The IASLC Lung Cancer Staging Project: Methodology and Validation Used in the Development of Proposals for Revision of the Stage Classification of NSCLC in the Forthcoming (Eighth) Edition of the TNM Classification of Lung Cancer. Issue 9 (September 2016)

• Record Type: Journal Article
• Authors: Detterbeck, Frank C.; Chansky, Kari; Groome, Patti; Bolejack, Vanessa; Crowley, John; Shemanski, Lynn; Kennedy, Catherine; Krasnik, Mark; Peake, Michael; Rami‐Porta, Ramón
• Is Part Of: Journal of thoracic oncology. Volume 11:Issue 9(2016)
• Publication Date: 2016-09
• Subjects: Lung cancer -- NSCLC -- TNM classification -- Lung cancer staging -- Validation -- Prognosis; Chest -- Cancer -- Periodicals; Thoracic Neoplasms -- Periodicals; 616.99494005
• DOI: 10.1016/j.jtho.2016.06.028
• Languages: English
• ISSNs: 1556-0864

Abstract

ABSTRACT : Introduction: : Stage classification provides a consistent language to describe the anatomic extent of disease and is therefore a critical tool in caring for patients. The Staging and Prognostic Factors Committee of the International Association for the Study of Lung Cancer developed proposals for revision of the classification of lung cancer for the eighth edition of the tumor, node, and metastasis (TNM) classification, which takes effect in 2017. Methods: : An international database of 94, 708 patients with lung cancer diagnosed in 1999–2010 was assembled. This article describes the process and statistical methods used to refine the lung cancer stage classification. Results: : Extensive analysis allowed definition of tumor, node, and metastasis categories and stage groupings that demonstrated consistent discrimination overall and within multiple different patient cohorts (e.g., clinical or pathologic stage, R0 or R‐any resection status, geographic region). Additional analyses provided evidence of applicability over time, across a spectrum of geographic regions, histologic types, evaluative approaches, and follow‐up intervals. Conclusions: : An extensive analysis has produced stage classification proposals for lung cancer with a robust degree of discriminatory consistency and general applicability. Nevertheless, external validation is encouraged to identify areas of strength and weakness; a sound validation should have discriminatory ability and be based on an independent data set of adequate size and sufficient follow‐up with enough patients for each subgroup.