• Mamoru Takahashi, Yoshiki Shigematsu, Makoto Ohta, Hironobu Tokumasu, Tadashi Matsukura, and Takashi Hirai.
• Department of Chest Surgery, Fukui Red Cross Hospital, Fukui, Japan. Electronic address: mt10947@yahoo.co.jp.
• J. Thorac. Cardiovasc. Surg. 2014 Jan 1; 147 (1): 54-9.

ObjectivesThe International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) have collaborated to propose a new pathologic classification of lung adenocarcinoma. In this classification, noninvasiveness and invasiveness have been newly defined for lung adenocarcinoma. The aims of this study were to validate the prognostic significance of tumor invasiveness as defined by the new IASLC/ATS/ERS classification and to assess the relationship between pathologic invasiveness and radiologic findings in pathologic stage IA lung adenocarcinoma.MethodsWe retrospectively reviewed 123 consecutive patients with pathologic stage IA lung adenocarcinoma. Pathologic data were classified according to the new IASLC/ATS/ERS classification. The following radiologic parameters were assessed using thin-section computed tomography: the ground-glass opacity ratio, tumor disappearance rate, and consolidation diameter.ResultsThere were 54 noninvasive and 69 invasive adenocarcinomas. Five-year overall survival rates for noninvasive adenocarcinoma and invasive adenocarcinoma were 100% and 78.4%, respectively; this difference was statistically significant (P < .01), indicating the prognostic value of this classification. Receiver operating characteristic curves of the ground-glass opacity ratio, tumor disappearance rate, and consolidation diameter identified the optimal cut-off values for predicting the presence of invasive tumors as 50%, 75%, and 10 mm, respectively.ConclusionsWe found that by using the new IASLC/ATS/ERS classification, histologic subtypes of pathologic stage IA lung adenocarcinoma with prognostic value could be identified. Tumor invasiveness of lung adenocarcinoma as defined by this classification can be predicted by evaluating the ground-glass opacity ratio, tumor disappearance rate, and consolidation diameter on thin-section computed tomography.Copyright © 2014 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.

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