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Yau STY, Leung EYM, Hung CT, Wong MCS, Chong KC, Lee A, Yeoh EK. Scoring System for Predicting the Risk of Liver Cancer among Diabetes Patients: A Random Survival Forest-Guided Approach. Cancers (Basel) 2024; 16:2310. [PMID: 39001373 PMCID: PMC11240698 DOI: 10.3390/cancers16132310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/02/2024] [Accepted: 06/07/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND Most liver cancer scoring systems focus on patients with preexisting liver diseases such as chronic viral hepatitis or liver cirrhosis. Patients with diabetes are at higher risk of developing liver cancer than the general population. However, liver cancer scoring systems for patients in the absence of liver diseases or those with diabetes remain rare. This study aims to develop a risk scoring system for liver cancer prediction among diabetes patients and a sub-model among diabetes patients without cirrhosis/chronic viral hepatitis. METHODS A retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. The outcome was diagnosis of liver cancer during follow-up. A risk scoring system was developed by applying random survival forest in variable selection, and Cox regression in weight assignment. RESULTS The liver cancer incidence was 0.92 per 1000 person-years. Patients who developed liver cancer (n = 1995) and those who remained free of cancer (n = 1969) during follow-up (median: 6.2 years) were selected for model building. In the final time-to-event scoring system, presence of chronic hepatitis B/C, alanine aminotransferase, age, presence of cirrhosis, and sex were included as predictors. The concordance index was 0.706 (95%CI: 0.676-0.741). In the sub-model for patients without cirrhosis/chronic viral hepatitis, alanine aminotransferase, age, triglycerides, and sex were selected as predictors. CONCLUSIONS The proposed scoring system may provide a parsimonious score for liver cancer risk prediction among diabetes patients.
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Affiliation(s)
- Sarah Tsz-Yui Yau
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Eman Yee-Man Leung
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Chi-Tim Hung
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Martin Chi-Sang Wong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Ka-Chun Chong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Albert Lee
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Eng-Kiong Yeoh
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
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Wang M, Zhuang B, Yu S, Li G. Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices. Front Oncol 2024; 14:1397505. [PMID: 38952558 PMCID: PMC11215019 DOI: 10.3389/fonc.2024.1397505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC.
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Affiliation(s)
- Mengxia Wang
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang, China
| | - Bo Zhuang
- Department of Hepatobiliary Surgery, The Affliated Jinhua Hospital of Zhejiang University School of Medicine, Jinhua, Zhejiang, China
| | - Shian Yu
- Department of Hepatobiliary Surgery, The Affliated Jinhua Hospital of Zhejiang University School of Medicine, Jinhua, Zhejiang, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
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Ko SH, Cao J, Yang YK, Xi ZF, Han HW, Sha M, Xia Q. Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation. Front Med (Lausanne) 2024; 11:1373005. [PMID: 38919938 PMCID: PMC11196752 DOI: 10.3389/fmed.2024.1373005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/28/2024] [Indexed: 06/27/2024] Open
Abstract
Background Liver transplantation (LT) is one of the main curative treatments for hepatocellular carcinoma (HCC). Milan criteria has long been applied to candidate LT patients with HCC. However, the application of Milan criteria failed to precisely predict patients at risk of recurrence. As a result, we aimed to establish and validate a deep learning model comparing with Milan criteria and better guide post-LT treatment. Methods A total of 356 HCC patients who received LT with complete follow-up data were evaluated. The entire cohort was randomly divided into training set (n = 286) and validation set (n = 70). Multi-layer-perceptron model provided by pycox library was first used to construct the recurrence prediction model. Then tabular neural network (TabNet) that combines elements of deep learning and tabular data processing techniques was utilized to compare with Milan criteria and verify the performance of the model we proposed. Results Patients with larger tumor size over 7 cm, poorer differentiation of tumor grade and multiple tumor numbers were first classified as high risk of recurrence. We trained a classification model with TabNet and our proposed model performed better than the Milan criteria in terms of accuracy (0.95 vs. 0.86, p < 0.05). In addition, our model showed better performance results with improved AUC, NRI and hazard ratio, proving the robustness of the model. Conclusion A prognostic model had been proposed based on the use of TabNet on various parameters from HCC patients. The model performed well in post-LT recurrence prediction and the identification of high-risk subgroups.
