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.

Preliminary data on computed tomography-based radiomics for predicting programmed death ligand 1 expression in urothelial carcinoma: Chang Mu Lee, Seung Baek Hong, Nam Kyung Lee, Hong Koo Ha, Kyung Hwan Kim, Byeong Jin Kang, Suk Kim, Ja Yoon Ku; Kosin Med J. 2024;39(3):186-194. Published online July 18, 2024; DOI: https://doi.org/10.7180/kmj.24.103

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Abstract PDF Supplementary Material PubReader ePub: Background
Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma.
Methods
Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed.
Results
Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96.
Conclusions
This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

Case report

Evaluation on Work-Relatedness of Herniated Discs on Lumbar Spine Using Ergonomic Assessment Tools: Jung Won Kim, Byeong Jin Ye; Kosin Med J. 2010;25(2):212-216. Published online December 31, 2010

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