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BACKGROUND: We have recently shown the prognostic value of growth pattern classification in non-small cell lung cancer. The aim of this study is to validate the hypothesis that these growth patterns have a distinct angiogenic and proliferative profile. METHODS: Hematoxylin-eosin stained tissue sections of 239 patients with non-small cell lung cancer were classified into growth patterns. One representative tissue section per patient was double immunostained with CD34 and Ki-67 antibodies. Endothelial cell proliferation fraction, tumor cell proliferation fraction, microvessel density, and Chalkley count were assessed at the invading front and the center of the selected tumor section. RESULTS: According to the growth pattern classification, 161 patients (67.4%) had a destructive, 33 (13.8%) a papillary, and 45 (18.8%) an alveolar growth pattern. There were significant differences in endothelial cell proliferation fraction (p < 0.001), tumor cell proliferation fraction (p < 0.001), microvessel density (p < 0.001), and Chalkley count (p < 0.001) between the growth patterns. Multiple Cox regression analysis showed that a low endothelial cell proliferation fraction was consistently an independent prognostic factor for overall poor (hazard ratio = 0.93; confidence interval: 0.88 to 0.97, p = 0.002) and disease-free survival (hazard ratio = 0.94; confidence interval: 0.89 to 0.98, p = 0.007). CONCLUSIONS: Growth patterns have a distinct angiogenic and proliferative profile. In non-small cell lung cancer, a low degree of angiogenesis (a low endothelial cell proliferation fraction) is associated with poor prognosis.

Original publication




Journal article


Ann Thorac Surg

Publication Date





395 - 405


Adult, Aged, Biopsy, Needle, Carcinoma, Non-Small-Cell Lung, Cell Growth Processes, Cell Proliferation, Cohort Studies, Disease-Free Survival, Female, Humans, Immunohistochemistry, Lung Neoplasms, Male, Middle Aged, Neoplasm Invasiveness, Neoplasm Recurrence, Local, Neovascularization, Pathologic, Probability, Prognosis, Proportional Hazards Models, Retrospective Studies, Risk Assessment, Sensitivity and Specificity, Statistics, Nonparametric, Survival Analysis