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Bi S, Zhu J, Huang L, Feng W, Peng L, Leng L, Wang Y, Shan P, Kong W, Zhu S. Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer. Int J Mol Sci 2024; 25:6849. [PMID: 38999959 PMCID: PMC11241446 DOI: 10.3390/ijms25136849] [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: 04/26/2024] [Revised: 06/09/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.
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Affiliation(s)
- Suzhen Bi
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Jie Zhu
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 00014 Helsinki, Finland
| | - Liting Huang
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Wanting Feng
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Lulu Peng
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Liangqi Leng
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Yin Wang
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Peipei Shan
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Weikaixin Kong
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 00014 Helsinki, Finland
| | - Sujie Zhu
- Institute of Translational Medicine, College of Medicine, Qingdao University, Qingdao 266021, China
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Rui F, Xu L, Yeo YH, Xu Y, Ni W, Tan Y, Zheng Q, Tian X, Zeng QL, He Z, Qiu Y, Zhu C, Ding W, Wang J, Huang R, Xue Q, Wang X, Chen Y, Fan J, Fan Z, Ogawa E, Kwak MS, Qi X, Shi J, Wong VWS, Wu C, Li J. Machine Learning-Based Models for Advanced Fibrosis and Cirrhosis Diagnosis in Chronic Hepatitis B Patients With Hepatic Steatosis. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00553-6. [PMID: 38906440 DOI: 10.1016/j.cgh.2024.06.014] [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: 08/22/2023] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND AND AIMS The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants noninvasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population. METHODS Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in 10 medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from SHAP (Shapley Additive exPlanations), were compared with Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index using the area under receiver-operating characteristic curve (AUROC) and decision curve analysis (DCA). RESULTS Of 1,198 eligible patients, the random forest model achieved AUROCs of 0.778 (95% confidence interval [CI], 0.749-0.807) for diagnosing advanced fibrosis (random forest advanced fibrosis model) and 0.777 (95% CI, 0.748-0.806) for diagnosing cirrhosis (random forest cirrhosis model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the random forest advanced fibrosis model obtained an AUROC of 0.825 (95% CI, 0.787-0.862) in patients with hepatitis B virus DNA ≥105 IU/mL, and the random forest cirrhosis model had an AUROC of 0.828 (95% CI, 0.774-0.883) in female patients. The 2 models outperformed Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index in the training cohort, and also performed well in the validation cohort. CONCLUSIONS The random forest models provide reliable, noninvasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the comanagement of the 2 diseases. CLINICALTRIALS gov, Number: NCT05766449.
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Affiliation(s)
- Fajuan Rui
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China
| | - Liang Xu
- Clinical School of the Second People's Hospital, Tianjin Medical University, Tianjin, China; Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China; Tianjin Research Institute of Liver Diseases, Tianjin, China
| | - Yee Hui Yeo
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yayun Xu
- Department of Gastroenterology, West China Tianfu Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wenjing Ni
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China
| | - Youwen Tan
- Department of Hepatology, The Third Hospital of Zhenjiang Affiliated Jiangsu University, Zhenjiang, China
| | - Qi Zheng
- Department of Hepatology, Hepatology Research institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Xiaorong Tian
- School of Computer Science, China University of Geosciences, Wuhan, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
| | - Qing-Lei Zeng
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zebao He
- Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, China
| | - Yuanwang Qiu
- Department of Infectious Diseases, The Fifth People's Hospital of Wuxi, Wuxi, China
| | - Chuanwu Zhu
- Department of Infectious Diseases, The Affiliated Infectious Diseases Hospital of Soochow University, Suzhou, China
| | - Weimao Ding
- Department of Hepatology, Huai'an No.4 People's Hospital, Huai'an, China
| | - Jian Wang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Rui Huang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qi Xue
- Department of Infectious Diseases, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, China
| | - Xueqi Wang
- Department of Gastroenterology, The First Affiliated Hospital of Shandong Second Medical University, Weifang People's Hospital, Weifang, China
| | - Yunliang Chen
- School of Computer Science, China University of Geosciences, Wuhan, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
| | - Junqing Fan
- School of Computer Science, China University of Geosciences, Wuhan, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
| | - Zhiwen Fan
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Eiichi Ogawa
- Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Japan
| | - Min-Sun Kwak
- Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
| | - Junping Shi
- Department of Infectious and Hepatology Diseases, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
| | - Vincent Wai-Sun Wong
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Chao Wu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China
| | - Jie Li
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China.
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Kim G, Yu TY, Jee JH, Bae JC, Kang M, Kim JH. Association between nonalcoholic fatty liver disease and left ventricular diastolic dysfunction: A 7-year retrospective cohort study of 3,380 adults using serial echocardiography. DIABETES & METABOLISM 2024; 50:101534. [PMID: 38608865 DOI: 10.1016/j.diabet.2024.101534] [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: 01/08/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/14/2024]
Abstract
AIM Left ventricular diastolic dysfunction (LVDD) has been observed in people with nonalcoholic fatty liver disease (NAFLD) in cross-sectional studies but the causal relationship is unclear. This study aimed to investigate the impact of NAFLD and the fibrotic progression of the disease on the development of LVDD, assessed by serial echocardiography, in a large population over a 7-year longitudinal setting. METHODS This retrospective cohort study included the data of 3,380 subjects from a medical health check-up program. We defined subjects having NAFLD by abdominal ultrasonography and assessed significant liver fibrosis by the aspartate transaminase (AST) to platelet ratio index (APRI), the NAFLD fibrosis score (NFS), and the fibrosis-4 (FIB-4) index. LVDD was defined using serial echocardiography. A parametric Cox proportional hazards model was used. RESULTS During 11,327 person-years of follow-up, there were 560 (16.0 %) incident cases of LVDD. After adjustment for multiple risk factors, subjects with NAFLD showed an increased adjusted hazard ratio (aHR) of 1.21 (95 % confidence interval [CI]=1.02-1.43) for incident LVDD compared to those without. The risk of LV diastolic dysfunction increased progressively with increasing degree of hepatic steatosis (P< 0.001). Compared to subjects without NAFLD, the multivariable-aHR (95 % CI) for LVDD in subjects with APRI < 0.5 and APRI ≥ 0.5 were 1.20 (1.01-1.42) and 1.36 (0.90-2.06), respectively (P= 0.036), while other fibrosis prediction models (NFS and FIB-4 index) showed insignificant results. CONCLUSIONS This study demonstrated that NAFLD was associated with an increased risk of LVDD in a large cohort. More severe forms of hepatic steatosis and/or significant liver fibrosis may increase the risk of developing LVDD.
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Affiliation(s)
- Gyuri Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Tae Yang Yu
- Division of Endocrinology and Metabolism, Department of Medicine, Wonkwang Medical Center, Wonkwang University School of Medicine, Iksan, Republic of Korea
| | - Jae Hwan Jee
- Department of Health Promotion Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ji Cheol Bae
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
| | - Mira Kang
- Department of Health Promotion Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Jae Hyeon Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Samsung Biomedical Research Institute, Samsung Medical Center, Seoul, Republic of Korea; Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Republic of Korea.
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [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: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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Fan R, Yu N, Li G, Arshad T, Liu WY, Wong GLH, Liang X, Chen Y, Jin XZ, Leung HHW, Chen J, Wang XD, Yip TCF, Sanyal AJ, Sun J, Wong VWS, Zheng MH, Hou J. Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis. Liver Int 2024; 44:749-759. [PMID: 38131420 DOI: 10.1111/liv.15818] [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: 07/06/2023] [Revised: 11/13/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND & AIMS aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance. METHODS A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125). RESULTS The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively). CONCLUSIONS The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.
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Affiliation(s)
- Rong Fan
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ning Yu
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Guanlin Li
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Tamoore Arshad
- Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia, United States
| | - Wen-Yue Liu
- Department of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Grace Lai-Hung Wong
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Xieer Liang
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yongpeng Chen
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiao-Zhi Jin
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Howard Ho-Wai Leung
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Hong Kong, China
| | - Jinjun Chen
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiao-Dong Wang
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
| | - Terry Cheuk-Fung Yip
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Arun J Sanyal
- Division of Gastroenterology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jian Sun
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Vincent Wai-Sun Wong
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
| | - Jinlin Hou
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Xie G, Xiao H, Liu Q, Chen T, Chen F, Zhou K, Wang X, Liu P, Jia Z, Chen L, Deng X, Meng F, Zhang Z, Chi X, Jia W. LiveBoost: A GB-based prediction system for liver fibrosis in chronic hepatitis B patients in China - A multi-center retrospective study. Heliyon 2024; 10:e24161. [PMID: 38293489 PMCID: PMC10825336 DOI: 10.1016/j.heliyon.2024.e24161] [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: 05/16/2023] [Revised: 11/24/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
Background The aim of this study was to evaluate the accuracy of LiveBoost™, a gradient boosting (GB)-based prediction system based on standard biochemical values (AST, ALT, platelet count) and age, in Chinese patients with chronic hepatitis B (CHB) and compare its performance with FIB-4 (fibrosis-4 score) and APRI (the aspartate transaminase to platelet ratio index). Methods This retrospective trial enrolled 454 participants, including 279 CHB patients who underwent liver biopsy and 175 normal controls from 3 centers in China. All participants underwent laboratory blood testing. LiveBoost was constructed using GB and FIB-4 and APRI were calculated from laboratory data. Results LiveBoost outperformed APRI and FIB-4 in predicting hepatic fibrosis and cirrhosis. The GB model had an AUROC of 0.977 for CHB diagnosis, 0.804 for early and advanced fibrosis, and 0.836 for non-cirrhosis and cirrhosis, compared to AUROC of 0.554, 0.673 and 0.720 for FIB-4, AUROC of 0.977, 0.652 and 0.654 for APRI. Conclusions LiveBoost is a more reliable and cost-effective method than APRI and FIB-4 for assessing liver fibrosis in Chinese patients with CHB.
