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Lin H, Li G, Delamarre A, Ahn SH, Zhang X, Kim BK, Liang LY, Lee HW, Wong GLH, Yuen PC, Chan HLY, Chan SL, Wong VWS, de Lédinghen V, Kim SU, Yip TCF. A Liver Stiffness-Based Etiology-Independent Machine Learning Algorithm to Predict Hepatocellular Carcinoma. Clin Gastroenterol Hepatol 2024; 22:602-610.e7. [PMID: 37993034 DOI: 10.1016/j.cgh.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND & AIMS The existing hepatocellular carcinoma (HCC) risk scores have modest accuracy, and most are specific to chronic hepatitis B infection. In this study, we developed and validated a liver stiffness-based machine learning algorithm (ML) for prediction and risk stratification of HCC in various chronic liver diseases (CLDs). METHODS MLs were trained for prediction of HCC in 5155 adult patients with various CLDs in Korea and further tested in 2 prospective cohorts from Hong Kong (HK) (N = 2732) and Europe (N = 2384). Model performance was assessed according to Harrell's C-index and time-dependent receiver operating characteristic (ROC) curve. RESULTS We developed the SMART-HCC score, a liver stiffness-based ML HCC risk score, with liver stiffness measurement ranked as the most important among 9 clinical features. The Harrell's C-index of the SMART-HCC score in HK and Europe validation cohorts were 0.89 (95% confidence interval, 0.85-0.92) and 0.91 (95% confidence interval, 0.87-0.95), respectively. The area under ROC curves of the SMART-HCC score for HCC in 5 years was ≥0.89 in both validation cohorts. The performance of SMART-HCC score was significantly better than existing HCC risk scores including aMAP score, Toronto HCC risk index, and 7 hepatitis B-related risk scores. Using dual cutoffs of 0.043 and 0.080, the annual HCC incidence was 0.09%-0.11% for low-risk group and 2.54%-4.64% for high-risk group in the HK and Europe validation cohorts. CONCLUSIONS The SMART-HCC score is a useful machine learning-based tool for clinicians to stratify HCC risk in patients with CLDs.
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Affiliation(s)
- Huapeng Lin
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong
| | - Guanlin Li
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong
| | - Adèle Delamarre
- Hepatology Unit, Hôpital Haut Lévêque, Bordeaux University Hospital, Bordeaux, France; INSERM U1312, Bordeaux University, Bordeaux, France
| | - Sang Hoon Ahn
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; Yonsei Liver Center, Severance Hospital, Seoul, Korea
| | - Xinrong Zhang
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong
| | - Beom Kyung Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; Yonsei Liver Center, Severance Hospital, Seoul, Korea
| | - Lilian Yan Liang
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong
| | - Hye Won Lee
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; Yonsei Liver Center, Severance Hospital, Seoul, Korea
| | - Grace Lai-Hung Wong
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong
| | - Pong-Chi Yuen
- Department of Computer Science, Hong Kong Baptist University, Hong Kong
| | - Henry Lik-Yuen Chan
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; Union Hospital, Hong Kong
| | - Stephen Lam Chan
- Department of Clinical Oncology, Sir YK Pao Centre for Cancer, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong
| | - Vincent Wai-Sun Wong
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong
| | - Victor de Lédinghen
- Hepatology Unit, Hôpital Haut Lévêque, Bordeaux University Hospital, Bordeaux, France; INSERM U1312, Bordeaux University, Bordeaux, France.
| | - Seung Up Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; Yonsei Liver Center, Severance Hospital, Seoul, Korea.
| | - Terry Cheuk-Fung Yip
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong.
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Wong GLH, Chan HLY, Tse YK, Yuen PC, Wong VWS. Machine learning in predicting hepatocellular carcinoma in patients with chronic viral hepatitis in Hong Kong: abridged secondary publication. Hong Kong Med J 2023; 29 Suppl 1:14-17. [PMID: 36919212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Affiliation(s)
- G L H Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - H L Y Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Y K Tse
- Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - P C Yuen
- Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - V W S Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
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Yip TCF, Lyu F, Lin H, Li G, Yuen PC, Wong VWS, Wong GLH. Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future. Clin Mol Hepatol 2023; 29:S171-S183. [PMID: 36503204 PMCID: PMC10029958 DOI: 10.3350/cmh.2022.0426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.
