Chun-Ting Ho, Elise Chia-Hui Tan, Pei-Chang Lee, I-Cheng Lee, Jiing-Chyuan Luo, Yi-Hsiang Huang, Teh-Ia Huo, Ming-Chih Hou, Jaw-Ching Wu, Chien-Wei Su
The ML-based CATS-BIT-ME score provides an accurate and reliable tool for stratifying HCC patients into distinct prognostic groups, enhancing personalized disease management and clinical decision-making.
BACKGROUND AND AIMS: The prognosis of hepatocellular carcinoma (HCC) is influenced by various factors. This study aimed to develop and validate a novel machine learning (ML)-based risk score to stratify HCC patients into prognostic groups and compare its predictive accuracy with conventional staging systems and biomarkers.
METHODS: This retrospective study included 4038 HCC patients diagnosed between 2012 and 2023. Patients were randomly divided into training (n = 2827) and validation (n = 1211) cohorts in a 7:3 ratio. Prognostic factors for overall survival were identified using conventional Cox proportional hazards models and ML-based least absolute shrinkage and selection operator Cox regression. Variables significant in both methods were incorporated into a risk score, with each parameter scaled from 0 to 100, based on multivariable Cox regression coefficients. Patients were categorized into 3 risk groups using the 33rd and 66th percentiles.
RESULTS: After a median follow-up of 42 months (interquartile range: 33.6-50.4), 1931 deaths occurred, with a 5-year overall survival rate of 26.0%. Eleven significant prognostic variables were identified: age, maximum tumor size, extrahepatic metastasis, macrovascular invasion, serum albumin, bilirubin, creatinine, aspartate aminotransferase, lymphocyte-to-monocyte ratio, alpha-fetoprotein level, and treatment modality. The CATS-BIT-ME score demonstrated high predictive accuracy for overall survival (area under the receiver operating characteristic curve: 0.800) and effective risk stratification within the 5 years (area under the receiver operating characteristic curve range: 0.852-0.884). It outperformed 13 conventional staging systems and biomarkers in prognostic precision.
CONCLUSION: The ML-based CATS-BIT-ME score provides an accurate and reliable tool for stratifying HCC patients into distinct prognostic groups, enhancing personalized disease management and clinical decision-making.