[肝内胆管癌预后评价方法的现状及展望]。

G W Ji, Z G Xu, S Y Cao, K Wang, X H Wang
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引用次数: 1

摘要

肝内胆管癌(ICC)是仅次于肝细胞癌的第二常见的肝脏原发性恶性肿瘤。近年来,其发病率和死亡率在世界范围内有所上升。手术切除是ICC的最佳治疗方式,但总体预后仍然较差。准确的术后预后评估有助于个体化治疗和改善ICC的长期预后。美国癌症联合委员会TNM分期手册是ICC标准化诊断和治疗的依据,但T期和N期的内容有待完善。在常用临床病理参数分析中建立的nomogram模型或评分系统可以提供个体化的预后评估,提高预测的准确性,但在临床应用前还需要更多的研究来验证结果。同时,影像学特征对建立ICC术后预后评价体系具有很大的潜力。基于分子的分类为预后评估以及选择对靶向治疗或免疫治疗敏感的人群提供了准确的保证。因此,在医疗大数据背景下,建立以临床和病理特征为基础,以多学科和多组学相结合为中心的预后评估体系,将有利于提高ICC手术切除后的远期预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Current status and future perspectives on the methods of prognosis evaluation for intrahepatic cholangiocarcinoma].

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary malignant tumor in the liver after hepatocellular carcinoma. Its incidence and mortality rates have increased worldwide in recent years. Surgical resection is the best treatment modality for ICC;however,the overall prognosis remains poor. Accurate evaluation of post operative prognosis allows personalized treatment and improved long-term outcomes of ICC. The American Joint Commission on Cancer TNM staging manual is the basis for the standardized diagnosis and treatment of ICC;however,the contents of stage T and stage N need to be improved. The nomogram model or scoring system established in the analysis of commonly used clinicopathological parameters can provide individualized prognostic evaluation and improve prediction accuracy;however,more studies are needed to validate the results before clinical use. Meanwhile,imaging features exhibit great potential to establish the post operative prognosis evaluation system for ICC. Molecular-based classification provides an accurate guarantee for prognostic assessment as well as selection of populations that are sensitive to targeted therapy or immunotherapy. Therefore,the establishment of a prognosis evaluation system,based on clinical and pathological characteristics and centered on the combination of multidisciplinary and multi-omics,will be conducive to improving the long-term outcomes of ICC after surgical resection in the context of big medical data.

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