Gastro hep advances · 2026
CATS-BIT-ME Score: A Machine Learning and Conventional Risk Model for Prognosis in Hepatocellular Carcinoma.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ho Chun-Ting, Chia-Hui Tan Elise, Lee Pei-Chang, Lee I-Cheng, Luo Jiing-Chyuan, Huang Yi-Hsiang, Huo Teh-Ia, Hou Ming-Chih, Wu Jaw-Ching, Su Chien-Wei
Аннотация
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.
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.