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논문 기본 정보

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학술저널
저자정보
Chun-Ting Ho (Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan) Elise Chia-Hui Tan (Department of Health Service Administration, College of Public Health, China Medical University, Taichung, Taiwan) Pei-Chang Lee (Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung) Chi-Jen Chu (Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung) Yi-Hsiang Huang (School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung Universit) Teh-Ia Huo (Division of Basic Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan) Yu-Hui Su (Department of Accounting, Soochow University, Taipei, Taiwan) Ming-Chih Hou (Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung) Jaw-Ching Wu (Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan) Chien-Wei Su (Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung)
저널정보
대한간학회 Clinical and Molecular Hepatology Clinical and Molecular Hepatology Vol.30 No.3
발행연도
2024.7
수록면
406 - 420 (15page)

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초록· 키워드

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Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-to-monocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATS-INF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions: Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.
#Fibrosis #Hepatocellular carcinoma #Inflammation #Machine learning #Prognosis

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