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Yang SC, Liang L, Wang MD, Wang XM, Gu LH, Lin KY, Zhou YH, Chen TH, Gu WM, Li J, Wang H, Chen Z, Li C, Yao LQ, Diao YK, Sun LY, Zhang CW, Zeng YY, Lau WY, Huang DS, Shen F, Yang T. Prospective validation of the Eastern Staging in predicting survival after surgical resection for patients with hepatocellular carcinoma: a multicenter study from China. HPB (Oxford) 2023; 25:81-90. [PMID: 36167767 DOI: 10.1016/j.hpb.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/29/2022] [Accepted: 09/06/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND The Eastern Staging System, which was specially developed for patients undergoing surgical resection for hepatocellular carcinoma (HCC), has been proposed for more than ten years. To prospectively validate the predictive accuracy of the Eastern staging on long-term survival after HCC resection. METHODS Patients who underwent hepatectomy for HCC from 2011 to 2020 at 10 Chinese hospitals were identified from a prospectively collected database. The survival predictive accuracy was evaluated and compared between the Eastern Staging with six other staging systems, including the JIS, BCLC, Okuda, CLIP, 8th AJCC TNM, and HKLC staging. RESULTS Among 2365 patients, the 1-, 3-, and 5-year overall survival rates were 84.2%, 64.5%, and 52.6%, respectively. Among these seven staging systems, the Eastern staging was associated with the best monotonicity of gradients (linear trend χ2: 408.5) and homogeneity (likelihood ratio χ2: 447.3), and the highest discriminatory ability (the areas under curves for 1-, 3-, and 5-year mortality: 0.776, 0.787, and 0.768, respectively). In addition, the Eastern staging was the most informative staging system in predicting survival (Akaike information criterion: 2982.33). CONCLUSION Using a large multicenter prospectively collected database, the Eastern Staging was found to show the best predictive accuracy on long-term overall survival in patients with resectable HCC than the other 6 commonly-used staging systems.
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Affiliation(s)
- Shun-Chao Yang
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China; Graduate School, Hebei North University, Hebei, China
| | - Lei Liang
- Department of General Surgery, Cancer Center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ming-Da Wang
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China
| | - Xian-Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong, China
| | - Li-Hui Gu
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China
| | - Kong-Ying Lin
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital, Fujian Medical University, Fujian, China
| | - Ya-Hao Zhou
- Department of Hepatobiliary Surgery, Pu'er People's Hospital, Yunnan, China
| | - Ting-Hao Chen
- Department of General Surgery, Ziyang First People's Hospital, Sichuan, China
| | - Wei-Min Gu
- The First Department of General Surgery, The Fourth Hospital of Harbin, Heilongjiang, China
| | - Jie Li
- Department of Hepatobiliary Surgery, Fuyang People's Hospital, Anhui, China
| | - Hong Wang
- Department of General Surgery, Liuyang People's Hospital, Hunan, China
| | - Zhong Chen
- Department of Hepatobiliary Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Chao Li
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China
| | - Lan-Qing Yao
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China
| | - Yong-Kang Diao
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China
| | - Li-Yang Sun
- Department of General Surgery, Cancer Center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Cheng-Wu Zhang
- Department of General Surgery, Cancer Center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yong-Yi Zeng
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital, Fujian Medical University, Fujian, China
| | - Wan Yee Lau
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China; Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Dong-Sheng Huang
- Department of General Surgery, Cancer Center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China; School of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Feng Shen
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China; Eastern Hepatobiliary Clinical Research Institute, Third Affiliated Hospital of Navy Medical University, Shanghai, China.
| | - Tian Yang
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, China; Department of General Surgery, Cancer Center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China; School of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China; Eastern Hepatobiliary Clinical Research Institute, Third Affiliated Hospital of Navy Medical University, Shanghai, China.
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Jiang Y, Wang K, Wang YR, Xiang YJ, Liu ZH, Feng JK, Cheng SQ. Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence. Technol Cancer Res Treat 2023; 22:15330338231212726. [PMID: 37933176 PMCID: PMC10631353 DOI: 10.1177/15330338231212726] [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: 07/26/2023] [Revised: 10/01/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.