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Affiliation(s)
- Guoxiang Xie
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Huanming Xiao
- Hepatology Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Quan Liu
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Tianlu Chen
- Center for Translational Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fengyan Chen
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Kejun Zhou
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
| | - Xiaoning Wang
- Institute of Interdisciplinary Integrative Medicine Research, Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Department of Pharmacology, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ping Liu
- Institute of Interdisciplinary Integrative Medicine Research, Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Institute of Liver Diseases, Shuguang Hospital, Department of Pharmacology, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhifeng Jia
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Lei Chen
- Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Xin Deng
- Hepatology Department, The Third People's Hospital of Shenzhen, Shenzhen, China
| | - Fankun Meng
- Ultrasound and Functional Diagnosis Center, Beijing Youan Hospital affiliated to Capital Medical University, Beijing, China
| | - Zhenhua Zhang
- Department of Infectious Diseases and Institute of Clinical Virology, The Second Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaoling Chi
- Hepatology Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Wei Jia
- Human Metabolomics Institute Inc., Shenzhen, Guangdong, China
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
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Ajuwon BI, Roper K, Richardson A, Lidbury BA. Routine blood test markers for predicting liver disease post HBV infection: precision pathology and pattern recognition. Diagnosis (Berl) 2023; 10:337-347. [PMID: 37725092 DOI: 10.1515/dx-2023-0078] [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: 06/28/2023] [Accepted: 08/25/2023] [Indexed: 09/21/2023]
Abstract
BACKGROUND Early stages of hepatitis B virus (HBV) infection usually involve inflammation of the liver. Patients with chronic infection have an increased risk of progressive liver fibrosis, cirrhosis, and life-threatening clinical complications of end-stage hepatocellular carcinoma (HCC). CONTENT Early diagnosis of hepatic fibrosis and timely clinical management are critical to controlling disease progression and decreasing the burden of end-stage liver cancer. Fibrosis staging, through its current gold standard, liver biopsy, improves patient outcomes, but the clinical procedure is invasive with unpleasant post-procedural complications. Routine blood test markers offer promising diagnostic potential for early detection of liver disease without biopsy. There is a plethora of candidate routine blood test markers that have gone through phases of biomarker validation and have shown great promise, but their current limitations include a predictive ability that is limited to only a few stages of fibrosis. However, the advent of machine learning, notably pattern recognition, presents an opportunity to refine blood-based non-invasive models of hepatic fibrosis in the future. SUMMARY In this review, we highlight the current landscape of routine blood-based non-invasive models of hepatic fibrosis, and appraise the potential application of machine learning (pattern recognition) algorithms to refining these models and optimising clinical predictions of HBV-associated liver disease. OUTLOOK Machine learning via pattern recognition algorithms takes data analytics to a new realm, and offers the opportunity for enhanced multi-marker fibrosis stage prediction using pathology profile that leverages information across patient routine blood tests.
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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 Microbiology, Faculty of Pure and Applied Sciences, Kwara State University, Malete, Nigeria
| | - Katrina Roper
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Australian Capital Territory, Australia
| | - Alice Richardson
- Statistical Support Network, The Australian National University, Acton, Australian Capital Territory, Australia
| | - 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
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Wang Z, Zhang A, Yin Y, Tian J, Wang X, Yue Z, Pei L, Qin L, Jia M, Wang H, Cao LL. Clinical prediction of HBV-associated cirrhosis using machine learning based on platelet and bile acids. Clin Chim Acta 2023; 551:117589. [PMID: 37821059 DOI: 10.1016/j.cca.2023.117589] [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: 07/28/2023] [Revised: 09/15/2023] [Accepted: 10/08/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVES The present study was conducted to evaluate the performance of serum bile acids in the prediction of cirrhosis in chronic hepatitis B (CHB) population. METHODS Dysregulated metabolites were explored using untargeted and targeted metabolomic analyses. A machine learning model based on platelet (PLT) and several bile acids was constructed using light gradient boosting machine (LightGBM), to differentiate HBV-associated cirrhosis (BAC) from CHB patients. RESULTS Serum bile acids were dysregulated in BAC compared to CHB patients. The LightGBM model consisted of PLT, TUDCA, UDCA, TLCA, LCA and CA. The model demonstrated a strong discrimination ability in the internal test subset of the training cohort to diagnose BAC from CHB patients (AUC = 0.97). The high diagnostic accuracy of the model was further validated in an independent validation cohort. In addition, the model had high predictive efficacy in discriminating compensated BAC from CHB patients (AUC = 0.89). The performance of the model was better than AST/ALT ratio and the gradient boosting (GB)-based model reported in previous studies. CONCLUSIONS Our study showed that this LightGBM model based on PLT and 5 bile acids has potential in clinical assessments of CHB progression and will be useful for early detection of cirrhosis in CHB patients.
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Affiliation(s)
- Zhenpeng Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Aimin Zhang
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Yue Yin
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Jiashu Tian
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Xialin Wang
- Beckman Coulter Commercial Enterprise Co. Ltd, No.518 Fuquan North Road, Shanghai, China
| | - Zhihong Yue
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Lin Pei
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Li Qin
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Mei Jia
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Hui Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China
| | - Lin-Lin Cao
- Department of Clinical Laboratory, Peking University People's Hospital, Xizhimen South Street No. 11, Beijing 100044, China.
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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.
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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
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10
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Cao J, Cao R, Liu Y, Dai T. CPNE1 mediates glycolysis and metastasis of breast cancer through activation of PI3K/AKT/HIF-1α signaling. Pathol Res Pract 2023; 248:154634. [PMID: 37454492 DOI: 10.1016/j.prp.2023.154634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 05/04/2023] [Accepted: 05/15/2023] [Indexed: 07/18/2023]
Abstract
CPNE1 regulates multiple signaling pathways and can stimulate cell proliferation and differentiation by activating the AKT-mTOR signaling pathway. In addition, CPNE1 is associated with various cancers; however, its role in breast cancer, particularly in TNBC, has not been fully elucidated. Our study aimed to reveal the impact of the CPNE1/PI3K/AKT/HIF-1α axis on TNBC. We first measured the expression of CPNE1 in the tumor tissues of TNBC patients and examined its prognostic value. Subsequently, we used sh-CPNE1 and overexpression vectors to transfect TNBC cell lines and analyzed cell viability, migration, and invasive abilities using colony formation and CCK-8 assays. Metabolites were analyzed through metabolomics. We found that higher expression of CPNE1 predicted poor prognosis in TNBC patients. Knockdown of CPNE1 reduced the viability, migration, invasion, and proliferation capabilities of TNBC cells. Furthermore, metabolomics analysis showed that glucose metabolism was the most dominant pathway, and knockdown of CPNE1 significantly limited the glycolytic activity of TNBC cells. We verified these conclusions in mouse models. Additionally, we overexpressed CPNE1 and treated TNBC cell lines with a PI3K inhibitor (LY294002). The results indicated that CPNE1 promoted aerobic glycolysis in TNBC cells through the PI3K/AKT/HIF-1α signaling pathway. This suggests that CPNE1 regulates cell glycolysis and participates in the development of TNBC. Our study may provide a new therapeutic target for TNBC treatment.
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Affiliation(s)
- Jingying Cao
- Department of Medicine Clinical Laboratory, The Third Xiangya Hospital of Central South University, Changsha 410013, Hunan Province, PR China.
| | - Renxian Cao
- Institute of Clinical Medicine, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, Hunan Province, PR China
| | - Yiqi Liu
- Hengyang Medical School, University of South China, Hengyang 421001, Hunan Province, PR China
| | - Tao Dai
- Department of Urology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan Cancer Hospital, Changsha 410013, Hunan Province, PR China.
