANCES 2022; 1:129-136. [PMID: 39131124 PMCID: PMC11307513 DOI: 10.1016/j.gastha.2021.11.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/17/2021] [Indexed: 08/13/2024]
Abstract
Background and Aims Existing models predicting hepatocellular carcinoma (HCC) occurrence do not account for competing risk events and, thus, may overestimate the probability of HCC. Our goal was to quantify this bias for patients with cirrhosis and cured hepatitis C. Methods We analyzed a nationwide cohort of patients with cirrhosis and cured hepatitis C infection from Scotland. Two HCC prognostic models were developed: (1) a Cox regression model ignoring competing risk events and (2) a Fine-Gray regression model accounting for non-HCC mortality as a competing risk. Both models included the same set of prognostic factors used by previously developed HCC prognostic models. Two predictions were calculated for each patient: first, the 3-year probability of HCC predicted by model 1 and second, the 3-year probability of HCC predicted by model 2. Results The study population comprised 1629 patients with cirrhosis and cured HCV, followed for 3.8 years on average. A total of 82 incident HCC events and 159 competing risk events (ie, non-HCC deaths) were observed. The mean predicted 3-year probability of HCC was 3.37% for model 1 (Cox) and 3.24% for model 2 (Fine-Gray). For most patients (76%), the difference in the 3-year probability of HCC predicted by model 1 and model 2 was minimal (ie, within 0 to ±0.3%). A total of 2.6% of patients had a large discrepancy exceeding 2%; however, these were all patients with a 3-year probability exceeding >5% in both models. Conclusion Prognostic models that ignore competing risks do overestimate the future probability of developing HCC. However, the degree of overestimation-and the way it is patterned-means that the impact on HCC screening decisions is likely to be modest.
Collapse
Affiliation(s)
- Hamish Innes
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
- Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK
| | - Philip Johnson
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Scott A. McDonald
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Victoria Hamill
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Alan Yeung
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - John F. Dillon
- Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, UK
| | | | | | | | - Andrew Fraser
- Aberdeen Royal Infirmary, Aberdeen, UK
- Queen Elizabeth University Hospital, Glasgow, UK
| | | | - David J. Goldberg
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Sharon J. Hutchinson
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| |
Collapse
|
31
|
Sato M, Tateishi R, Moriyama M, Fukumoto T, Yamada T, Nakagomi R, Kinoshita MN, Nakatsuka T, Minami T, Uchino K, Enooku K, Nakagawa H, Shiina S, Ninomiya K, Kodera S, Yatomi Y, Koike K. Machine Learning-Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation. GASTRO HEP ADVANCES 2022; 1:29-37. [PMID: 39129938 PMCID: PMC11308827 DOI: 10.1016/j.gastha.2021.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/13/2021] [Indexed: 08/13/2024]
Abstract
Background and Aims Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models-including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index and was validated externally using the split-sample method. Results The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups (P < .001 among all risk groups). The c-index of DeepSurv was 0.64. In order of significance, the tumor number, serum albumin level, and des-gamma-carboxyprothrombin level were the most important variables for the prediction of HCC recurrence in the GBDT model. Also, the current GBDT model enabled the output of a personalized cumulative recurrence prediction curve for each patient. Conclusion We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.