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Affiliation(s)
- Seung Hyoung Ko
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Medicine, CHA University, Seongnam-si, Republic of Korea
| | - Jie Cao
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yong-kang Yang
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-feng Xi
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hyun Wook Han
- Department of Medicine, CHA University, Seongnam-si, Republic of Korea
| | - Meng Sha
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qiang Xia
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Lai JCT, Liang LY, Wong GLH. Noninvasive tests for liver fibrosis in 2024: are there different scales for different diseases? Gastroenterol Rep (Oxf) 2024; 12:goae024. [PMID: 38605932 PMCID: PMC11009030 DOI: 10.1093/gastro/goae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/25/2024] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Liver fibrosis is the common pathway from various chronic liver diseases and its progression leads to cirrhosis which carries a significant risk for the development of portal hypertension-related complications and hepatocellular carcinoma. It is crucial to identify and halt the worsening of liver fibrosis given its important prognostic implication. Liver biopsy is the gold standard for assessing the degree of liver fibrosis but is limited due to its invasiveness and impracticality for serial monitoring. Many noninvasive tests have been developed over the years trying to assess liver fibrosis in a practical and accurate way. The tests are mainly laboratory- or imaging-based, or in combination. Laboratory-based tests can be derived from simply routine blood tests to patented laboratory parameters. Imaging modalities include ultrasound and magnetic resonance elastography, in which vibration-controlled transient elastography is the most widely validated and adopted whereas magnetic resonance elastography has been proven the most accurate liver fibrosis assessment tool. Nonetheless, noninvasive tests do not always apply to all liver diseases, nor does a common cut-off value of a test mean the same degree of liver fibrosis in different scenarios. In this review, we discuss the diagnostic and prognostic performance, as well as the confounders and limitations, of different noninvasive tests on liver fibrosis assessment in various liver diseases.
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Affiliation(s)
- Jimmy Che-To Lai
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
- Medical Data Analytics Centre, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lilian Yan Liang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
- Medical Data Analytics Centre, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Grace Lai-Hung Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
- Medical Data Analytics Centre, The Chinese University of Hong Kong, Hong Kong SAR, China
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Yerukala Sathipati S, Aimalla N, Tsai MJ, Carter T, Jeong S, Wen Z, Shukla SK, Sharma R, Ho SY. Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma. Carcinogenesis 2023; 44:650-661. [PMID: 37701974 DOI: 10.1093/carcin/bgad062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/01/2023] [Accepted: 09/08/2023] [Indexed: 09/14/2023] Open
Abstract
OBJECTIVE Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC. METHODS The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression. RESULTS HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis. CONCLUSIONS Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.
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Affiliation(s)
| | - Nikhila Aimalla
- Department of Internal Medicine-Pediatrics, Marshfield Clinic Health System, Marshfield, WI 54449, USA
| | - Ming-Ju Tsai
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Tonia Carter
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Sohyun Jeong
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Zhi Wen
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Sanjay K Shukla
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
| | - Rohit Sharma
- Department of Surgical Oncology, Marshfield Clinic Health System, Marshfield, WI 54449, USA
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
- Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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So H, Lam TO, Meng H, Lam SHM, Tam LS. Time and dose-dependent effect of systemic glucocorticoids on major adverse cardiovascular event in patients with rheumatoid arthritis: a population-based study. Ann Rheum Dis 2023; 82:1387-1393. [PMID: 37487608 DOI: 10.1136/ard-2023-224185] [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] [Accepted: 06/29/2023] [Indexed: 07/26/2023]
Abstract
OBJECTIVES Cardiovascular event (CVE) risk in rheumatoid arthritis (RA) was increased by glucocorticoids (GC) use. Whether there is a threshold dose and duration of GC use beyond which will increase CVE rate remains controversial. We studied the time-varying effect of GC and its dose on the risk of incident major adverse cardiovascular events (MACE) in patients with RA. METHODS Patients with RA without MACE at baseline were recruited from a Hong Kong citywide database from 2006 to 2015 and followed till 2018. The primary outcome was the first occurrence of an MACE. Cox regression and inverse probability treatment weighting analyses with time-varying covariates were used to evaluate the association of GC and MACE, adjusting for demographics, traditional CV risk factors, inflammatory markers and the usage of antirheumatic drugs. RESULTS Among 12 233 RA patients with 105 826 patient-years of follow-up and a mean follow-up duration of 8.7 years, 860 (7.0%) developed MACE. In the time-varying analyses after controlling for confounding factors, a daily prednisolone dose of ≥5 mg significantly increased the risk of MACE (erythrocyte sedimentation rate model: HR 2.02, 95% CI 1.72 to 2.37; C reactive protein model: HR 1.87, 95% CI 1.60 to 2.18), while a daily dose below 5 mg was not associated with MACE risk, compared with no GC use. In patients receiving daily prednisolone ≥5 mg, the risk of incident MACE was increased by 7% per month. CONCLUSIONS GC was associated with a duration and dose-dependent increased risk of MACE in patients with RA. Very low dose prednisolone (<5 mg daily) did not appear to confer excessive CV risk.
