Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis

被引:8
作者
Fan, Rong [1 ]
Yu, Ning [1 ]
Li, Guanlin [2 ,3 ]
Arshad, Tamoore [4 ]
Liu, Wen-Yue [5 ]
Wong, Grace Lai-Hung [2 ,3 ]
Liang, Xieer [1 ]
Chen, Yongpeng [1 ]
Jin, Xiao-Zhi [6 ]
Leung, Howard Ho-Wai [7 ]
Chen, Jinjun [1 ]
Wang, Xiao-Dong [8 ]
Yip, Terry Cheuk-Fung [2 ,3 ]
Sanyal, Arun J. [9 ]
Sun, Jian [1 ]
Wong, Vincent Wai-Sun [2 ,3 ,10 ]
Zheng, Ming-Hua [6 ,8 ]
Hou, Jinlin [1 ,11 ]
机构
[1] Southern Med Univ, Nanfang Hosp, Guangdong Prov Clin Res Ctr Viral Hepatitis, Guangdong Prov Key Lab Viral Hepatitis Res,Dept In, Guangzhou, Peoples R China
[2] Chinese Univ Hong Kong, Med Data Analyt Ctr, Dept Med & Therapeut, Hong Kong, Peoples R China
[3] Chinese Univ Hong Kong, Inst Digest Dis, State Key Lab Digest Dis, Hong Kong, Peoples R China
[4] Virginia Commonwealth Univ, Dept Internal Med, Richmond, VA USA
[5] Wenzhou Med Univ, Dept Endocrinol, Affiliated Hosp 1, Wenzhou, Peoples R China
[6] Wenzhou Med Univ, Affiliated Hosp 1, Dept Hepatol, MAFLD Res Ctr, 2 Fuxue Lane, Wenzhou 325000, Peoples R China
[7] Chinese Univ Hong Kong, Dept Anat & Cellular Pathol, Hong Kong, Peoples R China
[8] Key Lab Diag & Treatment Dev Chron Liver Dis Zheji, Wenzhou, Peoples R China
[9] Virginia Commonwealth Univ, Div Gastroenterol, Richmond, VA USA
[10] Prince Wales Hosp, Dept Med & Therapeut, Shatin, 9-F Clin Sci Bldg,30-32 Ngan Shing St, Hong Kong, Peoples R China
[11] Southern Med Univ, Nanfang Hosp, Dept Infect Dis, Guangzhou 510515, Peoples R China
关键词
advanced fibrosis; aMAP score; cirrhosis; machine learning; metabolic dysfunction-associated steatotic liver disease; SIMPLE NONINVASIVE INDEX; SCORE; ELASTOGRAPHY; PREDICTION; DISEASE; TESTS; RISK;
D O I
10.1111/liv.15818
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Background & Aims: aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance.Methods: A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125).Results: The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively).Conclusions: The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.
引用
收藏
页码:749 / 759
页数:11
相关论文
共 27 条
  • [1] New sequential combinations of non-invasive fibrosis tests provide an accurate diagnosis of advanced fibrosis in NAFLD
    Boursier, Jerome
    Guillaume, Maeva
    Leroy, Vincent
    Irles, Marie
    Roux, Marine
    Lannes, Adrien
    Foucher, Juliette
    Zuberbuhler, Floraine
    Delabaudiere, Cyrielle
    Barthelon, Justine
    Michalak, Sophie
    Hiriart, Jean-Baptiste
    Peron, Jean-Marie
    Gerster, Theophile
    Le Bail, Brigitte
    Riou, Jeremie
    Hunault, Gilles
    Merrouche, Wassil
    Oberti, Frederic
    Pelade, Laurence
    Fouchard, Isabelle
    Bureau, Christophe
    Cales, Paul
    de Ledinghen, Victor
    [J]. JOURNAL OF HEPATOLOGY, 2019, 71 (02) : 389 - 396
  • [2] Diagnostic accuracy and prognostic significance of blood fibrosis tests and liver stiffness measurement by FibroScan in non-alcoholic fatty liver disease
    Boursier, Jerome
    Vergniol, Julien
    Guillet, Anne
    Hiriart, Jean-Baptiste
    Lannes, Adrien
    Le Bail, Brigitte
    Michalak, Sophie
    Chermak, Faiza
    Bertrais, Sandrine
    Foucher, Juliette
    Oberti, Frederic
    Charbonnier, Maude
    Fouchard-Hubert, Isabelle
    Rousselet, Marie-Christine
    Cales, Paul
    de Ledinghen, Victor
    [J]. JOURNAL OF HEPATOLOGY, 2016, 65 (03) : 570 - 578
  • [3] Machine learning models are superior to noninvasive tests in identifying clinically significant stages of NAFLD and NAFLD-related cirrhosis
    Chang, Devon
    Truong, Emily
    Mena, Edward A.
    Pacheco, Fabiana
    Wong, Micaela
    Guindi, Maha
    Todo, Tsuyoshi T.
    Noureddin, Nabil
    Ayoub, Walid
    Yang, Ju Dong
    Kim, Irene K.