Collapse
Affiliation(s)
- Masaya Sato
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryosuke Tateishi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Makoto Moriyama
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Fukumoto
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomoharu Yamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Nakagomi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Takuma Nakatsuka
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tatsuya Minami
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Uchino
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kenichiro Enooku
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shuichiro Shiina
- Department of Gastroenterology, Juntendo University, Tokyo, Japan
| | - Kota Ninomiya
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
32
|
Nahon P, Najean M, Layese R, Zarca K, Segar LB, Cagnot C, Ganne-Carrié N, N'Kontchou G, Pol S, Chaffaut C, Carrat F, Ronot M, Audureau E, Durand-Zaleski I. Early hepatocellular carcinoma detection using magnetic resonance imaging is cost-effective in high-risk patients with cirrhosis. JHEP REPORTS : INNOVATION IN HEPATOLOGY 2022; 4:100390. [PMID: 34977518 PMCID: PMC8683591 DOI: 10.1016/j.jhepr.2021.100390] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/02/2021] [Accepted: 10/20/2021] [Indexed: 12/12/2022]
Abstract
Background & Aims Reinforced hepatocellular carcinoma (HCC) surveillance using magnetic resonance imaging (MRI) could increase early tumour detection but faces cost-effectiveness issues. In this study, we aimed to evaluate the cost-effectiveness of MRI for the detection of very early HCC (Barcelona Clinic Liver Cancer [BCLC] 0) in patients with an annual HCC risk >3%. Methods French patients with compensated cirrhosis included in 4 multicentre prospective cohorts were considered. A scoring system was constructed to identify patients with an annual risk >3%. Using a Markov model, the economic evaluation estimated the costs and life years (LYs) gained with MRI vs. ultrasound (US) monitoring over a 20-year period. The incremental cost-effectiveness ratio (ICER) was calculated by dividing the incremental costs by the incremental LYs. Results Among 2,513 patients with non-viral causes of cirrhosis (n = 840) and/or cured HCV (n = 1,489)/controlled HBV infection (n = 184), 206 cases of HCC were detected after a 37-month follow-up. When applied to training (n = 1,658) and validation (n = 855) sets, the construction of a scoring system identified 33.4% and 37.5% of patients with an annual HCC risk >3% (3-year C-Indexes 75 and 76, respectively). In patients with a 3% annual risk, the incremental LY gained with MRI was 0.4 for an additional cost of €6,134, resulting in an ICER of €15,447 per LY. Compared to US monitoring, MRI detected 5x more BCLC 0 HCC. The deterministic sensitivity analysis confirmed the impact of HCC incidence. At a willingness to pay of €50,000/LY, MRI screening had a 100% probability of being cost-effective. Conclusions In the era of HCV eradication/HBV control, patients with annual HCC risk >3% represent one-third of French patients with cirrhosis. MRI is cost-effective in this population and could favour early HCC detection. Lay summary The early identification of hepatocellular carcinoma in patients with cirrhosis is important to improve patient outcomes. Magnetic resonance imaging could increase early tumour detection but is more expensive and less accessible than ultrasound (the standard modality for surveillance). Herein, using a simple score, we identified a subgroup of patients with cirrhosis (accounting for >one-third), who were at increased risk of hepatocellular carcinoma and for whom the increased expense of magnetic resonance imaging would be justified by the potential improvement in outcomes.
Collapse
Key Words
- AFP, alpha-fetoprotein
- AMRI, abbreviated magnetic resonance imaging
- BCLC, Barcelona Clinic Liver Cancer
- HCC, hepatocellular carcinoma
- HR, hazard ratio
- ICER, incremental cost-effectiveness ratio
- LY, life years
- LYG, life years gained
- MRI
- MRI, magnetic resonance imaging
- NAFLD, non-alcoholic fatty liver disease
- QALY, quality-adjusted life year
- RFA, radiofrequency ablation
- SHR, subdistribution hazard ratio
- TACE, transarterial chemoembolization
- US, ultrasound
- cirrhosis
- cost-effectiveness
- liver cancer risk
- surveillance
Collapse
Affiliation(s)
- Pierre Nahon
- AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Unit, Bobigny, France.,Université Sorbonne Paris Nord, F-93000 Bobigny, France.,Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de recherche des Cordeliers, Université de Paris, Paris, France
| | - Marie Najean
- Université de Paris, CRESS, INSERM, INRA, URCEco, AP-HP, Hôpital de l'Hôtel Dieu, F-75004, Paris, France
