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

• Published in: Liver international Vol. 44; no. 3; pp. 749 - 759
• Main Authors: Fan, Rong, Yu, Ning, Li, Guanlin, Arshad, Tamoore, Liu, Wen‐Yue, Wong, Grace Lai‐Hung, Liang, Xieer, Chen, Yongpeng, Jin, Xiao‐Zhi, Leung, Howard Ho‐Wai, Chen, Jinjun, Wang, Xiao‐Dong, Yip, Terry Cheuk‐Fung, Sanyal, Arun J., Sun, Jian, Wong, Vincent Wai‐Sun, Zheng, Ming‐Hua, Hou, Jinlin
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.03.2024
• Subjects: advanced fibrosis; Algorithms; aMAP score; Aspartate aminotransferase; Aspartate Aminotransferases; Bilirubin; Biomarkers; Biopsy; Cirrhosis; Diagnosis; Diagnostic systems; Elasticity Imaging Techniques; Fatty liver; Fibrosis; Hepatitis B; Hepatocellular carcinoma; Humans; Learning algorithms; Liver; Liver - pathology; Liver cirrhosis; Liver Cirrhosis - diagnosis; Liver Cirrhosis - pathology; Liver diseases; Machine learning; metabolic dysfunction‐associated steatotic liver disease; Model accuracy; Platelets; ROC Curve; Stiffness; aMAP score; machine learning; cirrhosis; metabolic dysfunction-associated steatotic liver disease; advanced fibrosis
• ISSN: 1478-3223; 1478-3231
• DOI: 10.1111/liv.15818

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.