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11
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Chang D, Truong E, Mena EA, Pacheco F, Wong M, Guindi M, Todo TT, Noureddin N, Ayoub W, Yang JD, Kim IK, Kohli A, Alkhouri N, Harrison S, Noureddin M. Machine learning models are superior to noninvasive tests in identifying clinically significant stages of NAFLD and NAFLD-related cirrhosis. Hepatology 2023; 77:546-557. [PMID: 35809234 DOI: 10.1002/hep.32655] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 06/28/2022] [Accepted: 07/05/2022] [Indexed: 01/28/2023]
Abstract
BACKGROUND AND AIMS We assessed the performance of machine learning (ML) models in identifying clinically significant NAFLD-associated liver fibrosis and cirrhosis. APPROACH AND RESULTS We implemented ML models including logistic regression (LR), random forest (RF), and artificial neural network to predict histological stages of fibrosis using 17 demographic/clinical features in 1370 patients with NAFLD who underwent liver biopsy, FibroScan, and labs within a 6-month period at multiple U.S. centers. Histological stages of fibrosis (≥F2, ≥F3, and F4) were predicted using ML, FibroScan liver stiffness measurements, and Fibrosis-4 index (FIB-4). NASH with significant fibrosis (NAS ≥ 4 + ≥F2) was assessed using ML, FibroScan-AST (FAST) score, FIB-4, and NAFLD fibrosis score (NFS). We used 80% of the cohort to train and 20% to test the ML models. For ≥F2, ≥F3, F4, and NASH + NAS ≥ 4 + ≥F2, all ML models, especially RF, had primarily higher accuracy and AUC compared with FibroScan, FIB-4, FAST, and NFS. AUC for RF versus FibroScan and FIB-4 for ≥F2, ≥F3, and F4 were (0.86 vs. 0.81, 0.78), (0.89 vs. 0.83, 0.82), and (0.89 vs. 0.86, 0.85), respectively. AUC for RF versus FAST, FIB-4, and NFS for NASH + NAS ≥ 4 + ≥F2 were (0.80 vs. 0.77, 0.66, 0.63). For NASH + NAS ≥ 4 + ≥F2, all ML models had lower/similar percentages within the indeterminate zone compared with FIB-4 and NFS. Overall, ML models performed better in sensitivity, specificity, positive predictive value, and negative predictive value compared with traditional noninvasive tests. CONCLUSIONS ML models performed better overall than FibroScan, FIB-4, FAST, and NFS. ML could be an effective tool for identifying clinically significant liver fibrosis and cirrhosis in patients with NAFLD.
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Affiliation(s)
- Devon Chang
- Arnold O. Beckman High School , Irvine , California , USA
| | - Emily Truong
- Department of Medicine , Cedars Sinai Medical Center , Los Angeles , California , USA
| | - Edward A Mena
- California Liver Institute , Pasadena , California , USA
| | | | - Micaela Wong
- California Liver Institute , Pasadena , California , USA
| | - Maha Guindi
- Department of Pathology , Cedars-Sinai Medical Center , Los Angeles , California , USA
| | - Tsuyoshi T Todo
- Comprehensive Transplant Center , Cedars-Sinai Medical Center , Los Angeles , California , USA
| | - Nabil Noureddin
- Division of Gastroenterology , University of California at San Diego , La Jolla , California , USA
| | - Walid Ayoub
- Department of Medicine , Cedars Sinai Medical Center , Los Angeles , California , USA.,Comprehensive Transplant Center , Cedars-Sinai Medical Center , Los Angeles , California , USA.,Karsh Division of Gastroenterology and Hepatology , Cedars-Sinai Medical Center , Los Angeles , California , USA
| | - Ju Dong Yang
- Department of Medicine , Cedars Sinai Medical Center , Los Angeles , California , USA.,Comprehensive Transplant Center , Cedars-Sinai Medical Center , Los Angeles , California , USA.,Karsh Division of Gastroenterology and Hepatology , Cedars-Sinai Medical Center , Los Angeles , California , USA
| | - Irene K Kim
- Comprehensive Transplant Center , Cedars-Sinai Medical Center , Los Angeles , California , USA
| | - Anita Kohli
- Arizona Liver Health , Phoenix , Arizona , USA
| | | | - Stephen Harrison
- Oxford University, Pinnacle Research Center , Live Oak , Texas , USA
| | - Mazen Noureddin
- Department of Medicine , Cedars Sinai Medical Center , Los Angeles , California , USA.,Comprehensive Transplant Center , Cedars-Sinai Medical Center , Los Angeles , California , USA.,Karsh Division of Gastroenterology and Hepatology , Cedars-Sinai Medical Center , Los Angeles , California , USA
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12
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Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023; 13:149-161. [PMID: 36647407 PMCID: PMC9840075 DOI: 10.1016/j.jceh.2022.06.009] [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: 01/19/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.
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Key Words
- ACLF, acute on chronic liver failure
- AI, artificial intelligence
- ALD, alcoholic liver disease
- ALT, alanine transaminase
- ANN, artificial neural network
- AST, aspartate aminotransferase
- AUD, alcohol use disorder
- CHB, chronic hepatitis B
- CHC, chronic hepatitis C
- CLD, chronic liver disease
- CNN, convolutional neural network
- DL, deep learning
- FIB-4, fibrosis-4 score
- GGTP, gamma glutamyl transferase
- HCC, hepatocellular carcinoma
- HDL, high density lipoprotein
- ML, machine learning
- MLR, multi-nomial logistic regressions
- NAFLD
- NAFLD, non-alcoholic fatty liver disease
- NASH, non-alcoholic steatohepatitis
- NLP, natural language processing
- RF, random forest
- RTE, real-time tissue elastography
- SOLs, space-occupying lesions
- SVM, support vector machine
- artificial intelligence
- deep learning
- hepatology
- machine learning
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Affiliation(s)
- Rakesh Kalapala
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | - Hardik Rughwani
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
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13
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Xie D, Ying M, Lian J, Li X, Liu F, Yu X, Ni C. Serological indices and ultrasound variables in predicting the staging of hepatitis B liver fibrosis: A comparative study based on random forest algorithm and traditional methods. J Cancer Res Ther 2022; 18:2049-2057. [PMID: 36647969 DOI: 10.4103/jcrt.jcrt_1394_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Objective To compare the diagnostic efficacy of serological indices and ultrasound (US) variables in hepatitis B virus (HBV) liver fibrosis staging using random forest algorithm (RFA) and traditional methods. Methods The demographic and serological indices and US variables of patients with HBV liver fibrosis were retrospectively collected and divided into serology group, US group, and serology + US group according to the research content. RFA was used for training and validation. The diagnostic efficacy was compared to logistic regression analysis (LRA) and APRI and FIB-4 indices. Results For the serology group, the diagnostic performance of RFA was significantly higher than that of APRI and FIB-4 indices. The diagnostic accuracy of RFA in the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 79.17%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.99%, 90.69%, and 92.40%, respectively. The area under the curve (AUC) values were 0.945, 0.959, and 0.951, respectively. For the US group, there was no significant difference in diagnostic performance between RFA and LRA. The diagnostic performance of RFA in the serology + US group was significantly better than that of LRA. The diagnostic accuracy of the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 77.21%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.50%, 90.93%, and 93.38%, respectively. The AUC values were 0.948, 0.959, and 0.962, respectively. Conclusion RFA can significantly improve the diagnostic performance of HBV liver fibrosis staging. RFA based on serological indices has a good ability to predict liver fibrosis staging. RFA can help clinicians accurately judge liver fibrosis staging and reduce unnecessary biopsies.
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Affiliation(s)
- Daolin Xie
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou; Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Minghua Ying
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jingru Lian
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin Li
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Fangyi Liu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoling Yu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Caifang Ni
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
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14
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Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28:6230-6248. [PMID: 36504554 PMCID: PMC9730439 DOI: 10.3748/wjg.v28.i44.6230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/07/2022] [Accepted: 11/17/2022] [Indexed: 11/25/2022] Open
Abstract
The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.