| | - Richard Layese
- Univ Paris Est Créteil, INSERM, IMRB, Equipe CEpiA (Clinical Epidemiology and Ageing), Unité de Recherche Clinique (URC Mondor), Service de Santé Publique, Assistance Publique Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, F-94000, Créteil, France
| | - Kevin Zarca
- Université de Paris, CRESS, INSERM, INRA, URCEco, AP-HP, Hôpital de l'Hôtel Dieu, F-75004, Paris, France
| | - Laeticia Blampain Segar
- Université de Paris, CRESS, INSERM, INRA, URCEco, AP-HP, Hôpital de l'Hôtel Dieu, F-75004, Paris, France
| | - Carole Cagnot
- Clinical Research Department, ANRS
- Emerging Infectious Diseases, Paris, France
| | - Nathalie Ganne-Carrié
- AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Unit, Bobigny, France.,Université Sorbonne Paris Nord, F-93000 Bobigny, France.,Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de recherche des Cordeliers, Université de Paris, Paris, France
| | - Gisèle N'Kontchou
- AP-HP, Hôpitaux Universitaires Paris Seine Saint-Denis, Liver Unit, Bobigny, France.,Université Sorbonne Paris Nord, F-93000 Bobigny, France.,Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Centre de recherche des Cordeliers, Université de Paris, Paris, France
| | - Stanislas Pol
- Université de Paris, département d'hépatologie/Addictologie, Hôpital Cochin, APHP, Paris, France
| | - Cendrine Chaffaut
- SBIM, APHP, Hôpital Saint-Louis, Paris, France.,Inserm, UMR-1153, ECSTRA Team, Paris, France
| | - Fabrice Carrat
- Sorbonne Université, Inserm, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP, Hôpital Saint-Antoine, Unité de Santé Publique, Paris, France
| | - Maxime Ronot
- AP-HP, Hôpital Beaujon, Service de Radiologie, Clichy, France
| | - Etienne Audureau
- Univ Paris Est Créteil, INSERM, IMRB, Equipe CEpiA (Clinical Epidemiology and Ageing), Unité de Recherche Clinique (URC Mondor), Service de Santé Publique, Assistance Publique Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, F-94000, Créteil, France
| | - Isabelle Durand-Zaleski
- Université de Paris, CRESS, INSERM, INRA, URCEco, AP-HP, Hôpital de l'Hôtel Dieu, F-75004, Paris, France
| | | | | | | | | |
Collapse
|
33
|
A novel nomogram based on the nutritional risk screening 2002 score to predict survival in hepatocellular carcinoma treated with transarterial chemoembolization. NUTR HOSP 2022; 39:835-842. [DOI: 10.20960/nh.03983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
|
34
|
A 2-Step Strategy Combining FIB-4 With Transient Elastography and Ultrasound Predicted Liver Cancer After HCV Cure. Am J Gastroenterol 2022; 117:138-146. [PMID: 34817975 DOI: 10.14309/ajg.0000000000001503] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 08/03/2021] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Despite the direct-acting antiviral therapy has dramatically decreased the likelihood of having liver-related complications and extrahepatic outcomes, the risk of developing hepatocellular carcinoma (HCC) is not totally eliminated after sustained virological response (SVR). We aimed to develop an easy-to-apply strategy to be adopted in clinical practice for accurately classifying the HCC risk in hepatitis C virus patients after SVR. METHODS Prospective and multicenter study enrolling hepatitis C virus patients with advanced fibrosis (transient elastography [TE] > 10 kPa) or cirrhosis by ultrasound showing SVR. They were followed up until HCC, liver transplantation, death, or until October 2020, which occurred first, with a minimum follow-up period of 6 months after SVR (follow-up: 49 [interquartile range 28-59] months). RESULTS Patients with cirrhosis by ultrasound represented 58% (611/1,054) of the overall cohort. During the study, HCC occurrence was 5.3% (56/1,054). Multivariate analyses revealed that Fibrosis-4 (FIB-4) > 3.25 (hazard ratio [HR] 2.26 [1.08-4.73]; P = 0.030), TE (HR 1.02 [1.00-1.04]; P = 0.045) and cirrhosis by ultrasound (HR 3.15 [1.36-7.27]; P = 0.007) predicted HCC occurrence. Baseline HCC screening criteria (TE > 10 kPa or cirrhosis) identified patients at higher risk of HCC occurrence in presence of FIB-4 > 3.25 (8.8%; 44/498) vs FIB-4 < 3.25 (2.4%; 12/506), while those with only FIB > 3.25 had no HCC (0%; 0/50) (logRank 22.129; P = 0.0001). A combination of baseline FIB-4 > 3.25 and HCC screening criteria had an annual incidence >1.5 cases per 100 person-years, while the rest of the groups remained <1 case. Patients who maintained post-treatment FIB-4 > 3.25 and HCC screening criteria remained at the highest risk of HCC occurrence (13.7% [21/153] vs 4.9% [9/184]; logRank 7.396, P = 0.007). DISCUSSION We demonstrated that a two-step strategy combining FIB-4, TE, and ultrasound could help stratify HCC incidence risk after SVR.
