Journal of the National Cancer Institute · 2025
Liver elastography-based risk score for predicting hepatocellular carcinoma risk.
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.
Tian Chan, Ye Chunyan, Guo Haiyan, Lu Kun, Yang Juan, Wang Xiao, Ge Xinyuan, Yu Chengxiao, Lu Jing, Jiang Longfeng, Zhang Qun, Song Ci
Аннотация
BACKGROUND: Liver stiffness measurement (LSM) via vibration-controlled transient elastography accurately assesses fibrosis. We aimed to develop a universal risk score for predicting hepatocellular carcinoma (HCC) development in patients with chronic hepatitis.
METHODS: We systematically selected predictors and developed the risk prediction model (HCC-LSM) in the hepatitis B virus (HBV) training cohort (n = 2251, median follow-up of 3.2 years). The HCC-LSM model was validated in an independent HBV validation cohort (n = 1191, median follow-up of 5.7 years) and a non-viral chronic liver disease (CLD) extrapolation cohort (n = 1189, median follow-up of 3.3 years). An HCC risk score was then constructed based on a nomogram. An online risk evaluation tool Liver Elastography-Based Hepatocellular Carcinoma Risk Score (LEBER) was developed using ChatGPT4.0.
RESULTS: Eight routinely available predictors were identified, with LSM levels showing a significant dose-response relationship with HCC incidence (P < .001 by log-rank test). The HCC-LSM model exhibited excellent predictive performance in the HBV training cohort (C-index = 0.866) and the HBV validation cohort (C-index = 0.852), with good performance in the extrapolation CLD cohort (C-index = 0.769). The model demonstrated significantly superior discrimination compared to 6 previous models across the 3 cohorts. Cut-off values of 87.2 and 121.1 for the HCC-LSM score categorized participants into low-, medium-, and high-risk groups. An online public risk evaluation tool (LEBER; http://ccra.njmu.edu.cn/LEBER669.html) was developed to facilitate the use of HCC-LSM.
CONCLUSION: The accessible, reliable risk score based on LSM accurately predicted HCC development in patients with chronic hepatitis, providing an effective risk assessment tool for HCC surveillance strategies.
METHODS: We systematically selected predictors and developed the risk prediction model (HCC-LSM) in the hepatitis B virus (HBV) training cohort (n = 2251, median follow-up of 3.2 years). The HCC-LSM model was validated in an independent HBV validation cohort (n = 1191, median follow-up of 5.7 years) and a non-viral chronic liver disease (CLD) extrapolation cohort (n = 1189, median follow-up of 3.3 years). An HCC risk score was then constructed based on a nomogram. An online risk evaluation tool Liver Elastography-Based Hepatocellular Carcinoma Risk Score (LEBER) was developed using ChatGPT4.0.
RESULTS: Eight routinely available predictors were identified, with LSM levels showing a significant dose-response relationship with HCC incidence (P < .001 by log-rank test). The HCC-LSM model exhibited excellent predictive performance in the HBV training cohort (C-index = 0.866) and the HBV validation cohort (C-index = 0.852), with good performance in the extrapolation CLD cohort (C-index = 0.769). The model demonstrated significantly superior discrimination compared to 6 previous models across the 3 cohorts. Cut-off values of 87.2 and 121.1 for the HCC-LSM score categorized participants into low-, medium-, and high-risk groups. An online public risk evaluation tool (LEBER; http://ccra.njmu.edu.cn/LEBER669.html) was developed to facilitate the use of HCC-LSM.
CONCLUSION: The accessible, reliable risk score based on LSM accurately predicted HCC development in patients with chronic hepatitis, providing an effective risk assessment tool for HCC surveillance strategies.