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Affiliation(s)
- J Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Marta Alonso-Bernáldez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | | | - Juan A Vargas-Nuñez
- Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain
| | - Ana Ramírez de Molina
- Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Alberto Dávalos
- Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico
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15
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Registered Trials of Artificial Intelligence Conducted on Chronic Liver Disease: A Cross-Sectional Study on ClinicalTrials.gov. DISEASE MARKERS 2022; 2022:6847073. [PMID: 36193490 PMCID: PMC9526577 DOI: 10.1155/2022/6847073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 11/24/2022]
Abstract
Background Artificial intelligence (AI) has been widely applied in the diagnosis and therapy of chronic liver disease (CLD), but there is currently little insight into the trials registered on ClinicalTrials.gov. Thus, this cross-sectional study was focused on analyzing the progress in the use of AI in CLD. Methods Registered trials of AI applied in CLD on ClinicalTrials.gov were searched firstly. All available information was downloaded to Excel (Microsoft Excel, Rong, Rong, China), and duplicates were removed. We extracted the data of the included trials, then analyzed the characteristics of them finally. Results Up to the 27th of May 2021, 6835 trials were identified following an initial search, and 20 registered trials were included after screening for inclusion and exclusion criteria. Among those trials, hepatocellular carcinoma (HCC, 40.0%) and nonalcoholic fatty liver disease (NAFLD, 20.0%) were the most widely applied CLDs for AI. Trials started in 2013 until 2021, with 17 trials (85%) registered after 2016. There was a large trend in trial enrolment, with 40% of them including samples more than 500. Five trials (25%) have been completed, but only one of these had available results. The most frequent sponsors and collaborators were both hospitals at 55%, followed by universities at 35% and institutes at 11%, respectively. Of the 20 trials included, 35% (7 trials) were interventional trials and 65% (13 trials) were observational trials. Among 7 interventional trials, most trials were for diagnosis purpose (42.86%, 3 trials); 4 trials (57.14%) were randomized; 3 trials (42.86%) applied behavioral intervention, 1 trial (14.29%) was in device intervention, 2 trials (28.57%) were in diagnostic test, and 1 trial intervention was unknown. Among 13 observational trials, 8 (61.54%) were cohort studies; 6 (46.15%) were prospective studies, 4 (30.77%) were retrospective studies, 2 (15.38%) were cross-sectional studies, and 1 (7.69%) did not involve a temporal perspective. Conclusion The study is the first to focus on AI registration trials in CLD, which will aid relevant scholars in understanding the current state of the subject. This study demonstrates that additional research on AI used in the diagnosis and treatment of CLD is required, and timely publication of accessible results from registered trials is essential.
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16
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Li H, Bhatt M, Qu Z, Zhang S, Hartel MC, Khademhosseini A, Cloutier G. Deep learning in ultrasound elastography imaging: A review. Med Phys 2022; 49:5993-6018. [PMID: 35842833 DOI: 10.1002/mp.15856] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 02/04/2022] [Accepted: 07/06/2022] [Indexed: 11/11/2022] Open
Abstract
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hongliang Li
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada
| | - Manish Bhatt
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Zhen Qu
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Shiming Zhang
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Martin C Hartel
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Ali Khademhosseini
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Guy Cloutier
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada.,Department of Radiology, Radio-Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada
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17
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Lui TKL, Cheung KS, Leung WK. Machine learning models in the prediction of 1-year mortality in patients with advanced hepatocellular cancer on immunotherapy: a proof-of-concept study. Hepatol Int 2022; 16:879-891. [PMID: 35779202 DOI: 10.1007/s12072-022-10370-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 05/22/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. This study aimed to evaluate the role of machine learning (ML) models in predicting the 1-year cancer-related mortality in advanced HCC patients treated with immunotherapy. METHOD 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) between 2014 and 2019 in Hong Kong were included. The whole data sets were randomly divided into training (n = 316) and internal validation (n = 79) set. The data set, including 47 clinical variables, was used to construct six different ML models in predicting the risk of 1-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and their performances were compared with C-Reactive protein and Alpha Fetoprotein in ImmunoTherapY score (CRAFITY) and albumin-bilirubin (ALBI) score. The ML models were further validated with an external cohort between 2020 and 2021. RESULTS The 1-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.92 (95% CI 0.87-0.98), which was better than logistic regression (0.82, p = 0.01) as well as the CRAFITY (0.68, p < 0.01) and ALBI score (0.84, p = 0.04). RF had the lowest false positive (2.0%) and false negative rate (5.2%), and performed better than CRAFITY score in the external validation cohort (0.91 vs 0.66, p < 0.01). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models. CONCLUSION ML models could predict 1-year cancer-related mortality in HCC patients treated with immunotherapy, which may help to select patients who would benefit from this treatment.
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Affiliation(s)
- Thomas Ka Luen Lui
- Department of Medicine, University of Hong Kong, 4/F, Professorial Block, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong, China
| | - Ka Shing Cheung
- Department of Medicine, University of Hong Kong, 4/F, Professorial Block, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong, China
| | - Wai Keung Leung
- Department of Medicine, University of Hong Kong, 4/F, Professorial Block, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong, China.
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18
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Edeh MO, Dalal S, Dhaou IB, Agubosim CC, Umoke CC, Richard-Nnabu NE, Dahiya N. Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease. Front Public Health 2022; 10:892371. [PMID: 35570979 PMCID: PMC9092454 DOI: 10.3389/fpubh.2022.892371] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 12/15/2022] Open
Abstract
Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.
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Affiliation(s)
- Michael Onyema Edeh
- Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria
| | - Surjeet Dalal
- College of Computing Science & Information Technology, Teerthanker Mahaveer University, Moradabad, India
| | - Imed Ben Dhaou
- Department of Computer Science, Hekma School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah, Saudi Arabia
| | | | - Chukwudum Collins Umoke
- Department of Vocational and Technical Education, Alex Ekwueme Federal University Ndufu Alike Ikwo (AE- FUNAI), Abakaliki, Nigeria
| | - Nneka Ernestina Richard-Nnabu
- Department of Computer Science/Informatics, Alex Ekwueme Federal University Ndufu Alike Ikwo (AE-FUNAI), Abakaliki, Nigeria
| | - Neeraj Dahiya
- Department of Computer Science and Engineering, SRM University Delhi-NCR, Sonipat, India
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19
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Sarvestany SS, Kwong JC, Azhie A, Dong V, Cerocchi O, Ali AF, Karnam RS, Kuriry H, Shengir M, Candido E, Duchen R, Sebastiani G, Patel K, Goldenberg A, Bhat M. Development and validation of an ensemble machine learning framework for detection of all-cause advanced hepatic fibrosis: a retrospective cohort study. Lancet Digit Health 2022; 4:e188-e199. [DOI: 10.1016/s2589-7500(21)00270-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/31/2021] [Accepted: 11/23/2021] [Indexed: 02/08/2023]
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20
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Wu T, Simonetto DA, Halamka JD, Shah VH. The digital transformation of hepatology: The patient is logged in. Hepatology 2022; 75:724-739. [PMID: 35028960 PMCID: PMC9531185 DOI: 10.1002/hep.32329] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/14/2022]
Abstract
The rise in innovative digital health technologies has led a paradigm shift in health care toward personalized, patient-centric medicine that is reaching beyond traditional brick-and-mortar facilities into patients' homes and everyday lives. Digital solutions can monitor and detect early changes in physiological data, predict disease progression and health-related outcomes based on individual risk factors, and manage disease intervention with a range of accessible telemedicine and mobile health options. In this review, we discuss the unique transformation underway in the care of patients with liver disease, specifically examining the digital transformation of diagnostics, prediction and clinical decision-making, and management. Additionally, we discuss the general considerations needed to confirm validity and oversight of new technologies, usability and acceptability of digital solutions, and equity and inclusivity of vulnerable populations.
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Affiliation(s)
- Tiffany Wu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A. Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - John D. Halamka
- Mayo Clinic Platform, Mayo Clinic, Rochester, Minnesota, USA
| | - Vijay H. Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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21
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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22
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Gao B, Wu TC, Lang S, Jiang L, Duan Y, Fouts DE, Zhang X, Tu XM, Schnabl B. Machine Learning Applied to Omics Datasets Predicts Mortality in Patients with Alcoholic Hepatitis. Metabolites 2022; 12:41. [PMID: 35050163 PMCID: PMC8781791 DOI: 10.3390/metabo12010041] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/31/2021] [Accepted: 01/01/2022] [Indexed: 12/14/2022] Open
Abstract
Alcoholic hepatitis is a major health care burden in the United States due to significant morbidity and mortality. Early identification of patients with alcoholic hepatitis at greatest risk of death is extremely important for proper treatments and interventions to be instituted. In this study, we used gradient boosting, random forest, support vector machine and logistic regression analysis of laboratory parameters, fecal bacterial microbiota, fecal mycobiota, fecal virome, serum metabolome and serum lipidome to predict mortality in patients with alcoholic hepatitis. Gradient boosting achieved the highest AUC of 0.87 for both 30-day mortality prediction using the bacteria and metabolic pathways dataset and 90-day mortality prediction using the fungi dataset, which showed better performance than the currently used model for end-stage liver disease (MELD) score.
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Affiliation(s)
- Bei Gao
- School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China;
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; (S.L.); (L.J.); (Y.D.)
| | - Tsung-Chin Wu
- Department of Mathematics, University of California San Diego, San Diego, CA 92093, USA;
- Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health, University of California San Diego, San Diego, CA 92093, USA; (X.Z.); (X.-M.T.)
| | - Sonja Lang
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; (S.L.); (L.J.); (Y.D.)
| | - Lu Jiang
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; (S.L.); (L.J.); (Y.D.)
