| FIB-4plus Score: A novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): An international multicenter study |
| Bingtian Dong1,2, Ruiling He3,4, Shenghong Ju5, Yuping Chen1, Ivica Grgurevic6, Jianzhong Ma7, Ying Guo8, Huizhen Fan9, Qiang Yan10, Chuan Liu1, Huixiong Xu11, Anita Madir12, Kristian Podrug13, Jia Wang14, Linxue Qian14, Zhengzi Geng15, Shanghao Liu1, Tao Ren16, Guo Zhang17, Kun Wang8, Meiqin Su18, Fei Chen19, Sumei Ma19, Liting Zhang20, Zhaowei Tong21, Yonghe Zhou22, Xin Li22, Fanbin He15, Hui Huan16, Wenjuan Wang17, Yunxiao Liang17, Juan Tang23, Fang Ai24, Tingyu Wang25, Liyun Zheng26, Zhongwei Zhao26, Jiansong Ji26, Wei Liu27, Jiaojiao Xu27, Bo Liu27, Xuemei Wang28, Yao Zhang28, Qiong Yan29, Hui Liu30, Xiaomei Chen30, Shuhua Zhang31, Yihua Wang31, Yang Liu31, Li Yin32, Yanni Liu32, Yanqing Huang32, Li Bian33, Ping An33, Xin Zhang33, Shaoting Zhang34, Jinhua Shao35, Xiangman Zhang36, Wei Rao37, Chaoxue Zhang2, Christoph Frank Dietrich38, Won Kim39, Xiaolong Qi1 |
1Liver Disease Center of Integrated Traditional Chinese and Western Medicine, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Nanjing, China; Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University; State Key Laboratory of Digital Medical Engineering, Nanjing, China
2Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230001, China
3Department of Ultrasound, Donggang Branch the First Hospital of Lanzhou University, Lanzhou, China
4The First Clinical Medical College of Lanzhou University, Lanzhou, China
5Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, 210009, China
6Department of Gastroenterology, Hepatology and Clinical Nutrition, University Hospital Dubrava, University of Zagreb School of Medicine and Faculty of Pharmacy and Biochemistry, Zagreb, Croatia
7Department of General Surgery, The Third People’s Hospital of Taiyuan, Taiyuan, China
8Department of Hepatology, The Third people’s Hospital of Taiyuan, Taiyuan, China
9Department of Gastroenterology, Yichun People’s Hospital,Yichun, 336000, China
10Division of HPB Section Department of General Surgery, Huzhou Central Hospital, The Affiliated Central Hospital of Huzhou University, Huzhou, China
11Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
12Department of Gastroenterology, Hepatology and Clinical Nutrition, University Hospital Dubrava, Zagreb, Croatia
13Department of gastroenterology and hepatology, University Hospital Center Split, Split, Croatia
14Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
15Department of Ultrasound, The People’s Hospital of Linxia, Linxia, China
16Department of Gastroenterology, Hospital of Chengdu Office of People’s Government of Tibetan autonomous Region, Sichuan, China
17Department of Gastroenterology, Guangxi Hospital Division of the First Affiliated Hospital, Sun Yat-sen University, China
18Department of Ultrasound, The Third people’s Hospital of Taiyuan, Taiyuan, China
19Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou, China
20Department of Infectious Diseases, The First Hospital of Lanzhou University, Lanzhou, China
21Huzhou Key Laboratory of Precision Medicine Research and Translation for Infectious Diseases, Huzhou Central Hospital, Huzhou, Zhejiang, China
22Department of Ultrasonography, Tianjin Second People’s Hospital, Tianjin, China
23Department of Infectious Disease, Zigong First people’s Hospital, Zigong, China
24Department of Ultrasound, Zigong First people’s Hospital, Zigong, China
25Department of Gastroscopy, Zigong First people’s Hospital, Zigong, China
26Department of Radiology, Zhejiang Key Laboratory of Imaging and Interventional Medicine, Lishui Central Hospital, Li Shui, China
27Ultrasound Diagnosis Center, Shaanxi Provincial People’s Hospital, Xi’an, China
28Department of Ultrasound, Ditan Hospital, Capital Medical University, Beijing, China
29Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
30Department of Ultrasound, The Affiliated Hospital of Southwest Medical University, Luzhou, China
31Department of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, China
32Department of Ultrasound, Jincheng People’s Hospital, Jincheng, China
33Department of Hepatology, The Sixth People’s Hospital of Shenyang, Shenyang, China
34Sensetime Research, Shanghai, China
35Wuxi Hisky Medical Technologies Co., Ltd, Wuxi, China
36Department of medical and marketing, Suzhou Hengrui Medical Devices Co., Ltd. Suzhou, China
37Shenzhen New Industries Biomedical Engineering Co., Ltd., Shenzhen 518118, China
38Department General Internal Medicine (DAIM), Hospitals Hirslanden Bern Beau Site, Salem and Permanence, Bern, Switzerland
39Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul 07061, Republic of Korea |
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Received: October 10, 2024 Revised: January 23, 2025 Accepted: February 3, 2025
*Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan and Qiang Yan contributed equally to this work. |
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| ABSTRACT |
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Background/Aims
A large percentage of patients undergoing esophagogastroduodenoscopy (EGD) screening do not have esophageal varices (EV) or have only small EV. We evaluated a large, international, multicenter cohort to develop a novel score, termed FIB-4plus, by combining the fibrosis-4 (FIB-4) score, liver stiffness measurement (LSM), and spleen stiffness measurement (SSM) to identify high-risk EV (HRV) in compensated cirrhosis.
Methods
This international cohort study involved patients with compensated cirrhosis from 17 Chinese hospitals and one Croatian institution (NCT04546360). Two-dimensional shear wave elastography-derived LSM and SSM values, and components of the FIB-4 score (i.e., age, aspartate aminotransferase, alanine aminotransferase, and platelet count [PLT]) were combined using machine learning algorithms (logistic regression [LR] and extreme gradient boosting [XGBoost]) to develop the LR-FIB-4plus and XGBoost-FIB-4plus models, respectively. Shapley Additive exPlanations method was used to interpret the model predictions.
Results
We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. The XGBoost-FIB-4plus score demonstrated superior predictive performance for HRV, with an area under the receiver operating characteristic curve (AUROC) of 0.927 (95% CI: 0.897-0.957) in the training cohort (n=268), and 0.919 (95% CI: 0.843-0.995) and 0.902 (95% CI: 0.820-0.984) in the first (n=118) and second (n=82) external validation cohorts, respectively. Additionally, the XGBoost-FIB-4plus score exhibited high AUROC values for predicting EV across all cohorts. The FIB-4plus score outperformed the individual parameters (LSM, SSM, PLT, and FIB-4).
Conclusions
The FIB-4plus score effectively predicted EV and HRV in patients with compensated cirrhosis, providing clinicians with a valuable tool for optimizing patient management and outcomes. |
| KeyWords:
High-risk varices; Machine learning; Diagnostic model; Elasticity imaging techniques |
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