Imaging of Lung Cancer Staging: TNM 9 Updates

被引:3
|
作者
Erasmus, Lauren T. [1 ]
Strange, Chad D. [2 ]
Ahuja, Jitesh [2 ]
Agrawal, Rishi [2 ]
Shroff, Girish S. [2 ]
Marom, Edith M. [3 ]
Truong, Mylene T. [1 ,2 ]
机构
[1] Baylor Coll Med, Dept Radiat Oncol, Houston, TX USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Thorac Imaging, Houston, TX USA
[3] Chaim Sheba Med Ctr, Dept Radiol, Tel Aviv, Israel
关键词
POSITRON-EMISSION-TOMOGRAPHY; FORTHCOMING 8TH EDITION; INTERNATIONAL ASSOCIATION; COMPUTED-TOMOGRAPHY; PROJECT PROPOSALS; F-18-FDG PET/CT; LYMPH-NODES; CT; CLASSIFICATION; NSCLC;
D O I
10.1053/j.sult.2024.07.005
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Imaging plays a key role in clinical staging of lung cancer and guiding therapy. A thorough understanding of the staging system including the nomenclature and updates is necessary to tailor treatment plans and optimize patient care. The 9th edition of the Tumor, Node, Metastasis staging system for lung cancer has no changes for T classification and subdivides N2 and M1c categories. In nodal staging, N2 splits into N2a, ipsilateral mediastinal single station involvement and N2b, ipsilateral mediastinal multiple stations involvement. In the staging of multiple extrathoracic metastases, M1c splits into M1c1, multiple extrathoracic metastases in one organ system and M1c2, multiple extrathoracic metastases in multiple organ systems. Awareness of the proposed changes in TNM-9 staging classification is essential to provide methodical and accurate imaging interpretation. Semin Ultrasound CT MRI 45: 410-419 (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:410 / 419
页数:10
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