人工智能预测代谢相关脂肪变性肝病患者发生肝细胞癌的风险：306例患者预测模型的开发和验证

IF 4.4 3区医学 Q2 GASTROENTEROLOGY & HEPATOLOGY

Annals of hepatology Pub Date : 2025-09-01 DOI:10.1016/j.aohep.2025.101995

Giovane Carvalho Viola , Rodolfo Carvalho Viola , Regiane Alencar , Renato Altikes , Claudia Tani , Lisa Saud , Mario Pessoa , Aline Chagas , Claudia Oliveira

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引用次数: 0

摘要

目的评估基于常规临床和实验室数据的人工智能（AI）模型在预测代谢相关脂肪变性肝病（MASLD）患者发生肝细胞癌（HCC）风险方面的准确性。我们的目标是创建并验证一种工具，以支持风险分层，并促进对高危个体的早期监测。材料和方法本研究是一项回顾性病例对照研究，包括306例MASLD患者，分为肝癌组（129例），经组织病理学标准或LI-RADS分类确诊，对照组（177例）。收集的变量包括年龄、体重指数、合并症（糖尿病、血脂异常、是否存在门脉高压）、血清实验室参数：天冬氨酸转氨酶（AST）、丙氨酸转氨酶（ALT）、白蛋白、肌酐、血小板、胆固醇（HDL、LDL和甘油三酯），以及无创指标：中性粒细胞与淋巴细胞比值（NLR）、FIB-4和AST/ALT比值。采用XGBoost （Extreme Gradient Boosting）人工智能算法，将数据集随机分为训练队列（80%）和内部验证队列（20%），以开发和评估模型的性能。结果AI模型对HCC具有较高的鉴别能力，ROC曲线下面积（AUC-ROC）为0.9，灵敏度为90.9%，特异性为84.3%。HCC的最强预测因子是血清肌酐，其次是门脉高压、NLR升高和LDL水平。结论人工智能驱动的模型使用广泛可用的临床和实验室参数开发，在识别高风险的MASLD患者发展为肝细胞癌方面表现出色。通过实现早期和具有成本效益的风险分层，该工具可以支持有针对性的监测策略，并改善现实世界肝病学实践中的临床决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

ARTIFICIAL INTELLIGENCE IN PREDICTING THE RISK OF HEPATOCELLULAR CARCINOMA IN PATIENTS WITH METABOLICALLY ASSOCIATED STEATOTIC LIVER DISEASE: DEVELOPMENT AND VALIDATION OF A PREDICTIVE MODEL IN 306 PATIENTS

Introduction and Objectives

To evaluate the accuracy of an artificial intelligence (AI) model, based on routine clinical and laboratory data, in predicting the risk of developing hepatocellular carcinoma (HCC) in patients with Metabolically Associated Steatotic Liver Disease (MASLD). Our aim was to create and validate a tool to support risk stratification and facilitate early surveillance of high-risk individuals.

Materials and Methods

This was a retrospective case-control study including 306 MASLD patients divided into an HCC group (129 patients), with diagnosis confirmed by histopathological criteria or LI-RADS classification, and a control group (177 patients). Collected variables included age, body mass index, comorbidities (diabetes mellitus, dyslipidemia, presence of portal hypertension), and serum laboratory parameters: aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin, creatinine, platelets, cholesterol (HDL, LDL, and triglycerides), and non-invasive indices: neutrophil-to-lymphocyte ratio (NLR), FIB-4, and AST/ALT ratio. The XGBoost (Extreme Gradient Boosting) AI algorithm was implemented, and the dataset was randomly split into a training cohort (80%) and an internal validation cohort (20%) to develop and assess the model’s performance.

Results

The AI model demonstrated high discriminative ability for HCC, achieving an area under the ROC curve (AUC-ROC) of 0.9, with a sensitivity of 90.9% and specificity of 84.3%. The strongest predictors of HCC were serum creatinine, followed by the presence of portal hypertension, elevated NLR, and LDL levels.

Conclusions

The AI-driven model, developed using widely available clinical and laboratory parameters, demonstrated excellent performance in identifying MASLD patients at high risk of developing hepatocellular carcinoma. By enabling early and cost-effective risk stratification, this tool may support targeted surveillance strategies and improve clinical decision-making in real-world hepatology practice.

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来源期刊

Annals of hepatology 医学-胃肠肝病学

CiteScore

7.90

自引率

2.60%

发文量

183

审稿时长

4-8 weeks

期刊介绍： Annals of Hepatology publishes original research on the biology and diseases of the liver in both humans and experimental models. Contributions may be submitted as regular articles. The journal also publishes concise reviews of both basic and clinical topics.