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Affiliation(s)
- Terry Cheuk-Fung Yip
- Medical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Department of Medicine and Therapeutics, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Institute of Digestive Disease, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
| | - Fei Lyu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Huapeng Lin
- Medical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Department of Medicine and Therapeutics, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Institute of Digestive Disease, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
| | - Guanlin Li
- Medical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Department of Medicine and Therapeutics, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Institute of Digestive Disease, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
| | - Pong-Chi Yuen
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Vincent Wai-Sun Wong
- Medical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Department of Medicine and Therapeutics, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Institute of Digestive Disease, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
| | - Grace Lai-Hung Wong
- Medical Data Analytic Centre, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Department of Medicine and Therapeutics, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
- Institute of Digestive Disease, Prince of Wales Hospital and the University is The Chinese University of Hong Kong, Hong Kong, China
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Wong GLH, Hui VWK, Tan Q, Xu J, Lee HW, Yip TCF, Yang B, Tse YK, Yin C, Lyu F, Lai JCT, Lui GCY, Chan HLY, Yuen PC, Wong VWS. Novel machine-learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis. JHEP Rep 2022; 4:100441. [PMID: 35198928 PMCID: PMC8844233 DOI: 10.1016/j.jhepr.2022.100441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/08/2023] Open
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Wong GLH, Yuen PC, Ma AJ, Chan AWH, Leung HHW, Wong VWS. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021; 36:543-550. [PMID: 33709607 DOI: 10.1111/jgh.15385] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/14/2020] [Accepted: 12/20/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non-alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.
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Affiliation(s)
- Grace Lai-Hung Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong.,Medical Data Analytic Centre (MDAC), The Chinese University of Hong Kong, Shatin, Hong Kong.,Institute of Digestive Disease, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Pong-Chi Yuen
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Andy Jinhua Ma
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Anthony Wing-Hung Chan
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Howard Ho-Wai Leung
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Vincent Wai-Sun Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong.,Medical Data Analytic Centre (MDAC), The Chinese University of Hong Kong, Shatin, Hong Kong.,Institute of Digestive Disease, The Chinese University of Hong Kong, Shatin, Hong Kong
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Wong GLH, Ma AJ, Deng H, Ching JYL, Wong VWS, Tse YK, Yip TCF, Lau LHS, Liu HHW, Leung CM, Tsang SWC, Chan CW, Lau JYW, Yuen PC, Chan FKL. Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding. Aliment Pharmacol Ther 2019; 49:912-918. [PMID: 30761584 DOI: 10.1111/apt.15145] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 10/09/2018] [Accepted: 12/27/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Patients with a history of Helicobacter pylori-negative idiopathic bleeding ulcers have an increased risk of recurring ulcer complications. AIM To build a machine learning model to identify patients at high risk for recurrent ulcer bleeding. METHODS Data from a retrospective cohort of 22 854 patients (training cohort) diagnosed with peptic ulcer disease in 2007-2016 were analysed to build a model (IPU-ML) to predict recurrent ulcer bleeding. We tested the IPU-ML in all patients with a diagnosis of gastrointestinal bleeding (n = 1265) in 2008-2015 from a different catchment population (independent validation cohort). Any co-morbid conditions which had occurred in >1% of study population were eligible as predictors. RESULTS Recurrent ulcer bleeding developed in 4772 patients (19.5%) in the training cohort, during a median follow-up period of 2.7 years. IPU-ML model built on six parameters (age, baseline haemoglobin, and presence of gastric ulcer, gastrointestinal diseases, malignancies, and infections) identified patients with bleeding recurrence within 1 year with an area under the receiver operating characteristic curve (AUROC) of 0.648. When we set the IPU-ML cutoff value at 0.20, 27.5% of patients were classified as high risk for rebleeding with a sensitivity of 41.4%, specificity of 74.6%, and a negative predictive value of 91.1%. In the validation cohort, the IPU-ML identified patients with a recurrence ulcer bleeding within 1 year with an AUROC of 0.775, and 84.3% of overall accuracy. CONCLUSION We developed a machine-learning model to identify those patients with a history of idiopathic gastroduodenal ulcer bleeding who are not at high risk for recurrent ulcer bleeding.