Collapse
|
35
|
Innes H, Jepsen P, McDonald S, Dillon J, Hamill V, Yeung A, Benselin J, Went A, Fraser A, Bathgate A, Ansari MA, Barclay ST, Goldberg D, Hayes PC, Johnson P, Barnes E, Irving W, Hutchinson S, Guha IN. Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection. JHEP Rep 2021; 3:100384. [PMID: 34805817 PMCID: PMC8585647 DOI: 10.1016/j.jhepr.2021.100384] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND & AIMS Hepatocellular carcinoma (HCC) prediction models can inform clinical decisions about HCC screening provided their predictions are robust. We conducted an external validation of 6 HCC prediction models for UK patients with cirrhosis and a HCV virological cure. METHODS Patients with cirrhosis and cured HCV were identified from the Scotland HCV clinical database (N = 2,139) and the STratified medicine to Optimise Treatment of Hepatitis C Virus (STOP-HCV) study (N = 606). We calculated patient values for 4 competing non-genetic HCC prediction models, plus 2 genetic models (for the STOP-HCV cohort only). Follow-up began at the date of sustained virological response (SVR) achievement. HCC diagnoses were identified through linkage to nation-wide cancer, hospitalisation, and mortality registries. We compared discrimination and calibration measures between prediction models. RESULTS Mean follow-up was 3.4-3.9 years, with 118 (Scotland) and 40 (STOP-HCV) incident HCCs observed. The age-male sex-ALBI-platelet count score (aMAP) model showed the best discrimination; for example, the Concordance index (C-index) in the Scottish cohort was 0.77 (95% CI 0.73-0.81). However, for all models, discrimination varied by cohort (being better for the Scottish cohort) and by age (being better for younger patients). In addition, genetic models performed better in patients with HCV genotype 3. The observed 3-year HCC risk was 3.3% (95% CI 2.6-4.2) and 5.1% (3.5-7.0%) in the Scottish and STOP-HCV cohorts, respectively. These were most closely matched by aMAP, in which the mean predicted 3-year risk was 3.6% and 5.0% in the Scottish and STOP-HCV cohorts, respectively. CONCLUSIONS aMAP was the best-performing model in terms of both discrimination and calibration and, therefore, should be used as a benchmark for rival models to surpass. This study underlines the opportunity for 'real-world' risk stratification in patients with cirrhosis and cured HCV. However, auxiliary research is needed to help translate an HCC risk prediction into an HCC-screening decision. LAY SUMMARY Patients with cirrhosis and cured HCV are at high risk of developing liver cancer, although the risk varies substantially from one patient to the next. Risk calculator tools can alert clinicians to patients at high risk and thereby influence decision-making. In this study, we tested the performance of 6 risk calculators in more than 2,500 patients with cirrhosis and cured HCV. We show that some risk calculators are considerably better than others. Overall, we found that the 'aMAP' calculator worked the best, but more work is needed to convert predictions into clinical decisions.