- Department of Medicine, VA San Diego Healthcare System, San Diego, CA 92161, USA
| | - Yi Duan
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; (S.L.); (L.J.); (Y.D.)
| | | | - Xinlian Zhang
- Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health, University of California San Diego, San Diego, CA 92093, USA; (X.Z.); (X.-M.T.)
| | - Xin-Ming Tu
- Division of Biostatistics and Bioinformatics, Herbert Wertheim School of Public Health, University of California San Diego, San Diego, CA 92093, USA; (X.Z.); (X.-M.T.)
| | - Bernd Schnabl
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; (S.L.); (L.J.); (Y.D.)
- Department of Medicine, VA San Diego Healthcare System, San Diego, CA 92161, USA
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23
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Chen T, Zhou K, Sun T, Sang C, Jia W, Xie G. Altered bile acid glycine : taurine ratio in the progression of chronic liver disease. J Gastroenterol Hepatol 2022; 37:208-215. [PMID: 34655465 DOI: 10.1111/jgh.15709] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND AIM The onset and progression of chronic liver disease (CLD) is a multistage process spanning years or several decades. Some bile acid (BA) features are identified as indicators for CLD progression. However, BAs are highly influenced by various factors and are stage and/or population specific. Emerging evidences demonstrated the association of structure of conjugated BAs and CLD progression. Here, we aimed to investigate the alteration of conjugated BAs and identify new features for CLD progression. METHODS Based on liquid chromatography-mass spectrometry platform, 15 BAs were quantified in 1883 participants including healthy controls and CLD patients (non-alcoholic fatty liver [NAFL], non-alcoholic steatohepatitis [NASH], fibrosis, cirrhosis, and three types of liver cancer). Logistic regression was used to construct diagnostic models. Model performances were evaluated in discovery and test sets by area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and kappa index. RESULTS Five BA glycine : taurine ratios were calculated, and glycocholic acid/taurocholic acid, glycodeoxycholic acid/taurodeoxycholic acid, and glycochenodeoxycholic acid/taurochenocholic acid were identified as candidates. Three diagnostic models were constructed for the differentiation of healthy control and early CLD (NAFL + NASH), early and advanced CLD (fibrosis + cirrhosis + liver cancer), and NAFL and NASH, respectively. The areas under the receiver operating characteristic curve of the models ranged from 0.91 to 0.97. The addition of age and gender improved model performances further. The alterations of the candidates and the performances of the diagnostic models were successfully validated by independent test sets (n = 291). CONCLUSIONS Our findings revealed stage-specific BA perturbation patterns and provided new biomarkers and tools for the monitoring of liver disease progression.
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Affiliation(s)
- Tianlu Chen
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Kejun Zhou
- Human Metabolomics Institute, Inc., Shenzhen, Guangdong, China
| | - Tao Sun
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Chao Sang
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Wei Jia
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Hong Kong Traditional Chinese Medicine Phenome Research Centre, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Guoxiang Xie
- Human Metabolomics Institute, Inc., Shenzhen, Guangdong, China
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24
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Singh D, Prashar D, Singla J, Ahmad Khan A, Al-Sarem M, Ali Kurdi N. Intelligent Medical Diagnostic System for Hepatitis B. COMPUTERS, MATERIALS & CONTINUA 2022; 73:6047-6068. [DOI: 10.32604/cmc.2022.031255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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25
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Li R, Li L, Xu Y, Yang J. Machine learning meets omics: applications and perspectives. Brief Bioinform 2021; 23:6425809. [PMID: 34791021 DOI: 10.1093/bib/bbab460] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/29/2021] [Accepted: 10/07/2021] [Indexed: 02/07/2023] Open
Abstract
The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.
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Affiliation(s)
- Rufeng Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Lixin Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Yungang Xu
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Juan Yang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China.,Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education of China, Xi'an 710061, P. R. China
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26
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Philips CA, Ahamed R, Abduljaleel JK, Rajesh S, Augustine P. Critical Updates on Chronic Hepatitis B Virus Infection in 2021. Cureus 2021; 13:e19152. [PMID: 34733599 PMCID: PMC8557099 DOI: 10.7759/cureus.19152] [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] [Subscribe] [Scholar Register] [Accepted: 10/30/2021] [Indexed: 02/06/2023] Open
Abstract
Chronic hepatitis B virus (HBV) infection is a global healthcare burden in the form of chronic liver disease, cirrhosis, liver failure and liver cancer. There is no definite cure for the virus and even though extensive vaccination programs have reduced the burden of liver disease in the future population, treatment options to eradicate the virus from the host are still lacking. In this review, we discuss in detail current updates on the structure and applied biology of the virus in the host, examine updates to current treatment and explore novel and state-of-the-art therapeutics in the pipeline for management of chronic HBV. Furthermore, we also specifically review clinical updates on HBV-related acute on chronic liver failure (ACLF). Current treatments for chronic HBV infection have seen important updates in the form of considerations for treating patients in the immune tolerant phase and some clarity on end points for treatment and decisions on finite therapy with nucleos(t)ide inhibitors. Ongoing cutting-edge research on HBV biology has helped us identify novel target areas in the life cycle of the virus for application of new therapeutics. Due to improvements in the area of genomics, the hope for therapeutic vaccines, vector-based treatments and focused management aimed at targeting host integration of the virus and thereby a total cure could become a reality in the near future. Newer clinical prognostic tools have improved our understanding of timing of specific treatment options for the catastrophic syndrome of ACLF secondary to reactivation of HBV. In this review, we discuss in detail pertinent updates regarding virus biology and novel therapeutic targets with special focus on the appraisal of prognostic scores and treatment options in HBV-related ACLF.
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Affiliation(s)
- Cyriac A Philips
- Clinical and Translational Hepatology, The Liver Institute, Rajagiri Hospital, Aluva, IND
| | - Rizwan Ahamed
- Gastroenterology and Advanced Gastrointestinal Endoscopy, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
| | - Jinsha K Abduljaleel
- Gastroenterology and Advanced Gastrointestinal Endoscopy, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
| | - Sasidharan Rajesh
- Diagnostic and Interventional Radiology, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
| | - Philip Augustine
- Gastroenterology and Advanced Gastrointestinal Endoscopy, Center of Excellence in Gastrointestinal Sciences, Rajagiri Hospital, Aluva, IND
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27
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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28
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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29
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021; 29:451-463. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [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] [Indexed: 11/17/2022]
Abstract
Here we review artificial intelligence (AI) models which aim to assess various aspects of chronic liver disease. Despite the clinical importance of hepatocellular carcinoma in the setting of chronic liver disease, we focus this review on AI models which are not lesion-specific and instead review models developed for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. Optimization of these models offers the opportunity to potentially reduce the need for invasive procedures such as catheterization to measure hepatic venous pressure gradient or biopsy to assess fibrosis and steatosis. We compare the performance of these AI models amongst themselves as well as to radiomics approaches and alternate modality assessments. We conclude that these models show promising performance and merit larger-scale evaluation. We review artificial intelligence models that aim to assess various aspects of chronic liver disease aside from hepatocellular carcinoma. We focus this review on models for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. We conclude that these models show promising performance and merit a larger scale evaluation.
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Affiliation(s)
- Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, 1501 N. Campbell, P.O. Box 245067, Tucson, AZ 85724-5067, USA.
| | - Bradley M Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, 1542 Tulane Avenue, Rm 343, New Orleans, LA 70112, USA
| | - Ahmed W Moawad
- Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Unit 1472, P.O. Box 301402, Houston, TX 77230-1402, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas, MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA
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Fu Q, Agarwal D, Deng K, Matheson R, Yang H, Wei L, Ran Q, Deng S, Markmann JF. An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation. Front Immunol 2021; 12:695806. [PMID: 34305931 PMCID: PMC8297499 DOI: 10.3389/fimmu.2021.695806] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/18/2021] [Indexed: 12/14/2022] Open
Abstract
Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.
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Affiliation(s)
- Qiang Fu
- Organ Transplantation Center, Sichuan Provincial People's Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Center for Transplantation Sciences, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Divyansh Agarwal
- Division of Transplantation, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, United States
| | - Kevin Deng
- Center for Transplantation Sciences, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Rudy Matheson
- Center for Transplantation Sciences, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Hongji Yang
- Organ Transplantation Center, Sichuan Provincial People's Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Wei
- Organ Transplantation Center, Sichuan Provincial People's Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Ran
- Organ Transplantation Center, Sichuan Provincial People's Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shaoping Deng
- Organ Transplantation Center, Sichuan Provincial People's Hospital and School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - James F Markmann
- Center for Transplantation Sciences, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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Li X, Yang L, Yuan Z, Lou J, Fan Y, Shi A, Huang J, Zhao M, Wu Y. Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection. J Transl Med 2021; 19:281. [PMID: 34193166 PMCID: PMC8243478 DOI: 10.1186/s12967-021-02955-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/19/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters. METHODS Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches. RESULTS Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk. CONCLUSIONS By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.
