Frank C Detterbeck1, Kari Chansky2, Patti Groome3, Vanessa Bolejack2, John Crowley2, Lynn Shemanski2, Catherine Kennedy4, Mark Krasnik5, Michael Peake6, Ramón Rami-Porta7. 1. Department of Surgery, Yale University, New Haven, Connecticut. Electronic address: frank.detterbeck@yale.edu. 2. Cancer Research And Biostatistics, Seattle, Washington. 3. Queen's Cancer Research Institute, Kingston, Ontario, Canada. 4. University of Sydney, Strathfield Private Hospital Campus, Strathfield, New South Wales, Australia. 5. Gentofte University Hospital, Copenhagen, Denmark. 6. University of Leicester, Leicester, United Kingdom. 7. Thoracic Surgery Service, Hospital Universitari Mutua Terrassa and Centros de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES) Lung Cancer Group, Terrassa, Barcelona, Spain.
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.
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.
Authors: Brandilyn A Peters; Richard B Hayes; Chandra Goparaju; Christopher Reid; Harvey I Pass; Jiyoung Ahn Journal: Cancer Epidemiol Biomarkers Prev Date: 2019-02-07 Impact factor: 4.254