Collapse
Key Words
- ALT, alanine aminotransferase
- AST, aspartate aminotransferase
- C-index, Concordance index
- External validation
- GGT, gamma glutamyl transferase
- GRS, genetic risk score
- Genetic risk scores
- HCC, hepatocellular carcinoma
- ICD, International Classification of Diseases
- IDU, injecting-drug user
- IF, interferon
- PNPLA3, patatin-like phospholipase domain-containing protein 3
- Primary liver cancer
- Prognosis
- Risk prediction
- SMR01, Scottish Inpatient Hospital Admission Database
- SMR06, Scottish Cancer Register
- STOP-HCV, STratified medicine to Optimise Treatment of Hepatitis C Virus
- SVR, sustained virological response
- THRI, Toronto HCC Risk Index
- VHA, Veteran Health Affairs
- aMAP, age-male sex-ALBI-platelet count score
Collapse
Affiliation(s)
- Hamish Innes
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
- Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK
| | - Peter Jepsen
- Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK
- Department of Hepatology and Gastroenterology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Scott McDonald
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - John Dillon
- Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, UK
| | - Victoria Hamill
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Alan Yeung
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Jennifer Benselin
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK
| | | | - Andrew Fraser
- Aberdeen Royal Infirmary, Aberdeen, UK
- Queen Elizabeth University Hospital, Glasgow, UK
| | | | - M. Azim Ansari
- Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine and the Oxford NIHR Biomedical Research Centre, Oxford University, Oxford, UK
| | | | - David Goldberg
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | | | - Philip Johnson
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Eleanor Barnes
- Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine and the Oxford NIHR Biomedical Research Centre, Oxford University, Oxford, UK
| | - William Irving
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK
| | - Sharon Hutchinson
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
- Public Health Scotland, Glasgow, UK
| | - Indra Neil Guha
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK
| |
Collapse
|
36
|
Demirtas CO, Brunetto MR. Surveillance for hepatocellular carcinoma in chronic viral hepatitis: Is it time to personalize it? World J Gastroenterol 2021; 27:5536-5554. [PMID: 34588750 PMCID: PMC8433616 DOI: 10.3748/wjg.v27.i33.5536] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 04/28/2021] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
Surveillance with abdominal ultrasound with or without alpha-fetoprotein is recommended by clinical practice guidelines for patients who are considered to be at risk of developing hepatocellular carcinoma (HCC), including those with cirrhosis, advanced fibrosis and special subgroups of chronic hepatitis B (CHB). Application of the standard surveillance strategy to all patients with chronic liver disease (CLD) with or without cirrhosis imposes major sustainability and economic burdens on healthcare systems. Thus, a number of HCC risk scores were constructed, mainly from Asian cohorts, to stratify the HCC prediction in patients with CHB. Similarly, even if less than for CHB, a few scoring systems were developed for chronic hepatitis C patients or cirrhotic patients with CLD of different etiologies. Recently, a few newsworthy HCC-risk algorithms were developed for patients with cirrhosis using the combination of serologic HCC markers and clinical parameters. Overall, the HCC risk stratification appears at hand by several validated multiple score systems, but their optimal performance is obtained only in populations who show highly homogenous clinic-pathologic, epidemiologic, etiologic and therapeutic characteristics and this limitation poses a major drawback to their sustainable use in clinical practice. A better understanding of the dynamic process driving the progression from CLD to HCC derived from studies based on molecular approaches and genetics, epigenetics and liquid biopsy will enable the identification of new biomarkers to define the individual risk of HCC in the near future, with the possibility to achieve a real and cost/effective personalization of surveillance.
