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Comparative Study
. 2006 Jun;81(6):1958-62; discussion 1962.
doi: 10.1016/j.athoracsur.2005.12.054.

Stage IB nonsmall cell lung cancers: are they all the same?

Affiliations
Comparative Study

Stage IB nonsmall cell lung cancers: are they all the same?

David R Jones et al. Ann Thorac Surg. 2006 Jun.

Abstract

Background: There is renewed interest in adjuvant chemotherapy after complete resection of nonsmall cell lung cancer, including stage IB (T2N0) cancers. Given the heterogeneity of the T2 classification, we hypothesize that there are survival differences in patients with stage IB NSCLC based on specific histopathologic tumor characteristics.

Methods: A retrospective evaluation of 119 consecutive patients from 1999 to 2004 with a pathologic diagnosis of T2N0 nonsmall cell lung cancer was performed. Patient follow-up was 97%. Overall survival and disease-free survival rates were calculated by the Kaplan-Meier method. Univariate analysis was performed using the log rank test and multivariate analysis by Cox's proportional hazard model. Data were significant if p < 0.05.

Results: The 4-year overall survival and disease-free survival rates were 62% and 60%, respectively. The local and distant recurrence rates were 5% and 18%, respectively. Tumor size (p = 0.001), histologic grade (p = 0.002), the Eastern Cooperative Oncology Group performance status (p = 0.002), angioinvasion (p = 0.03), and visceral pleural involvement (p = 0.02) were predictors of overall survival by univariate analysis. Multivariate analysis demonstrated increasing tumor size (1.26 [95% confidence intervals 1.12, 1.64]) and histologic grade (4.05 [95% confidence intervals 1.38, 11.90]) to be significant independent predictors of a worse overall survival. The 4-year survival of patients without any of these variables was 89% compared with 56% if one or more of these factors were present (p = 0.03).

Conclusions: There is significant heterogeneity in the T2N0 class of nonsmall cell lung cancer. Risk stratification using specific histopathologic variables may help determine which patients will benefit most from adjuvant therapy.

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