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Affiliation(s)
- Xiawei Li
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Litao Yang
- Department of Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310000, Zhejiang, China
| | - Zheping Yuan
- Hessian Health Technology Co., Ltd, Beijing, 100007, China
| | - Jianyao Lou
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Yiqun Fan
- Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, Zhejiang, China
| | - Aiguang Shi
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Junjie Huang
- Department of Surgery, Changxing People's Hospital, Huzhou, 313100, Zhejiang, China
| | - Mingchen Zhao
- Hessian Health Technology Co., Ltd, Beijing, 100007, China
| | - Yulian Wu
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China.
- Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang, China.
- Cancer Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021; 73:2546-2563. [PMID: 33098140 DOI: 10.1002/hep.31603] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/15/2020] [Accepted: 09/29/2020] [Indexed: 12/11/2022]
Abstract
Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.
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Affiliation(s)
- Joseph C Ahn
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | | | | | | | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
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Muhlestein WE, Monsour MA, Friedman GN, Zinzuwadia A, Zachariah MA, Coumans JV, Carter BS, Chambless LB. Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model. Neurosurgery 2021; 88:838-845. [PMID: 33483747 DOI: 10.1093/neuros/nyaa585] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 10/10/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructured text. OBJECTIVE To present an NLP model that predicts nonhome discharge and a point-of-care implementation. METHODS We retrospectively collected age, preoperative notes, and radiology reports from 595 adults who underwent meningioma resection in an academic center from 1995 to 2015. A total of 32 algorithms were trained with the data; the 3 best performing algorithms were combined to form an ensemble. Predictive ability, assessed by area under the receiver operating characteristic curve (AUC) and calibration, was compared to a previously published model utilizing 52 neurosurgeon-selected variables. We then built a multi-institutional model by incorporating notes from 693 patients at another center into algorithm training. Permutation importance was used to analyze the relative importance of each input to model performance. Word clouds and non-negative matrix factorization were used to analyze predictive features of text. RESULTS The single-institution NLP model predicted nonhome discharge with AUC of 0.80 (95% CI = 0.74-0.86) on internal and 0.76 on holdout validation compared to AUC of 0.77 (95% CI = 0.73-0.81) and 0.74 for the 52-variable ensemble. The multi-institutional model performed similarly well with AUC = 0.78 (95% CI = 0.74-0.81) on internal and 0.76 on holdout validation. Preoperative notes most influenced predictions. The model is available at http://nlp-home.insds.org. CONCLUSION ML and NLP are underutilized in neurosurgery. Here, we construct a multi-institutional NLP model that predicts nonhome discharge.
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Affiliation(s)
- Whitney E Muhlestein
- Department of Neurosurgery, University of Michigan Medical Center, Ann Arbor, Michigan
| | | | - Gabriel N Friedman
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Marcus A Zachariah
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean-Valery Coumans
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bob S Carter
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, Tennessee
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Hashem A, Awad A, Shousha H, Alakel W, Salama A, Awad T, Mabrouk M. Validation of a machine learning approach using FIB-4 and APRI scores assessed by the metavir scoring system: A cohort study. Arab J Gastroenterol 2021; 22:88-92. [PMID: 33985905 DOI: 10.1016/j.ajg.2021.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 02/01/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND STUDY AIM The study aim was to improve and validate the accuracy of the fibrosis-4 (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI) scores for use in a potential machine-learning (ML) method that accurately predicts the extent of liver fibrosis. PATIENTS AND METHODS This retrospective multicenter study included 69,106 patients with chronic hepatitis C planned for antiviral therapy from January 2010-December 2014 with liver biopsy results. FIB-4 and APRI scores were calculated and their performance for predicting significant liver fibrosis (F3-F4) assessed against the Metavir scoring system. ML was used for feature selection and reduction to identify the most relevant attributes (CfsSubseteval/best first) for prediction. RESULTS In this study, 57,492 (83.2%) patients were F0-F2, and 11,615 (16.8%) patients were F3-F4. The revalidation of FIB-4 and APRI showed lower accuracy and higher disagreement with the biopsy results, with AUCs of 0.68 and 0.58, respectively. FIB-4 diagnosed fewer (14%) F3-F4 patients, and the high specificity and negative predictive values of FIB-4 and APRI reflected the low prevalence of F3-F4 in the study population. Out of 15 attributes, age (>35 years), AFP (>6.5 ng/ml), and platelet count (<150,000/mm3) were the most relevant risk attributes, and patients with one or more of these risk factors were likely to be F3-F4, with a classification accuracy of ≤ 92% and receiver operating characteristics area of 0.74. CONCLUSION FIB-4 and APRI scores were not very accurate and missed diagnosing most of the F3-F4 patients. ML implementation improved medical decisions and minimized the required clinical data to three risk factors.
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Affiliation(s)
- Ahmed Hashem
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Abubakr Awad
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, UK
| | - Hend Shousha
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt.
| | - Wafaa Alakel
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt; National Hepatology and Tropical Medicine Research Institute, Ministry of Health and Population, Cairo, Egypt
| | - Ahmed Salama
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Tahany Awad
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mahasen Mabrouk
- Endemic Medicine and Hepatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
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Kumar R, Khan FU, Sharma A, Aziz IB, Poddar NK. Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases. Curr Med Chem 2021; 29:66-85. [PMID: 33820515 DOI: 10.2174/0929867328666210405114938] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/25/2021] [Accepted: 03/06/2021] [Indexed: 11/22/2022]
Abstract
There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh. India
| | - Farhat Ullah Khan
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh. India
| | - Izzatdin Ba Aziz
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
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Sang C, Wang X, Zhou K, Sun T, Bian H, Gao X, Wang Y, Zhang H, Jia W, Liu P, Xie G, Chen T. Bile Acid Profiles Are Distinct among Patients with Different Etiologies of Chronic Liver Disease. J Proteome Res 2021; 20:2340-2351. [PMID: 33754726 DOI: 10.1021/acs.jproteome.0c00852] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A significant increase of bile acid (BA) levels has been recognized as a general metabolic phenotype of diverse liver diseases. Monitoring of BA profiles has been proposed for etiology differentiation on liver injury. Here, we quantitatively profiled serum BAs of healthy controls and 719 patients with chronic liver disease of five etiologies, hepatitis B virus (HBV), hepatitis C virus (HCV), nonalcoholic steatohepatitis (NASH), alcohol-induced liver disease (ALD), and primary biliary cirrhosis (PBC), and investigated the generality and specificity of different etiologies. The raw data have been deposited into MetaboLights (ID: MTBLS2459). We found that patients with HBV, HCV, and NASH appeared to be more similar, and ALD and PBC patients clustered together. BA profiles, consisting of a total concentration of the 21 quantified BAs [total BAs (TBAs)], 21 BA proportions, and 24 BA relevant variables, were highly different among the etiologies. Specifically, the total BAs was higher in ALD and PBC patients compared with the other three groups. The proportion of conjugated deoxycholates was the highest in HBV-infected patients. The ratio of 12α-hydroxylated (12α-OH) to non-12α-OH BAs was the highest in NASH patients. The proportion of taurine-conjugated BAs was the highest in ALD patients. For PBC patients, the proportion of ursodeoxycholate species was the highest, and the ratio of primary to secondary BAs was the lowest. Comparatively, the difference of BA profiles among cirrhosis patients was consistent but weaker than that of all patients. The correlations between BA profiles and clinical indices were also quite different in different pathological groups, both in all patients and in patients with cirrhosis. Overall, our findings suggested that BA compositions are distinct among patients with different etiologies of chronic liver disease, and some BA-relevant variables are of clinical potentials for liver injury type differentiation, although further validations on more etiologies and populations are needed.
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Affiliation(s)
- Chao Sang
- Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Xiaoning Wang
- E-institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Kejun Zhou
- Human Metabolomics Institute, Inc., Shenzhen 518109, Guangdong, China
| | - Tao Sun
- Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Hua Bian
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xin Gao
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yixing Wang
- E-institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Hua Zhang
- E-institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Wei Jia
- Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China.,Hong Kong Traditional Chinese Medicine Phenome Research Centre, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong 999077, Hong Kong, China
| | - Ping Liu
- E-institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Guoxiang Xie
- Human Metabolomics Institute, Inc., Shenzhen 518109, Guangdong, China
| | - Tianlu Chen
- Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
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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.