Collapse
Affiliation(s)
- Coskun Ozer Demirtas
- Department of Gastroenterology and Hepatology, Marmara University, School of Medicine, Istanbul 34854, Turkey
| | - Maurizia Rossana Brunetto
- Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa 56125, Italy
- Hepatology Unit, University Hospital of Pisa, Pisa 56125, Italy
- Biostructure and Bio-imaging Institute, National Research Council of Italy, Naples 56125, Italy
| |
Collapse
|
37
|
Poynard T, Lacombe JM, Deckmyn O, Peta V, Akhavan S, de Ledinghen V, Zoulim F, Samuel D, Mathurin P, Ratziu V, Thabut D, Housset C, Fontaine H, Pol S, Carrat F. External validation of LCR1-LCR2, a multivariable HCC risk calculator, in patients with chronic HCV. JHEP Rep 2021; 3:100298. [PMID: 34142073 PMCID: PMC8187244 DOI: 10.1016/j.jhepr.2021.100298] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/07/2021] [Accepted: 04/12/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND & AIMS The Liver Cancer Risk test algorithm (LCR1-LCR2) is a multianalyte blood test combining proteins involved in liver cell repair (apolipoprotein-A1 and haptoglobin), known hepatocellular carcinoma (HCC) risk factors (sex, age, and gamma-glutamyl transferase), a marker of fibrosis (alpha2-macroglobulin) and alpha-fetoprotein (AFP), a specific marker of HCC. The aim was to externally validate the LCR1-LCR2 in patients with chronic HCV (CHC) treated or not with antivirals. METHODS Pre-included patients were from the Hepather cohort, a multicentre prospective study in adult patients with CHC in France. LCR1-LCR2 was assessed retrospectively in patients with the test components and AFP, available at baseline. The co-primary study outcome was the negative predictive value (NPV) of LCR1-LCR2 for the occurrence of HCC at 5 years and for survival without HCC according to the predetermined LCR1-LCR2 cut-offs. The cut-offs were adjusted for risk covariables and for the response to HCV treatment, and were quantified using time-dependent proportional hazards models. RESULTS In total, 4,903 patients, 1,026 (21.9%) with baseline cirrhosis, were included in the study. Patients were followed for a median of 5.7 (IQR 4.2-11.3) years. A total of 3,788/4,903 (77.3%) patients had a sustained virological response. There were 137 cases of HCC at 5 years and 214 at the end of follow-up. HCC occurred at 5 years in 24/3,755 patients with low-risk LCR1-LCR2 compared with 113/1,148 patients with high-risk LCR1-LCR2. The NPV was 99.4% (95% CI 99.1-99.6). Similar findings (hazard ratio, 10.8; 95% CI, 8.1-14.3; p <0.001) were obtained after adjustment for exposure to antivirals, age, sex, geographical origin, HCV genotype 3, alcohol consumption, and type 2 diabetes mellitus. CONCLUSIONS The results showed that LCR1-LCR2 can be used to successfully identify patients with HCV at very low risk of HCC at 5 years. LAY SUMMARY Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and the fastest growing cause of cancer death in many countries. We constructed and internally validated a new multianalyte blood test to assess this Liver Cancer Risk (LCR1-LCR2). This study confirmed the performance of LCR1-LCR2 in patients with chronic HCV in the national French cohort Hepather, and its ability to identify patients at a very low risk of HCC at 5 years. CLINICAL TRIALS REGISTRATION The study is registered at ClinicalTrials.gov (NCT01953458).
Collapse
Key Words
- AFP
- AFP, alpha-fetoprotein
- AUROC, area under the receiver operating curve
- CHC, chronic HCV
- Cirrhosis
- DAA, direct-acting antivirals
- EASL, European Association for the Study of the Liver
- FIB4, Fibrosis-4
- FibroTest™
- Fibrosis progression
- HCC, hepatocellular carcinoma
- LCR, Liver Cancer Risk
- LCR1-LCR2
- Liver Cancer Risk
- Multi-analyte blood test
- NNS, needed to screen
- NPV, negative predictive value
- SIR, standardised incidence ratio
- STARD, Standards for the Reporting of Diagnostic Accuracy Studies
- STROBE, Strengthening the Reporting of Observational Studies in Epidemiology
- SVR, sustained virological response
- Surveillance
- VCTE, vibration-controlled transient elastography
Collapse
Affiliation(s)
- Thierry Poynard
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Jean Marc Lacombe
- Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique, Paris, France
| | | | - Valentina Peta
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
- BioPredictive, Paris, France
| | - Sepideh Akhavan
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, Pitié-Salpêtrière Hospital, Paris, France
| | - Victor de Ledinghen
- Hepatology Unit Hôpital Haut-Lévêque, Pessac, and INSERM U1053, Université Bordeaux Segalen, Bordeaux, France
| | - Fabien Zoulim
- Hospices civils de Lyon, Hôpital Croix Rousse, Department of Hepatology, INSERM U1052, Université de Lyon, Lyon, France
| | - Didier Samuel
- AP-HP, Hospital Paul Brousse, Hepatology Department, UMR-S1193, Villejuif, France
- Université Paris-Saclay, and Hepatinov, Villejuif, France
| | | | - Vlad Ratziu
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Dominique Thabut
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Chantal Housset
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Hélène Fontaine
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, AP-HP, Hôpital Cochin, Hepatology Department, Paris, France
| | - Stanislas Pol
- Assistance Publique-Hôpitaux de Paris (AP-HP), Department of Hepato-Gastroenterology, AP-HP, Hôpital Cochin, Hepatology Department, Paris, France
| | - Fabrice Carrat
- Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique, Paris, France
| |
Collapse
|
38
|
Costentin CE, Nahon P. HCC risk prediction using biomarkers in non-cirrhotic patients following HCV eradication: Reassuring the patient or the doctor? JHEP Rep 2021; 3:100320. [PMID: 34308325 PMCID: PMC8283026 DOI: 10.1016/j.jhepr.2021.100320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 05/26/2021] [Indexed: 11/18/2022] Open
Affiliation(s)
- Charlotte E. Costentin
- Université Grenoble Alpes, 38000 Grenoble, France
- Institute for Advanced Biosciences, Research Center UGA/Inserm U 1209/CNRS 5309, 38700 La Tronche, France
- Service d’hépato-gastroentérologie, Pôle Digidune, CHU Grenoble Alpes, 38700 La Tronche, France
- Corresponding authors: Addresses: Centre Hospitalier Universitaire Grenoble Alpes, Avenue Maquis du Grésivaudan, 38700 La Tronche, France; Tel.: +33 4 76 76 75 75.
| | - Pierre Nahon
- Centre de Recherche des Cordeliers, Sorbonne Universités, Université Paris Descartes, Université Paris Diderot, Université Paris, Paris, France
- Functional Genomics of Solid Tumors, USPC, Université Paris Descartes, Université Paris Diderot, Université Paris, Paris, France
- Service d'hépatologie, Hôpital Avicenne, Assistance-Publique Hôpitaux de Paris, 125 Route de Stalingrad 93000 Bobigny, France
- Unité de Formation et de Recherche Santé Médecine et Biologie Humaine, Université Paris 13, Communauté d'Universités et Etablissements Sorbonne Paris Cité, Paris, France
- Service d’hépatologie, Hôpital Avicenne, Assistance-Publique Hôpitaux de Paris, 125 Route de Stalingrad 93000 Bobigny, France; Tel.: +33 148026294.
| |
Collapse
|
39
|
Maan R, van der Meer AJ. Hepatocellular carcinoma (HCC) risk stratification after virological cure for hepatitis C virus (HCV)-induced cirrhosis: time to refine predictive models. Hepatobiliary Surg Nutr 2021; 10:385-387. [PMID: 34159170 DOI: 10.21037/hbsn-21-95] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Raoel Maan
- Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Adriaan J van der Meer
- Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
40
|
We need stronger evidence for (or against) hepatocellular carcinoma surveillance. J Hepatol 2021; 74:1234-1239. [PMID: 33465402 DOI: 10.1016/j.jhep.2020.12.029] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 02/07/2023]
Abstract
Current guidelines from EASL recommend that most patients with cirrhosis are offered surveillance for hepatocellular carcinoma (HCC), but fewer patients than expected actually receive it. The recommendation is based on observational studies and simulations, not randomised trials. In this opinion piece we argue that a randomised trial of HCC surveillance vs. no surveillance is necessary and feasible, and we believe that clinician and patient participation in HCC surveillance would be better if it were based on trial results demonstrating its value.
