Kosin Medical Journal

Original articles

Platelet count as a predictor of advanced-stage liver cirrhosis: a comparative study with established fibrosis markers: Hyung Hwan Moon, Kwang Il Seo, Hyunyong Hwang, Young Il Choi, Dong Hoon Shin, Myunghee Yoon, Bohyeon Kim, Yeha Joo; Kosin Med J. 2025;40(4):308-316. Published online December 26, 2025; DOI: https://doi.org/10.7180/kmj.25.143

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Abstract PDF PubReader ePub: Background
Accurate assessment of liver fibrosis is critical for the management of chronic liver disease. Noninvasive biomarkers are increasingly being investigated as alternatives to liver biopsy. Platelet count has emerged as a potential predictor of advanced fibrosis and may complement established indices such as the fibrosis-4 (FIB-4) score and the aspartate aminotransferase-to-platelet ratio index (APRI).
Methods
This prospective analysis included 101 patients with histologically confirmed data obtained through liver biopsy or hepatic resection. Platelet count, APRI, FIB-4, Model for End-Stage Liver Disease score, Mac-2 binding protein glycosylation isomer (M2BPGi), and albumin-bilirubin score were measured and correlated with fibrosis stage using the METAVIR scoring system. Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to assess the predictive performance of each marker.
Results
Platelet count demonstrated an inverse correlation with fibrosis severity and was identified as the most reliable predictor of advanced fibrosis (METAVIR ≥3), with an area under the ROC curve of 0.822. Using a cutoff value of 184,000, platelet count yielded a sensitivity of 69.2% and a specificity of 87.8% for the detection of significant fibrosis.
Conclusions
Platelet count is a simple, widely available, and robust predictor of liver fibrosis, outperforming APRI, FIB-4, and M2BPGi in multivariate analysis. Validation in larger, independent cohorts is warranted to confirm its clinical utility.

A prospective study of the correlation between hepatic fibrosis and noninvasively measured fibrosis markers including serum M2BPGi and acoustic radiation force impulse elastography: Kwang Il Seo, Hyunyong Hwang, Byung Cheol Yun, Hyung Hwan Moon, Young Il Choi, Dong Hoon Shin, Myunghee Yoon; Kosin Med J. 2022;37(2):146-153. Published online June 24, 2022; DOI: https://doi.org/10.7180/kmj.22.110

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Background
Mac-2 binding protein glycosylation isomer (M2BPGi) was introduced as a noninvasively measurable serologic marker for liver fibrosis. Acoustic radiation force impulse imaging (ARFI) elastography is another noninvasive method of measuring hepatic fibrosis. There are limited data about the correlations between histologic fibrosis grade and noninvasively measured markers, including M2BPGi and ARFI.
Methods
This prospective study was conducted among patients admitted consecutively for liver resection, cholecystectomy, or liver biopsy. ARFI elastography, serum M2BPGi levels, and the AST to Platelet Ratio Index (APRI) score were evaluated before histologic evaluation. Histologic interpretation was performed by a single pathologist using the METAVIR scoring system.
Results
In patients with high METAVIR scores, M2BPGi levels and ARFI values showed statistically significant differences between patients with fibrosis and those without fibrosis. In 41 patients with hepatocellular carcinoma, as METAVIR scores increased, M2BPGi levels also tended to increase (p=0.161). ARFI values changed significantly as METAVIR scores increased (p=0.039). In 33 patients without hepatocellular carcinoma, as METAVIR scores increased, M2BPGi levels significantly increased (p=0.040). ARFI values also changed significantly as METAVIR scores increased (p=0.033). M2BPGi levels were significantly correlated with ARFI values (r=0.604, p<0.001), and APRI values (r=0.704, p<0.001), respectively.
Conclusions
Serum M2BPGi levels increased with liver fibrosis severity and could be a good marker for diagnosing advanced hepatic fibrosis regardless of the cause of liver disease.

Citations

Citations to this article as recorded by

Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence
Chol Min Kang, Hyung June Ku, Hyung Hwan Moon, Seong-Eun Kim, Ji Hoon Jo, Young Il Choi, Dong Hoon Shin
Journal of Clinical Medicine.2024; 13(2): 381. CrossRef

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