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Affiliation(s)
- Ho So
- Department of Medicine & Therapeutics, The Chinese University, Hong Kong, Hong Kong
| | - Tsz On Lam
- Department of Medicine & Therapeutics, The Chinese University, Hong Kong, Hong Kong
| | - Huan Meng
- Department of Medicine & Therapeutics, The Chinese University, Hong Kong, Hong Kong
| | - Steven Ho Man Lam
- Department of Medicine & Therapeutics, The Chinese University, Hong Kong, Hong Kong
| | - Lai-Shan Tam
- Department of Medicine & Therapeutics, The Chinese University, Hong Kong, Hong Kong
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Feng S, Wang J, Wang L, Qiu Q, Chen D, Su H, Li X, Xiao Y, Lin C. Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma. J Clin Transl Hepatol 2023; 11:1184-1191. [PMID: 37577233 PMCID: PMC10412715 DOI: 10.14218/jcth.2022.00077s] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/11/2022] [Accepted: 02/21/2023] [Indexed: 07/03/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.
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Affiliation(s)
- Sijia Feng
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Jianhua Wang
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Liheng Wang
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Qixuan Qiu
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Dongdong Chen
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Huo Su
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Xiaoli Li
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Yao Xiao
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Chiayen Lin
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
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Yip TCF, Yurdaydin C. Improving prediction of hepatocellular carcinoma in chronic hepatitis B by machine learning: Productive relationship of medicine with computer science. Liver Int 2023; 43:1626-1628. [PMID: 37452504 DOI: 10.1111/liv.15631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Terry C F Yip
- Medical Data Analytics Centre, 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
| | - Cihan Yurdaydin
- Department of Gastroenterology & Hepatology, Koç University Medical School, Istanbul, Turkey
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Kim J, Hur MH, Kim SU, Kim JW, Sinn DH, Lee HW, Kim MY, Cheong JY, Jung YJ, Lee HA, Jin YJ, Yoon JS, Park SJ, Lee CH, Kim IH, Lee JS, Cho YY, Kim HJ, Park SY, Seo YS, Oh H, Jun DW, Kim MN, Chang Y, Jang JY, Hwang SY, Kim YJ. Inverse Propensity Score-Weighted Analysis of Entecavir and Tenofovir Disoproxil Fumarate in Patients with Chronic Hepatitis B: A Large-Scale Multicenter Study. Cancers (Basel) 2023; 15:cancers15112936. [PMID: 37296898 DOI: 10.3390/cancers15112936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/17/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Tenofovir disoproxil fumarate (TDF) is reportedly superior or at least comparable to entecavir (ETV) in preventing hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients; however, it remains controversial. This study aimed to conduct comprehensive comparisons between the two antivirals. CHB patients initially treated with ETV or TDF between 2012 and 2015 at 20 referral centers in Korea were included. The primary outcome was the cumulative incidence of HCC. The secondary outcomes included death or liver transplantation, liver-related outcome, extrahepatic malignancy, development of cirrhosis, decompensation events, complete virologic response (CVR), seroconversion rate, and safety. Baseline characteristics were balanced using the inverse probability of treatment weighting (IPTW). Overall, 4210 patients were enrolled: 1019 received ETV and 3191 received TDF. During the median follow-ups of 5.6 and 5.5 years, 86 and 232 cases of HCC were confirmed in the ETV and TDF groups, respectively. There was no difference in HCC incidence between the groups both before (p = 0.36) and after IPTW was applied (p = 0.81). Although the incidence of extrahepatic malignancy was significantly higher in the ETV group than in the TDF group before weighting (p = 0.02), no difference was confirmed after IPTW (p = 0.29). The cumulative incidence rates of death or liver transplantation, liver-related outcome, new cirrhosis development, and decompensation events were also comparable in the crude population (p = 0.24-0.91) and in the IPTW-adjusted population (p = 0.39-0.80). Both groups exhibited similar rates of CVR (ETV vs. TDF: 95.1% vs. 95.8%, p = 0.38), and negative conversion of hepatitis B e antigen (41.6% vs. 37.2%, p = 0.09) or surface antigen (2.8% vs. 1.9%, p = 0.10). Compared to the ETV group, more patients in the TDF group changed initial antivirals due to side effects, including decreased kidney function (n = 17), hypophosphatemia (n = 20), and osteoporosis (n = 18). In this large-scale multicenter study, ETV and TDF demonstrated comparable effectiveness across a broad range of outcomes in patients with treatment-naïve CHB during similar follow-up periods.
