医学
肺癌
肺癌分期
放射科
TNM分期系统
阶段(地层学)
肺
癌症
肿瘤科
内科学
肿瘤分期
纵隔镜检查
生物
古生物学
作者
Brett W. Carter,John P. Lichtenberger,Marcelo K. Benveniste,Patricia M. de Groot,Carol C. Wu,Jeremy J. Erasmus,Mylene T. Truong
出处
期刊:Radiographics
[Radiological Society of North America]
日期:2018-03-01
卷期号:38 (2): 374-391
被引量:77
标识
DOI:10.1148/rg.2018170081
摘要
Lung cancer remains the leading cause of cancer-related mortality worldwide. To formulate effective treatment strategies and optimize patient outcomes, accurate staging is essential. Lung cancer staging has traditionally relied on a TNM staging system, for which the International Association for the Study of Lung Cancer (IASLC) has recently proposed changes. The revised classification for this eighth edition of the TNM staging system (TNM-8) is based on detailed analysis of a new large international database of lung cancer cases assembled by the IASLC for the purposes of this project. Fundamental changes incorporated into TNM-8 include (a) modifications to the T classification on the basis of 1-cm increments in tumor size; (b) grouping of lung cancers that result in partial or complete lung atelectasis or pneumonitis; (c) grouping of tumors with involvement of a main bronchus irrespective of distance from the carina; (d) reassignment of diaphragmatic invasion in terms of T classification; (e) elimination of mediastinal pleural invasion from the T classification; and (f) subdivision of the M classification into different descriptors on the basis of the number and site of extrathoracic metastases. In response to these revisions, established stage groups have been modified, and others have been created. In addition, recommendations for classifying patterns of disease that result in multiple sites of pulmonary involvement, including multiple primary lung cancers, lung cancers with separate tumor nodules, multiple ground-glass/lepidic lesions, and consolidation, as well as recommendations for lesion measurement, are addressed. Understanding the key revisions introduced in TNM-8 allows radiologists to accurately stage patients with lung cancer and optimize therapy. ©RSNA, 2018
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