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. 2024 Mar;44(3):749-759.
doi: 10.1111/liv.15818. Epub 2023 Dec 22.

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

Rong Fan  1 , Ning Yu  1 , Guanlin Li  2   3 , Tamoore Arshad  4 , Wen-Yue Liu  5 , Grace Lai-Hung Wong  2   3 , Xieer Liang  1 , Yongpeng Chen  1 , Xiao-Zhi Jin  6 , Howard Ho-Wai Leung  7 , Jinjun Chen  1 , Xiao-Dong Wang  8 , Terry Cheuk-Fung Yip  2   3 , Arun J Sanyal  9 , Jian Sun  1 , Vincent Wai-Sun Wong  2   3 , Ming-Hua Zheng  6   8 , Jinlin Hou  1
Affiliations

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

Rong Fan et al. Liver Int. 2024 Mar.

Abstract

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.

Keywords: aMAP score; advanced fibrosis; cirrhosis; machine learning; metabolic dysfunction-associated steatotic liver disease.

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References

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