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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 (05) : 702 -710. doi: 10.3877/cma.j.issn.2095-3232.2026.05.004

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胆道恶性肿瘤预后预测模型：现状、挑战与未来展望
马煜博, 李起, 耿智敏†()   
  1. 710061　西安交通大学第一附属医院肝胆外科
  • 收稿日期:2026-04-12 出版日期:2026-10-10
  • 通信作者: 耿智敏
  • 基金资助:
    国家自然科学基金(62076194); 陕西省重点研发计划(2025SF-YBXM-386)

Prognostic models of biliary tract cancer: present, challenges and future prospects

Yubo Ma, Qi Li, Zhimin Geng†()   

  1. Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
  • Received:2026-04-12 Published:2026-10-10
  • Corresponding author: Zhimin Geng
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马煜博, 李起, 耿智敏. 胆道恶性肿瘤预后预测模型：现状、挑战与未来展望[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(05): 702-710.

Yubo Ma, Qi Li, Zhimin Geng. Prognostic models of biliary tract cancer: present, challenges and future prospects[J/OL]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(05): 702-710.

胆道恶性肿瘤（BTC）包括胆囊癌和胆管癌，具有早期诊断困难、侵袭性强，淋巴结转移率高等特点，预后较差。根治性切除手术是BTC有效的治疗手段，但术后高复发率严重影响了患者的长期生存。BTC预后预测模型可通过预后评估、精准识别高复发风险人群以提升精准化诊疗水平，对于手术治疗、新辅助治疗、辅助治疗及随访监测等临床治疗决策选择具有重要指导价值，有助于改善患者生存质量及延长生存期。近年来，随着学科交叉融合发展，在传统方法基础上，基于机器学习、影像组学等技术构建的BTC预后预测模型得到不断发展，初步显示出一定优势。本文将剖析其发展现状与挑战，为该领域的临床实践与研究提供参考。

关键词: 胆道恶性肿瘤, 胆管癌, 胆囊癌, 预测模型, 预后, 人工智能

Biliary tract cancer (BTC), including gallbladder cancer and cholangiocarcinoma, is characterized by difficulty in early diagnosis, strong invasiveness, high lymph node metastasis rate and poor prognosis. Radical resection is an effective treatment for BTC, whereas high postoperative recurrence rate severely shortens the long-term survival of BTC patients. Prognostic models can improve the levels of precision diagnosis and treatment through prognostic evaluation and precise identification of populations with high recurrence risk. These models have important guiding values for clinical treatment decision-making such as surgery, neoadjuvant therapy, adjuvant therapy and follow-up monitoring, which contribute to elevating the quality of life and prolonging the survival of BTC patients.With recent developments of interdisciplinary integration, on the basis of traditional methods, prognostic models for BTC based on machine learning, radiomics and other technologies have been continuously developed, showcasing certain advantages. In this article, development status and challenges in prognostic models for BTC were illustrated, aiming to provide reference for clinical practice and research in this field.

Key words: Biliary tract cancer, Cholangiocarcinoma, Gallbladder cancer, Prediction model, Artificial intelligence, Prognosis
表1 不同列线图模型在BTC预后评估中的应用
年份 第一作者 研究指标 病种 纳入指标 模型准确性 优势
2013 Wang[28] OS iCCA CEA、CA19-9、肿瘤直径和数量、血管侵犯、淋巴结转移、直接侵袭和局部肝外转移 C-index：0.74/0.75 准确性优于AJCC第6版和第7版TNM分期(0.65)；Nathan(0.64)、日本癌症肝癌研究组(0.64)和Okabayashi分期(0.67)
2014 Hyder[29] OS iCCA 年龄、肿瘤大小、多发性肿瘤、肝硬化、淋巴结转移和大血管浸润 C-index：0.699/0.706 东西方多中心研究, 准确性优于TNM分期系统(0.54/0.59)
2018 Liang[30] RFS iCCA MRI影像特征与TNM临床分期 AUC：0.90/0.86 结合影像组学特征和临床分期的列线图准确性优于单一影像组学列线图(0.82/0.77)
2022 Perez[31] RFS dCCA 淋巴结比率、神经浸润、切缘状态和分化程度 C-index：0.8 基于淋巴结比率的列线图可为切除dCCA的患者提供准确的预后评估
2022 Zhao[23] OS pCCA/dCCA 年龄、TNM分期、病理分级、淋巴结转移、辅助治疗、肿瘤大小、肿瘤数量和婚姻状况 C-index：0.785/0.776 优于传统TNM分期系统(低于0.65)
2022 Li[32] OS/RFS iCCA CA19-9、肿瘤大小、血管侵犯、微血管浸润及N分期 C-index：0.777/0.716 决策曲线分析表明该模型的预测能力优于TNM分期, 可用于筛查可从辅助化疗中获益的患者
2023 沈泽锋[33] OS iCCA CA19-9、CA125、中转开腹、淋巴结转移 C-index：0.705/0.714 该研究为国内多中心研究, 筛选了腹腔镜肝切除术后远期预后的独立危险因素并构建了预测模型
2024 Yin[34] OS GBC CEA、白蛋白胆红素比值(ALBI)、老年营养风险指数、肿瘤T分期和N分期 C-index：0.793 性能优于既往类似研究模型, 具有良好的预测能力和临床实用性
表2 不同机器学习模型在BTC预后评估中的应用
年份 第一作者 研究指标 病种 算法数目 最优算法 效果 优势
2018 汤朝晖[36] OS GBC 1 BN BN在胆囊癌预后预测中的初步尝试, 模型准确度达74.86% 初步证明了BN在GBC生存预测中的价值
2020 Wu[37] OS GBC 1 BN 内外部验证集的AUC分别达84.14%和76.46%, 准确率分别为75.65%和66.88% 优于基于Cox回归的列线图(78.22%/70.19%, 72.17%/60.25%)
2021 Ji[38] OS/RFS BTC 3 GBM OS和RFS的C-index分别达0.776~0.816、0.741~0.781 性能优于TNM分期系统(C-index<0.7)
2022 Chang[39] OS GBC 5 SVM 敏感性为0.9186, 特异性为0.8622, 准确性达94.94% 优于传统算法, 兼顾高敏感性、高特异性及高准确性
2023 Alaimo[40] RFS iCCA 3 RF 训练集与验证集AUC分别为0.904和0.779 优于SVM与逻辑回归模型
2024 Wang[41] RFS pCCA 4 RF 训练集与验证集AUC分别为0.983和0.952 优于其他传统模型(训练集AUC均<0.95, 测试集AUC均<0.90)
2024 Perez[42] RFS dCCA 1 Lasso-Cox 内部验证与外部验证的AUC分别为92.4%和91.5% 优于传统nomogram模型(AUC：0.65~0.78)
2024 Catalano[43] RFS GBC 4 RF 训练集与验证集AUC分别为0.764和0.753 高、低风险组间生存差异显著, 开发的在线计算器可辅助术后分层及治疗
2025 Janczewski[44] OS BTC 2 RF 内部验证集C-index为0.74, AUC为0.83 优于传统TNM分期系统(C-index：0.64, AUC：0.68)
2025 Wang[45] OS iCCA 6 XGB 训练集与验证集AUC分别为0.755和0.714 基于营养指标和临床病理变量的机器学习模型可有效预测OS
2025 Kawashima[46] VER pCCA 1 XGB 训练集和验证集C-index分别为0.74和0.77 开发的在线VER预测模型可辅助临床评估
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