JOURNAL ARTICLE

Validation of the 9th edition of the TNM staging system for non-small cell lung cancer with lobectomy in stage IA–IIIA.

  • Published In: European Journal of Cardio-Thoracic Surgery, 2024, v. 65, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Rang-Rang; Li, Ming-Jun; Peng, Qiao; Huang, Zhi-Ye; Wu, Lei-Lei; Xie, Dong 3 of 3

Abstract

This article focuses on the external validation and comparison of the 9th edition of the tumour–node–metastasis (TNM) staging system for non-small cell lung cancer (NSCLC) using data from 19,193 patients who underwent lobectomy in the Surveillance, Epidemiology, and End Results (SEER) database. The study found that the 9th edition, announced in 2023 by the International Association for the Study of Lung Cancer (IASLC), offers improved differentiation between certain adjacent stages—particularly between stages IB and IIA—compared to the 8th edition, although both editions show near-identical prognostic accuracy for overall survival. Key changes in the 9th edition include subdivision of N2 and M1c categories and reclassification of some stage groups, but predictive performance measured by metrics such as area under the curve (AUC), Akaike information criterion (AIC), Bayesian information criterion (BIC), and consistency index (C-index) remained similar to the previous edition. The study acknowledges limitations including retrospective design, lack of some clinical variables in SEER, and limited generalizability beyond predominantly White U.S. populations.

Additional Information

  • Source:European Journal of Cardio-Thoracic Surgery. 2024/03, Vol. 65, Issue 3, p1
  • Document Type:Article
  • Subject Area:Consumer Health
  • Publication Date:2024
  • ISSN:1010-7940
  • DOI:10.1093/ejcts/ezae071
  • Accession Number:176301054
  • Copyright Statement:Copyright of European Journal of Cardio-Thoracic Surgery is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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