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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
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Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease. J Gastroenterol Hepatol 2021; 36:539-542. [PMID: 33709605 DOI: 10.1111/jgh.15409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/16/2021] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science that attempts to mimic human intelligence, such as learning and problem-solving skills. The use of AI in hepatology occurred later than in gastroenterology. Nevertheless, studies on applying AI to liver disease have recently increased. AI in hepatology can be applied for detecting liver fibrosis, differentiating focal liver lesions, predicting prognosis of chronic liver disease, and diagnosing of nonalcoholic fatty liver disease. We expect that AI will eventually help manage patients with liver disease, predict the clinical outcomes, and reduce medical errors. However, there are several hurdles that need to be overcome. Here, we will briefly review the areas of liver disease to which AI can be applied.
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Affiliation(s)
- Hye Won Lee
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea.,Yonsei Liver Center, Severance Hospital, Seoul, South Korea
| | - Joseph J Y Sung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Sang Hoon Ahn
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, South Korea.,Yonsei Liver Center, Severance Hospital, Seoul, South Korea
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40
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Sang C, Yan H, Chan WK, Zhu X, Sun T, Chang X, Xia M, Sun X, Hu X, Gao X, Jia W, Bian H, Chen T, Xie G. Diagnosis of Fibrosis Using Blood Markers and Logistic Regression in Southeast Asian Patients With Non-alcoholic Fatty Liver Disease. Front Med (Lausanne) 2021; 8:637652. [PMID: 33708783 PMCID: PMC7940822 DOI: 10.3389/fmed.2021.637652] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 01/22/2021] [Indexed: 12/14/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the main causes of fibrosis. Liver biopsy remains the gold standard for the confirmation of fibrosis in NAFLD patients. Effective and non-invasive diagnosis of advanced fibrosis is essential to disease surveillance and treatment decisions. Herein we used routine medical test markers and logistic regression to differentiate early and advanced fibrosis in NAFLD patients from China, Malaysia, and India (n 1 = 540, n 2 = 147, and n 3 = 97) who were confirmed by liver biopsy. Nine parameters, including age, body mass index, fasting blood glucose, presence of diabetes or impaired fasting glycemia, alanine aminotransferase, γ-glutamyl transferase, triglyceride, and aspartate transaminase/platelet count ratio, were selected by stepwise logistic regression, receiver operating characteristic curve (ROC), and hypothesis testing and were used for model construction. The area under the ROC curve (auROC) of the model was 0.82 for differentiating early and advanced fibrosis (sensitivity = 0.69, when specificity = 0.80) in the discovery set. Its diagnostic ability remained good in the two independent validation sets (auROC = 0.89 and 0.71) and was consistently superior to existing panels such as the FIB-4 and NAFLD fibrosis score. A web-based tool, LiveFbr, was developed for fast access to our model. The new model may serve as an attractive tool for fibrosis classification in NAFLD patients.
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Affiliation(s)
- Chao Sang
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Hongmei Yan
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.,Fudan Institute for Metabolic Diseases, Fudan University, Shanghai, China
| | - Wah Kheong Chan
- Gastroenterology and Hepatology Unit, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Xiaopeng Zhu
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Tao Sun
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xinxia Chang
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mingfeng Xia
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaoyang Sun
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiqi Hu
- Department of Pathology, Medical College, Fudan University, Shanghai, China
| | - Xin Gao
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.,Fudan Institute for Metabolic Diseases, Fudan University, Shanghai, China
| | - Wei Jia
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.,Hong Kong Traditional Chinese Medicine Phenome Research Centre, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Hua Bian
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.,Fudan Institute for Metabolic Diseases, Fudan University, Shanghai, China
| | - Tianlu Chen
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Guoxiang Xie
- Human Metabolomics Institute, Inc., Shenzhen, China
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Kwee SA, Tiirikainen M. Beta-catenin activation and immunotherapy resistance in hepatocellular carcinoma: mechanisms and biomarkers. ACTA ACUST UNITED AC 2021; 7. [PMID: 33553649 PMCID: PMC7861492 DOI: 10.20517/2394-5079.2020.124] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Mutations involving CTNNB1, the gene encoding beta-catenin, and other molecular alterations that affect the Wnt/beta-catenin signaling pathway are exceptionally common in hepatocellular carcinoma. Several of these alterations have also been associated with scarcity of immune cells in the tumor microenvironment and poor clinical response to immune checkpoint inhibitor therapy. In light of these associations, tumor biomarkers of beta-catenin status could have the potential to serve as clinical predictors of immunotherapy outcome. This editorial review article summarizes recent pre-clinical and clinical research pertaining to associations between beta-catenin activation and diminished anti-tumor immunity. Potential non-invasive biomarkers that may provide a window into this oncogenic mechanism of immune evasion are also presented and discussed.
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Affiliation(s)
- Sandi A Kwee
- Cancer Biology Program (SAK) and Population Sciences in the Pacific Program (MT), University of Hawaii Cancer Center, University of Hawaii, Honolulu, Hawaii 96813, USA.,Hamamatsu/Queen's PET Imaging Center, The Queen's Medical Center, Honolulu, Hawaii 96813, USA
| | - Maarit Tiirikainen
- Cancer Biology Program (SAK) and Population Sciences in the Pacific Program (MT), University of Hawaii Cancer Center, University of Hawaii, Honolulu, Hawaii 96813, USA
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Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021; 21:10. [PMID: 33407169 PMCID: PMC7788739 DOI: 10.1186/s12876-020-01585-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/16/2020] [Indexed: 12/12/2022] Open
Abstract
Background The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance. Methods We systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95% CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities, population and AI classifiers were performed. Results We included 19 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography, magnetic resonance imaging and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.71–0.85), 0.89 (0.81–0.94), 0.72 (0.58–0.83), 0.92 (0.88–0.94) and 31.58 (11.84–84.25), respectively, for cirrhosis; 0.86 (0.80–0.90), 0.87 (0.80–0.92), 0.85 (0.75–0.91), 0.88 (0.82–0.92) and 37.79 (16.01–89.19), respectively; for advanced fibrosis; and 0.86 (0.78–0.92), 0.81 (0.77–0.84), 0.88 (0.80–0.93), 0.77 (0.58–0.89) and 26.79 (14.47–49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76–1.00), 0.91 (0.78–0.97), 0.95 (0.87–0.98), 0.93 (0.80–0.98) and 191.52 (38.82–944.81), respectively, for the diagnosis of liver steatosis. Conclusions AI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice. Trial registration: The protocol was registered with PROSPERO (CRD42020183295).
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Affiliation(s)
| | - Roongruedee Chaiteerakij
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama IV Rd., Pathum Wan, Bangkok, 10330, Thailand. .,Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
| | | | - Sombat Treeprasertsuk
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama IV Rd., Pathum Wan, Bangkok, 10330, Thailand
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Function of TREM1 and TREM2 in Liver-Related Diseases. Cells 2020; 9:cells9122626. [PMID: 33297569 PMCID: PMC7762355 DOI: 10.3390/cells9122626] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023] Open
Abstract
TREM1 and TREM2 are members of the triggering receptors expressed on myeloid cells (TREM) family. Both TREM1 and TREM2 are immunoglobulin superfamily receptors. Their main function is to identify foreign antigens and toxic substances, thereby adjusting the inflammatory response. In the liver, TREM1 and TREM2 are expressed on non-parenchymal cells, such as liver sinusoidal endothelial cells, Kupffer cells, and hepatic stellate cells, and cells which infiltrate the liver in response to injury including monocyte-derived macrophages and neutrophils. The function of TREM1 and TREM2 in inflammatory response depends on Toll-like receptor 4. TREM1 mainly augments inflammation during acute inflammation, while TREM2 mainly inhibits chronic inflammation to protect the liver from pathological changes. Chronic inflammation often induces metabolic abnormalities, fibrosis, and tumorigenesis. The above physiological changes lead to liver-related diseases, such as liver injury, nonalcoholic steatohepatitis, hepatic fibrosis, and hepatocellular carcinoma. Here, we review the function of TREM1 and TREM2 in different liver diseases based on inflammation, providing a more comprehensive perspective for the treatment of liver-related diseases.