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Affiliation(s)
- Grace Lai-Hung Wong
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Andy Jinhua Ma
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.,School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Huiqi Deng
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.,School of Mathematics, Sun Yat-Sen University, Guangzhou, China
| | - Jessica Yuet-Ling Ching
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Vincent Wai-Sun Wong
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Yee-Kit Tse
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Terry Cheuk-Fung Yip
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Louis Ho-Shing Lau
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Chi-Man Leung
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | | | - Chun-Wing Chan
- Department of Medicine, Yan Chai Hospital, Hong Kong, China
| | - James Yun-Wong Lau
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Pong-Chi Yuen
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Francis Ka-Leung Chan
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
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Tan Q, Ma AJ, Deng H, Wong VWS, Tse YK, Yip TCF, Wong GLH, Ching JYL, Chan FKL, Yuen PC. A Hybrid Residual Network and Long Short-Term Memory Method for Peptic Ulcer Bleeding Mortality Prediction. AMIA Annu Symp Proc 2018; 2018:998-1007. [PMID: 30815143 PMCID: PMC6371275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The prediction of patient mortality, which can detect high-risk patients, is a significant yet challenging problem in medical informatics. Thanks to the wide adoption of electronic health records (EHRs), many data-driven methods have been proposed to forecast mortality. However, most existing methods do not consider correlations between static and dynamic data, which contain significant information about mutual influences between these data. In this paper, we utilize a deep Residual Network (ResNet) consisting of many convolution units, which can jointly analyze different variables, to capture correlation information in and between static and dynamic variables. Furthermore, the Long Short-Term Memory (LSTM) method is used to extract temporal dependencies information from dynamic data. Finally, a deep fusion method is used to integrate these different types of information to improve mortality prediction. Experiment results on Peptic Ulcer Bleeding (PUB) mortality prediction show that the proposed method outperforms existing methods and achieves an AUC (area under the receiver operating characteristic curve) score of 0.9353.
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Affiliation(s)
| | - Andy Jinhua Ma
- Hong Kong Baptist University, Hong Kong
- Sun Yat-Sen University, Guangzhou, China
| | - Huiqi Deng
- Hong Kong Baptist University, Hong Kong
- Sun Yat-Sen University, Guangzhou, China
| | | | - Yee-Kit Tse
- The Chinese University of Hong Kong, Hong Kong
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Abstract
A regularized color clustering algorithm is proposed to solve the color clustering problem in medical image database. By incorporating both measures of cluster separability and cluster compactness, regularized color clustering allows the automatic extraction of significant color groups with varying populations. Experimental results in different color spaces show that the regularized color clustering gives superior results in extracting significant distinct/abnormal color clusters without significant increases in cluster compactness. Furthermore, results of color clustering in different color spaces show that the LUV color space is more suitable for color clustering. Methods for selecting the regularization constants have also been suggested.
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Knapp JZ, Dull HF, Bjorndal PM, Brakl SF, Yuen PC. A wide pH range stopper for improved particulate quality in parenteral solutions. J Parenter Sci Technol 1984; 38:128-38. [PMID: 6491855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Weaver PF, Yuen PC, Prolss GW, Furumoto AS. Acoustic Coupling into the Ionosphere from Seismic Waves of the Earthquake at Kurile Islands on August 11, 1969. Nature 1970; 226:1239-41. [PMID: 16057779 DOI: 10.1038/2261239a0] [Citation(s) in RCA: 43] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/1969] [Indexed: 11/09/2022]
Affiliation(s)
- P F Weaver
- Department of Electrical Engineering, University of Hawaii, Honolulu, Hawaii 96822, USA
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