Biomedical and environmental sciences : BES · 2026
Lasso Regression-based Model for Cross-Sectional Identification of Significant Hepatic Fibrosis in NAFLD LASSO-based identification of hepatic fibrosis in NAFLD.
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.
Deng Wen, Zhang Ya Qin, Cao Wei Hua, Wang Shuo Jie, Wang Shi Yu, Zhang Zi Yu, Li Xin Xin, Yao Lin Mei, Gao Zi Xuan, Wei Xin, Ma Tian Yu, Qiu Dian Ya, Hao Hong Xiao, Xie Yao, Li Ming Hui
Аннотация
OBJECTIVE: Nonalcoholic fatty liver disease (NAFLD) is an increasing global health concern, with liver-related mortality increasing as fibrosis progresses. This study aimed to identify the key determinants and develop a noninvasive model to detect significant hepatic fibrosis.
METHODS: A total of 466 patients with biopsy-confirmed NAFLD were retrospectively analyzed at Beijing Ditan Hospital between 2008 and 2018. The patients were classified into non-significant (S0-1) and significant fibrosis (S2-4) groups. Relevant features were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression to construct a model for the cross-sectional identification of significant fibrosis. Model performance was assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and bootstrap validation.
RESULTS: Of the 466 patients, 112 had significant fibrosis. LASSO regression identified 10 relevant features, and the model achieved an AUC of 0.919 (sensitivity, 83.9%; specificity, 85.3%) with a corrected AUC of 0.907 after bootstrap validation. It outperformed the APRI, FIB-4, and LSM ( P < 0.001), and the DCA confirmed its clinical utility across probability thresholds.
CONCLUSION: The noninvasive model, incorporating demographic, laboratory, and imaging parameters, accurately identified significant hepatic fibrosis in NAFLD and outperformed existing noninvasive scores. This may facilitate interventions and guide personalized management.
METHODS: A total of 466 patients with biopsy-confirmed NAFLD were retrospectively analyzed at Beijing Ditan Hospital between 2008 and 2018. The patients were classified into non-significant (S0-1) and significant fibrosis (S2-4) groups. Relevant features were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression to construct a model for the cross-sectional identification of significant fibrosis. Model performance was assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and bootstrap validation.
RESULTS: Of the 466 patients, 112 had significant fibrosis. LASSO regression identified 10 relevant features, and the model achieved an AUC of 0.919 (sensitivity, 83.9%; specificity, 85.3%) with a corrected AUC of 0.907 after bootstrap validation. It outperformed the APRI, FIB-4, and LSM ( P < 0.001), and the DCA confirmed its clinical utility across probability thresholds.
CONCLUSION: The noninvasive model, incorporating demographic, laboratory, and imaging parameters, accurately identified significant hepatic fibrosis in NAFLD and outperformed existing noninvasive scores. This may facilitate interventions and guide personalized management.