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偶然发现的肺结节的处理:当前策略和未来展望。

Management of incidental pulmonary nodules: current strategies and future perspectives.

机构信息

Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea.

Respiratory and Critical Care Division of Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine (SKKU-SOM), Seoul, South Korea.

出版信息

Expert Rev Respir Med. 2020 Feb;14(2):173-194. doi: 10.1080/17476348.2020.1697853. Epub 2019 Dec 3.

Abstract

: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.

摘要

: 检测和描述肺部结节是一个重要的问题,因为这个过程是肺癌管理的第一步。: 我们于 2019 年 5 月 15 日在 PubMed、美国国立卫生研究院国家医学图书馆和美国国家生物技术信息中心进行了文献回顾。介绍了有助于识别可用药基因突变和预测恶性结节预后的 CT 特征。介绍了 MRI 和 PET/CT 的技术进展,以提供有关恶性结节的功能信息。还介绍了各种组织活检技术的进步,这些技术能够对不确定的结节进行分子分析和组织学诊断。总结了放射组学、深度学习 (DL) 技术和人工智能等新技术在区分良恶性结节方面的应用前景。最近,还描述了 CT 偶然发现的实性和部分实性结节的更新管理指南。简要总结了正在积极研究的不确定结节的风险分层和预测模型。: CT 知识的进步使得 CT 特征与基因组改变或肿瘤组织学之间的相关性更好。最近的进展,如 PET/CT、MRI、放射组学和基于深度学习的方法,在肺部结节的特征描述和预后方面显示出了有前景的结果。

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