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中华肝脏外科手术学电子杂志

中华肝脏外科手术学电子杂志 ›› 2026, Vol. 15 ›› Issue (03) : 326 -336. doi: 10.3877/cma.j.issn.2095-3232.2026.03.006

前沿与争鸣

肝癌多模式诊断研究进展与展望
石亚超1, 魏六木1, 东小鸽1, 樊海宁2,3, 侯立朝2,3, 杜凯豪1, 汪占金1, 薛伟伟1, 刘海刚1, 王展2,3,4,†()   
  1. 1　810000　西宁，青海大学临床医学院
    2　810000　西宁，青海大学附属医院肝胆胰外科
    4　810000　西宁，青海大学附属医院医工结合与转化应用
    3　810000　西宁，青海省包虫病研究重点实验室
  • 收稿日期:2025-10-15 出版日期:2026-06-10
  • 通信作者: 王展
  • 基金资助:
    国家自然科学基金(82160131); 中国科学院“西部之光”青年学者项目

Multi-mode diagnostic strategies for liver cancer

Yachao Shi1, Liumu Wei1, Xiaoge Dong1, Haining Fan2,3, Lichao Hou2,3, Kaihao Du1, Zhanjin Wang1, Weiwei Xue1, Haigang Liu1, Zhan Wang2,3,4,†()   

  1. 1 Clinical Medical College of Qinghai University, Xining 810000, China
    2 Department of Hepatobiliary and Pancreatic Surgery, Qinghai University Affiliated Hospital, Xining 810000, China
    3 Qinghai Province Key Laboratory of Hydatid Disease Research, Xining 810000, China
    4 Department of Medical Engineering and Translational Application, Qinghai University Affiliated Hospital, Xining 810000, China
  • Received:2025-10-15 Published:2026-06-10
  • Corresponding author: Zhan Wang
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引用本文：

石亚超, 魏六木, 东小鸽, 樊海宁, 侯立朝, 杜凯豪, 汪占金, 薛伟伟, 刘海刚, 王展. 肝癌多模式诊断研究进展与展望[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 326-336.

Yachao Shi, Liumu Wei, Xiaoge Dong, Haining Fan, Lichao Hou, Kaihao Du, Zhanjin Wang, Weiwei Xue, Haigang Liu, Zhan Wang. Multi-mode diagnostic strategies for liver cancer[J/OL]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(03): 326-336.

原发性肝癌（肝癌）是全球主要的恶性肿瘤之一，早期诊断和治疗对于提高患者生存率至关重要。近年来，医学影像学、肿瘤标志物和人工智能（AI）技术在肝癌的诊断和管理中发挥了重要作用。影像学技术如超声、CT、MRI和放射性核素成像等已被广泛应用于肝癌的筛查、诊断、分级和治疗评估。肿瘤标志物如AFP、甲胎蛋白异质体（AFP-L3）和PIVKA-Ⅱ等是肝癌诊断中的重要工具，被称为“肝癌三联检”。AI技术，尤其是深度学习和神经网络模型，能够有效地分析医学影像数据，辅助医生进行更准确的诊断。这些技术的综合应用有望进一步提高肝癌的早期诊断率和患者的整体生存率。

关键词: 原发性肝癌, 癌，肝细胞, 影像学检查, 肿瘤标志物, 人工智能

Liver cancer is one of the main malignant tumors worldwide. Early diagnosis and treatment are of significance for improving the survival rate of patients. In recent years, medical imaging, tumor markers and artificial intelligence (AI) technologies have played a critical role in the diagnosis and management of liver cancer. Imaging technologies, such as ultrasound, CT, MRI and radionuclide imaging, have been widely applied in the screening, diagnosis, grading and treatment evaluation of liver cancer. Tumor markers, such as AFP, AFP-L3 and PIVKA-Ⅱ, are important tools in the diagnosis of liver cancer, which are known as "triple detections of liver cancer". AI technologies, especially deep learning and neural network model, can effectively analyze medical imaging data and assist physicians to make more accurate diagnosis. Integrated application of these technologies is expected to further enhance early diagnosis rate and the overall survival of liver cancer patients.

Key words: Primary liver cancer, Carcinoma,hepatocellular, Imaging examinations, Tumor markers, Artificial intelligence
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