    Kohli, Anita
    Alkhouri, Naim
    Harrison, Stephen
    Noureddin, Mazen
    [J]. HEPATOLOGY, 2023, 77 (02) : 546 - 557
  • [4] Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B
    Chen, Yang
    Luo, Yan
    Huang, Wei
    Hu, Die
    Zheng, Rong-qin
    Gong, Shu-zhen
    Meng, Fan-kun
    Yang, Hong
    Lin, Hong-jun
    Sun, Yan
    Wang, Xiu-yan
    Wu, Tao
    Ren, Jie
    Pei, Shu-Fang
    Zheng, Ying
    He, Yun
    Hu, Yu
    Yang, Na
    Yan, Hongmei
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2017, 89 : 18 - 23
  • [5] Model consisting of ultrasonographic and simple blood indexes accurately identify compensated hepatitis B cirrhosis
    Chen, Yong-Peng
    Dai, Lin
    Wang, Jing-Lin
    Zhu, You-Fu
    Feng, Xiao-Rong
    Hou, Jin-Lin
    [J]. JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY, 2008, 23 (08) : 1228 - 1234
  • [6] Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver
    Choi, Kyu Jin
    Jang, Jong Keon
    Lee, Seung Soo
    Sung, Yu Sub
    Shim, Woo Hyun
    Kim, Ho Sung
    Yun, Jessica
    Choi, Jin-Young
    Lee, Yedaun
    Kang, Bo-Kyeong
    Kim, Jin Hee
    Kim, So Yeon
    Yu, Eun Sil
    [J]. RADIOLOGY, 2018, 289 (03) : 688 - 697
  • [7] Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease
    Eddowes, Peter J.
    Sasso, Magali
    Allison, Michael
    Tsochatzis, Emmanouil
    Anstee, Quentin M.
    Sheridan, David
    Guha, Indra N.
    Cobbold, Jeremy F.
    Deeks, Jonathan J.
    Paradis, Valerie
    Bedossa, Pierre
    Newsome, Philip N.
    [J]. GASTROENTEROLOGY, 2019, 156 (06) : 1717 - 1730
  • [8] aMAP Score and Its Combination With Liver Stiffness Measurement Accurately Assess Liver Fibrosis in Chronic Hepatitis B Patients
    Fan, Rong
    Li, Guanlin
    Yu, Ning
    Chang, Xiujuan
    Arshad, Tamoore
    Liu, Wen-Yue
    Chen, Yan
    Wong, Grace Lai-Hung
    Jiang, Yiyue
    Liang, Xieer
    Chen, Yongpeng
    Jin, Xiao-Zhi
    Dong, Zheng
    Leung, Howard Ho-Wai
    Wang, Xiao-Dong
    Zeng, Zhen
    Yip, Terry Cheuk-Fung
    Xie, Qing
    Tan, Deming
    You, Shaoli
    Ji, Dong
    Zhao, Jun
    Sanyal, Arun J.
    Sun, Jian
    Zheng, Ming-Hu
    Wong, Vincent Wai-Sun
    Yang, Yongping
    Hou, Jinlin
    [J]. CLINICAL GASTROENTEROLOGY AND HEPATOLOGY, 2023, 21 (12) : 3070 - +
  • [9] Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures
    Fan, Rong
    Chen, Lei
    Zhao, Siru
    Yang, Hao
    Li, Zhengmao
    Qian, Yunsong
    Ma, Hong
    Liu, Xiaolong
    Wang, Chuanxin
    Liang, Xieer
    Bai, Jian
    Xie, Jianping
    Fan, Xiaotang
    Xie, Qing
    Hao, Xin
    Wang, Chunying
    Yang, Song
    Gao, Yanhang
    Bai, Honglian
    Dou, Xiaoguang
    Liu, Jingfeng
    Wu, Lin
    Jiang, Guoqing
    Xia, Qi
    Zheng, Dan
    Rao, Huiying
    Xia, Jie
    Shang, Jia
    Gao, Pujun
    Xie, Dongying
    Yu, Yanlong
    Yang, Yongfeng
    Gao, Hongbo
    Liu, Yali
    Sun, Aimin
    Jiang, Yongfang
    Yu, Yanyan
    Niu, Junqi
    Sun, Jian
    Wang, Hongyang
    Hou, Jinlin
    [J]. JOURNAL OF HEPATOLOGY, 2023, 79 (04) : 933 - 944
  • [10] aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis
    Fan, Rong
    Papatheodoridis, George
    Sun, Jian
    Innes, Hamish
    Toyoda, Hidenori
    Xie, Qing
    Mo, Shuyuan
    Sypsa, Vana
    Guha, Indra Neil
    Kumada, Takashi
    Niu, Junqi
    Dalekos, George
    Yasuda, Satoshi
    Barnes, Eleanor
    Lian, Jianqi
    Suri, Vithika
    Idilman, Ramazan
    Barclay, Stephen T.
    Dou, Xiaoguang
    Berg, Thomas
    Hayes, Peter C.
    Flaherty, John F.
    Zhou, Yuanping
    Zhang, Zhengang
    Buti, Maria
    Hutchinson, Sharon J.
    Guo, Yabing
    Calleja, Jose Luis
    Lin, Lanjia
    Zhao, Longfeng
    Chen, Yongpeng
    Janssen, Harry L. A.
    Zhu, Chaonan
    Shi, Lei
    Tang, Xiaoping
    Gaggar, Anuj
    Wei, Lai
    Jia, Jidong
    Irving, William L.
    Johnson, Philip J.
    Lampertico, Pietro
    Hou, Jinlin
    [J]. JOURNAL OF HEPATOLOGY, 2020, 73 (06) : 1368 - 1378