Collapse
|
41
|
Vo Quang E, Shimakawa Y, Nahon P. Epidemiological projections of viral-induced hepatocellular carcinoma in the perspective of WHO global hepatitis elimination. Liver Int 2021; 41:915-927. [PMID: 33641230 DOI: 10.1111/liv.14843] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/19/2021] [Accepted: 02/13/2021] [Indexed: 12/13/2022]
Abstract
Hepatitis B is an eminent risk factor for hepatocellular carcinoma (HCC) in Southeast Asia and sub-Saharan Africa, whereas hepatitis C is a key risk factor for HCC in Western Europe and North America. Increased awareness of the global burden of viral hepatitis resulted, in May 2016, in the adoption of the first global health sector strategy on viral hepatitis by the World Health Assembly, which calls for the elimination of viral hepatitis as a public health threat by 2030. Although the incidence of liver cancer resulting from viral infections has increased since the 1990s, the implementation of public health interventions, such as hepatitis B vaccination and antiviral therapies might have reduced the global burdens of HCC. Hepatitis B immunization in infancy has been associated with a reduction in the risk of infant fulminant hepatitis, chronic liver disease, and HCC in Taiwan. Achieving viral hepatitis elimination by 2030 can be accelerated by improving the access to HCC screening programs. HCC surveillance programs in developed countries must be refined to increase an access to personalized surveillance program, whereas the limited access to surveillance and treatment of HCC in developing countries remains a significant public health issue.
Collapse
Affiliation(s)
- Erwan Vo Quang
- Université Paris-Sud, Université Paris-Saclay, Kremlin-Bicêtre, France.,AP-HP, Hôpital Avicenne, Service d'Hépatologie, Bobigny, France.,Equipe labellisée Ligue Contre le Cancer, Université Paris 13, Sorbonne Paris Cité, Saint-Denis, France.,Inserm, UMR-1162, Génomique fonctionnelle des tumeurs solides, Paris, France
| | - Yusuke Shimakawa
- Unité d'Epidémiologie des Maladies Emergentes, Institut Pasteur, Paris, France
| | - Pierre Nahon
- AP-HP, Hôpital Avicenne, Service d'Hépatologie, Bobigny, France.,Equipe labellisée Ligue Contre le Cancer, Université Paris 13, Sorbonne Paris Cité, Saint-Denis, France.,Inserm, UMR-1162, Génomique fonctionnelle des tumeurs solides, Paris, France
| |
Collapse
|
42
|
Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021; 36:569-580. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 12/14/2022]
Abstract
The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline-specific therapy toward patient-specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi-omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non-alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real-world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.
Collapse
Affiliation(s)
- Tung-Hung Su
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.,Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Chih-Horng Wu
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
| | - Jia-Horng Kao
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.,Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan.,Graduate Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei, Taiwan.,Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan
| |
Collapse
|
43
|
Stratification of Hepatocellular Carcinoma Risk Following HCV Eradication or HBV Control. J Clin Med 2021; 10:jcm10020353. [PMID: 33477752 PMCID: PMC7832303 DOI: 10.3390/jcm10020353] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/07/2021] [Accepted: 01/13/2021] [Indexed: 12/18/2022] Open
Abstract
Hepatocellular carcinoma (HCC) incidence has dramatically decreased in patients infected with HCV and HBV due to the widespread use of highly effective antiviral agents. Nevertheless, a substantial proportion of patients with advanced fibrosis or cirrhosis following HCV clearance of in case of HBV control whatever the stage of fibrosis remains at risk of liver cancer development. Cancer predictors in these virus-free patients include routine parameters estimating coexisting comorbidities, persisting liver inflammation or function impairment, and results of non-invasive tests which can be easily combined into HCC risk scoring systems. The latter enables stratification according to various liver cancer incidences and allocation of patients into low, intermediate or high HCC risk probability groups. All international guidelines endorse lifelong surveillance of these patients using semi-annual ultrasound, with known sensibility issues. Refining HCC prediction in this growing population ultimately will trigger personalized management using more effective surveillance tools such as contrast-enhanced imaging techniques or circulating biomarkers while taking into account cost-effectiveness parameters.
Collapse
|
44
|
Calvaruso V, Bruix J. Towards personalized screening for hepatocellular carcinoma: Still not there. J Hepatol 2020; 73:1319-1321. [PMID: 32771323 DOI: 10.1016/j.jhep.2020.06.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Vincenza Calvaruso
- GI & Liver Unit, Department of Health Promotion Sciences Maternal and Infantile Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo.
| | - Jordi Bruix
- BCLC group, Liver Unit, Hospital Clínic, University of Barcelona, IDIBAPS, CIBEREHD
| |
Collapse
|