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Affiliation(s)
- Jihye Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Moon Haeng Hur
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Seung Up Kim
- Department of Internal Medicine and Yonsei Liver Center, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jin-Wook Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Dong Hyun Sinn
- Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Hyun Woong Lee
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Moon Young Kim
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Republic of Korea
| | - Jae Youn Cheong
- Department of Gastroenterology, Ajou University School of Medicine, Suwon 16499, Republic of Korea
| | - Yong Jin Jung
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul 07061, Republic of Korea
| | - Han Ah Lee
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul 07985, Republic of Korea
| | - Young-Joo Jin
- Department of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon 22332, Republic of Korea
| | - Jun Sik Yoon
- Department of Gastroenterology and Hepatology, Inje University Busan Paik Hospital, Busan 47392, Republic of Korea
| | - Sung-Jae Park
- Department of Gastroenterology and Hepatology, Inje University Busan Paik Hospital, Busan 47392, Republic of Korea
| | - Chang Hun Lee
- Department of Internal Medicine, Jeonbuk National University Hospital, Jeonju 54907, Republic of Korea
| | - In Hee Kim
- Department of Internal Medicine, Jeonbuk National University Hospital, Jeonju 54907, Republic of Korea
| | - June Sung Lee
- Department of Internal Medicine, Inje University Ilsan Paik Hospital, Goyang 10380, Republic of Korea
| | - Young Youn Cho
- Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
| | - Hyung Joon Kim
- Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
| | - Soo Young Park
- Department of Internal Medicine, School of Medicine, Kyungpook National University Hospital, Kyungpook National University, Daegu 41944, Republic of Korea
| | - Yeon Seok Seo
- Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea
| | - Hyunwoo Oh
- Department of Internal Medicine, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu 11759, Republic of Korea
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
| | - Mi Na Kim
- Department of Internal Medicine, CHA Bundang Medical Center, CHA University, Seongnam 13496, Republic of Korea
| | - Young Chang
- Department of Internal Medicine, Soonchunhyang University College of Medicine Seoul Hospital, Seoul 04401, Republic of Korea
| | - Jae Young Jang
- Department of Internal Medicine, Soonchunhyang University College of Medicine Seoul Hospital, Seoul 04401, Republic of Korea
| | - Sang Youn Hwang
- Department of Internal Medicine and Gastrointestinal Cancer Center, Dongnam Institute of Radiological & Medical Sciences, Busan 46033, Republic of Korea
| | - Yoon Jun Kim
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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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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A novel image-based machine learning model with superior accuracy and predictability for knee arthroplasty loosening detection and clinical decision making. J Orthop Translat 2022; 36:177-183. [PMID: 36263380 PMCID: PMC9562957 DOI: 10.1016/j.jot.2022.07.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/13/2022] [Accepted: 07/04/2022] [Indexed: 11/08/2022] Open
Abstract
Background Loosening is the leading cause of total knee arthroplasty (TKA) revision. This is a heavy burden toward the healthcare system owing to the difficulty in diagnosis and complications occurring from the delay management. Based on automatic analytical model building, machine learning, may potentially help to automatically recognize the risk of loosening based on radiographs alone. The aim of this study was to build an image-based machine-learning model for detecting TKA loosening. Methods Image-based machine-learning model was developed based on ImageNet, Xception model and a TKA patient X-ray image dataset. Based on a dataset with TKA patient clinical parameters, another system was then created for developing the clinical-information-based machine learning model with random forest classifier. In addition, the Xception Model was pre-trained on the ImageNet database with python and TensorFlow deep learning library for the prediction of loosening. Class activation maps were also used to interpret the prediction decision made by model. Two senior orthopaedic specialists were invited to assess loosening from X-ray images for 3 attempts in setting up comparison benchmark. Result In the image-based machine learning loosening model, the precision rate and recall rate were 0.92 and 0.96, respectively. While for the accuracy rate, 96.3% for visualization classification was observed. However, the addition of clinical-information-based model, with precision rate of 0.71 and recall rate of 0.20, did not further showed improvement on the accuracy. Moreover, as class activation maps showed corresponding signals over bone-implant interface that is loosened radiographically, this confirms that the current model utilized a similar image recognition pattern as that of inspection by clinical specialists. Conclusion The image-based machine learning model developed demonstrated high accuracy and predictability of knee arthroplasty loosening. And the class activation heatmap matched well with the radiographic features used clinically to detect loosening, which highlighting its potential role in assisting clinicians in their daily practice. However, addition of clinical-information-based machine-learning model did not offer further improvement in detection. As far as we know, this is the first report of pure image-based machine learning model with high detection accuracy. Importantly, this is also the first model to show relevant class activation heatmap corresponding to loosening location. Translational potential The finding in this study indicated image-based machine learning model can detect knee arthroplasty loosening with high accuracy and predictability, which the class activation heatmap can potentially assist surgeons to identify the sites of loosening.
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Yip TCF, Lai JCT, Liang LY, Hui VWK, Wong VWS, Wong GLH. Risk of HCC in Patients with HBV, Role of Antiviral Treatment. CURRENT HEPATOLOGY REPORTS 2022; 21:76-86. [DOI: 10.1007/s11901-022-00588-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/19/2022] [Indexed: 08/08/2023]
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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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