Suppr超能文献

肝细胞癌中的患者分层:对治疗选择的影响。

Patient stratification in hepatocellular carcinoma: impact on choice of therapy.

作者信息

Papaconstantinou Dimitrios, Hewitt D Brock, Brown Zachary J, Schizas Dimitrios, Tsilimigras Diamantis I, Pawlik Timothy M

机构信息

Third Department of Surgery, Attikon University Hospital,Athens, Greece.

Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States.

出版信息

Expert Rev Anticancer Ther. 2022 Mar;22(3):297-306. doi: 10.1080/14737140.2022.2041415. Epub 2022 Feb 21.

Abstract

INTRODUCTION

HCC comprises roughly 60 to 80% of all primary liver cancers and exhibits wide geographical variability. Appropriate treatment allocation needs to include both patient and tumor characteristics.

AREAS COVERED

Current HCC classification systems to guide therapy are either liver function-centric and evaluate physiologic liver function to guide therapy or prognostic stratification classification systems broadly based on tumor morphologic parameters, patient performance status, and liver reserve assessment. This review focuses on different classification systems for HCC, their strengths, and weaknesses, as well as the use of artificial intelligence in improving prognostication in HCC.

EXPERT OPINION

Future HCC classification systems will need to incorporate clinic-pathologic data from a multitude of sources and emerging therapies to develop patient-specific treatment plans targeting a patient's unique tumor profile.

摘要

引言

肝细胞癌(HCC)约占所有原发性肝癌的60%至80%,且存在广泛的地域差异。合理的治疗分配需要综合考虑患者和肿瘤的特征。

涵盖领域

当前用于指导治疗的HCC分类系统,要么是以肝功能为中心,评估生理肝功能以指导治疗,要么是广泛基于肿瘤形态学参数、患者体能状态和肝储备评估的预后分层分类系统。本综述重点关注HCC的不同分类系统、它们的优缺点,以及人工智能在改善HCC预后评估中的应用。

专家观点

未来的HCC分类系统需要整合来自多种来源的临床病理数据和新兴疗法,以制定针对患者独特肿瘤特征的个性化治疗方案。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验