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Affiliation(s)
- Yu Jiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Kang Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Yu-Ran Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yan-Jun Xiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Zong-Han Liu
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jin-Kai Feng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Shu-Qun Cheng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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TED: Two-stage expert-guided interpretable diagnosis framework for microvascular invasion in hepatocellular carcinoma. Med Image Anal 2022; 82:102575. [DOI: 10.1016/j.media.2022.102575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 07/08/2022] [Accepted: 08/11/2022] [Indexed: 12/16/2022]
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Huang T, Liu H, Lin Z, Kong J, Lin K, Lin Z, Chen Y, Lin Q, Zhou W, Li J, Li JT, Zeng Y. Preoperative prediction of intrahepatic cholangiocarcinoma lymph node metastasis by means of machine learning: a multicenter study in China. BMC Cancer 2022; 22:931. [PMID: 36038816 PMCID: PMC9426211 DOI: 10.1186/s12885-022-10025-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/22/2022] [Indexed: 11/14/2022] Open
Abstract
Background Hepatectomy is currently the most effective modality for the treatment of intrahepatic cholangiocarcinoma (ICC). The status of the lymph nodes directly affects the choice of surgical method and the formulation of postoperative treatment plans. Therefore, a preoperative judgment of lymph node status is of great significance for patients diagnosed with this condition. Previous prediction models mostly adopted logistic regression modeling, and few relevant studies applied random forests in the prediction of ICC lymph node metastasis (LNM). Methods A total of 149 ICC patients who met clinical conditions were enrolled in the training group. Taking into account preoperative clinical data and imaging features, 21 indicators were included for analysis and modeling. Logistic regression was used to filter variables through multivariate analysis, and random forest regression was used to rank the importance of these variables through the use of algorithms. The model’s prediction accuracy was assessed by the concordance index (C-index) and calibration curve and validated with external data. Result Multivariate analysis shows that Carcinoembryonic antigen (CEA), Carbohydrate antigen19-9 (CA19-9), and lymphadenopathy on imaging are independent risk factors for lymph node metastasis. The random forest algorithm identifies the top four risk factors as CEA, CA19-9, and lymphadenopathy on imaging and Aspartate Transaminase (AST). The predictive power of random forest is significantly better than the nomogram established by logistic regression in both the validation group and the training group (Area Under Curve reached 0.758 in the validation group). Conclusions We constructed a random forest model for predicting lymph node metastasis that, compared with the traditional nomogram, has higher prediction accuracy and simultaneously plays an auxiliary role in imaging examinations.
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Affiliation(s)
- Tingfeng Huang
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, Fujian, 350025, PR, China
| | - Hongzhi Liu
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, Fujian, 350025, PR, China
| | - Zhaowang Lin
- Department of Radiology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Jie Kong
- Department of Hepatobiliary, Heze Municiple Hospital, Heze, China
| | - Kongying Lin
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, Fujian, 350025, PR, China
| | - Zhipeng Lin
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, Fujian, 350025, PR, China
| | - Yifan Chen
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, Fujian, 350025, PR, China
| | - Qizhu Lin
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, Fujian, 350025, PR, China
| | - Weiping Zhou
- The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jingdong Li
- Department of General Surgery, Institute of Hepatobiliary-Pancreatic-Intestinal Diseases, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jiang-Tao Li
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yongyi Zeng
- Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, Fujian, 350025, PR, China.
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Chu T, Zhao C, Zhang J, Duan K, Li M, Zhang T, Lv S, Liu H, Wei F. Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma. Ann Surg Oncol 2022; 29:6774-6783. [PMID: 35754067 PMCID: PMC9492610 DOI: 10.1245/s10434-022-12000-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer death worldwide, and the prognosis remains dismal. In this study, two pivotal factors, microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) were preoperatively predicted simultaneously to assess prognosis. Methods A total of 133 HCC patients who underwent surgical resection and preoperative gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) were included. The statuses of MVI and VETC were obtained from the pathological report and CD34 immunohistochemistry, respectively. A three-dimensional convolutional neural network (3D CNN) for single-task learning aimed at MVI prediction and for multitask learning aimed at simultaneous prediction of MVI and VETC was established by using multiphase Gd-EOB-DTPA-enhanced MRI. Results The 3D CNN for single-task learning achieved an area under receiver operating characteristics curve (AUC) of 0.896 (95% CI: 0.797–0.994). Multitask learning with simultaneous extraction of MVI and VETC features improved the performance of MVI prediction, with an AUC value of 0.917 (95% CI: 0.825–1.000), and achieved an AUC value of 0.860 (95% CI: 0.728–0.993) for the VETC prediction. The multitask learning framework could stratify high- and low-risk groups regarding overall survival (p < 0.0001) and recurrence-free survival (p < 0.0001), revealing that patients with MVI+/VETC+ were associated with poor prognosis. Conclusions A deep learning framework based on 3D CNN for multitask learning to predict MVI and VETC simultaneously could improve the performance of MVI prediction while assessing the VETC status. This combined prediction can stratify prognosis and enable individualized prognostication in HCC patients before curative resection. Supplementary Information The online version contains supplementary material available at 10.1245/s10434-022-12000-6.
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Affiliation(s)
- Tongjia Chu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Chen Zhao
- College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China
| | - Jian Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Kehang Duan
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Tianqi Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Shengnan Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Huan Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China.
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Liu W, Zhang L, Xin Z, Zhang H, You L, Bai L, Zhou J, Ying B. A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm. Front Oncol 2022; 12:852736. [PMID: 35311094 PMCID: PMC8931027 DOI: 10.3389/fonc.2022.852736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/11/2022] [Indexed: 01/27/2023] Open
Abstract
BackgroundThe non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.MethodsA total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.ResultsThe top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.ConclusionsThe XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.
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Affiliation(s)
- Weiwei Liu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Lifan Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaodan Xin
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Haili Zhang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu, China
| | - Liting You
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ling Bai
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Juan Zhou, ; Binwu Ying,
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