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Liu K, Qin M, Tao K, Liang Z, Cai F, Zhao L, Peng P, Liu S, Zou J, Huang J. Identification and external validation of the optimal FIB-4 and APRI thresholds for ruling in chronic hepatitis B related liver fibrosis in tertiary care settings. J Clin Lab Anal 2020; 35:e23640. [PMID: 33146916 PMCID: PMC7891512 DOI: 10.1002/jcla.23640] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND With the initially defined thresholds, the most widely used serum biomarkers for staging liver fibrosis (ie, APRI and FIB-4 scores) proved to be ineffective among patients with chronic hepatitis B virus infection (CHB). Whether optimizing the FIB-4 and APRI thresholds could improve their diagnostic accuracy requires further research. METHODS Using data of treat-naïve CHB patients from three tertiary hospitals, we explored the optimal FIB-4 and APRI thresholds to rule in liver fibrosis accurately. Subsequently, we validated the applicability of the newly defined thresholds to the CHB patients from another two tertiary hospitals. RESULTS The fibrosis stages between discovery cohort (n = 433) and the external validation cohort (n = 568) were statistically different (P < .001). When ruling in significant fibrosis and advanced fibrosis by the newly defined FIB-4 thresholds (2.25 and 3.00, respectively), 24.0% and 14.3% of patients, respectively, could be classified with excellent accuracy (PPVs of 91.3% and 80.6%, respectively; misdiagnosis rates of 6.0% and 5.4%, respectively), supported by the internal and external validation tests. Regrettably, the more accurate and robust thresholds of APRI score for ruling in significant fibrosis and advanced fibrosis could not be found. Besides, the FIB-4 and APRI scores should not be recommended for ruling in cirrhosis because of poor clinical diagnostic performance. CONCLUSION The newly defined FIB-4 thresholds for ruling in significant fibrosis and advanced fibrosis showed superior and reproducible clinical diagnostic accuracy. The well-validated threshold (≥2.25) of FIB-4 score could aid in antiviral treatment decisions for treat-naïve adult CHB patients by accurately ruling in significant fibrosis in tertiary care settings.
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Affiliation(s)
- Kecheng Liu
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Mengbin Qin
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Kunlin Tao
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhihai Liang
- Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Fuqing Cai
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Liebin Zhao
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Peng
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shiquan Liu
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jun Zou
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiean Huang
- Department of Gastroenterology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
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Rybicka M, Bielawski KP. Recent Advances in Understanding, Diagnosing, and Treating Hepatitis B Virus Infection. Microorganisms 2020; 8:E1416. [PMID: 32942584 PMCID: PMC7565763 DOI: 10.3390/microorganisms8091416] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 02/06/2023] Open
Abstract
Chronic hepatitis B virus (HBV) infection affects 292 million people worldwide and is associated with a broad range of clinical manifestations including cirrhosis, liver failure, and hepatocellular carcinoma (HCC). Despite the availability of an effective vaccine HBV still causes nearly 900,000 deaths every year. Current treatment options keep HBV under control, but they do not offer a cure as they cannot completely clear HBV from infected hepatocytes. The recent development of reliable cell culture systems allowed for a better understanding of the host and viral mechanisms affecting HBV replication and persistence. Recent advances into the understanding of HBV biology, new potential diagnostic markers of hepatitis B infection, as well as novel antivirals targeting different steps in the HBV replication cycle are summarized in this review article.
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Affiliation(s)
- Magda Rybicka
- Department of Molecular Diagnostics, Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Abrahama 58, 80-307 Gdansk, Poland;
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Xiong Y, Lu H, Xu H. Galangin Reverses Hepatic Fibrosis by Inducing HSCs Apoptosis via the PI3K/Akt, Bax/Bcl-2, and Wnt/β-Catenin Pathway in LX-2 Cells. Biol Pharm Bull 2020; 43:1634-1642. [PMID: 32893252 DOI: 10.1248/bpb.b20-00258] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Hepatic fibrosis (HF) is a common disease, with currently no available treatment. Galangin, a natural flavonoid extracted from Alpinia officinaruim Hance, has multiple effects demonstrated in previous studies. The aim of the present study was to explore the anti-fibrogenic effect of galangin in vitro, and research its potential molecular mechanisms. LX-2 cells were chosen as an in vitro HF model, and were treated with galangin in different concentrations. Cell viability was analyzed using Cell Counting Kit-8 (CCK-8) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, cell apoptosis was measured using flow cytometry, and the anti-fibrogenic effect of galangin was determined using RT-quantitative (q)PCR, immunofluorescence, and Western blotting. The results show that the proliferation of LX-2 cells was efficiently inhibited by galangin, and apoptosis was induced in a dose-dependent manner. Both the mRNA and protein expression of alpha-smooth muscle actin (α-SMA) and collagen I were markedly downregulated. Galangin also inhibited the phosphatidylinositol 3-kinase (PI3K)/Akt and Wnt/β-catenin signaling pathways and increased the Bax/Bcl-2 ratio. The results of this study suggest that galangin has an anti-fibrogenic effect and may represent a promising agent in the treatment of hepatic fibrosis.
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Affiliation(s)
- Yuanguo Xiong
- School of Pharmaceuticals, Hubei University of Chinese Medicine
| | - Hao Lu
- School of Pharmaceuticals, Hubei University of Chinese Medicine
| | - Hanlin Xu
- School of Pharmaceuticals, Hubei University of Chinese Medicine
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Xie G, Wang X, Wei R, Wang J, Zhao A, Chen T, Wang Y, Zhang H, Xiao Z, Liu X, Deng Y, Wong L, Rajani C, Kwee S, Bian H, Gao X, Liu P, Jia W. Serum metabolite profiles are associated with the presence of advanced liver fibrosis in Chinese patients with chronic hepatitis B viral infection. BMC Med 2020; 18:144. [PMID: 32498677 PMCID: PMC7273661 DOI: 10.1186/s12916-020-01595-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 04/16/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Accurate and noninvasive diagnosis and staging of liver fibrosis are essential for effective clinical management of chronic liver disease (CLD). We aimed to identify serum metabolite markers that reliably predict the stage of fibrosis in CLD patients. METHODS We quantitatively profiled serum metabolites of participants in 2 independent cohorts. Based on the metabolomics data from cohort 1 (504 HBV associated liver fibrosis patients and 502 normal controls, NC), we selected a panel of 4 predictive metabolite markers. Consequently, we constructed 3 machine learning models with the 4 metabolite markers using random forest (RF), to differentiate CLD patients from normal controls (NC), to differentiate cirrhosis patients from fibrosis patients, and to differentiate advanced fibrosis from early fibrosis, respectively. RESULTS The panel of 4 metabolite markers consisted of taurocholate, tyrosine, valine, and linoelaidic acid. The RF models of the metabolite panel demonstrated the strongest stratification ability in cohort 1 to diagnose CLD patients from NC (area under the receiver operating characteristic curve (AUROC) = 0.997 and the precision-recall curve (AUPR) = 0.994), to differentiate fibrosis from cirrhosis (0.941, 0.870), and to stage liver fibrosis (0.918, 0.892). The diagnostic accuracy of the models was further validated in an independent cohort 2 consisting of 300 CLD patients with chronic HBV infection and 90 NC. The AUCs of the models were consistently higher than APRI, FIB-4, and AST/ALT ratio, with both greater sensitivity and specificity. CONCLUSIONS Our study showed that this 4-metabolite panel has potential usefulness in clinical assessments of CLD progression in patients with chronic hepatitis B virus infection.
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Affiliation(s)
- Guoxiang Xie
- E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
- Human Metabolomics Institute, Inc., Shenzhen, 518109, Guangdong, China
| | - Xiaoning Wang
- E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
- Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Runmin Wei
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Jingye Wang
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Aihua Zhao
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Tianlu Chen
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Yixing Wang
- Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hua Zhang
- E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
- Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Zhun Xiao
- E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
- Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Xinzhu Liu
- E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
- Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Youping Deng
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Linda Wong
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Cynthia Rajani
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Sandi Kwee
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Hua Bian
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xin Gao
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Ping Liu
- E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
- Key Laboratory of Liver and Kidney Diseases (Ministry of Education), Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
- Institute of Liver Diseases, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 528 Zhangheng Road, Shanghai, 201203, China.
| | - Wei Jia
- E-Institute of Shanghai Municipal Education Committee, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
- University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
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A deep learning approach to evaluate intestinal fibrosis in magnetic resonance imaging models. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04838-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Spann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A, Bhat M. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology 2020; 71:1093-1105. [PMID: 31907954 DOI: 10.1002/hep.31103] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 12/05/2019] [Indexed: 12/13/2022]
Abstract
Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.
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Affiliation(s)
- Ashley Spann
- Division of Gastroenterology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Justin Kang
- Multi Organ Transplant Program, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Kymberly Watt
- Division of Gastroenterology, Mayo Clinic, Rochester, MN
| | - Bo Wang
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Anna Goldenberg
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Mamatha Bhat
- Multi Organ Transplant Program, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.,Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, ON, Canada
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Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020; 158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Citation(s) in RCA: 280] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 02/07/2023]
Abstract
Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
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Affiliation(s)
- Catherine Le Berre
- Institut des Maladies de l'Appareil Digestif, Nantes University Hospital, France; Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France
| | | | - Sabeur Aridhi
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Marie-Dominique Devignes
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Laure Fournier
- Université Paris-Descartes, Institut National de la Santé et de la Recherche Médicale, Unité Mixte De Recherché S970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Malika Smaïl-Tabbone
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Silvio Danese
- Inflammatory Bowel Disease Center and Department of Biomedical Sciences, Humanitas Clinical and Research Center, Humanitas University, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France.
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