The objective of the present study was to elaborate a survival model that integrates anatomic factors, according to the 2010 seventh edition of the tumour, node and metastasis (TNM) staging system, with clinical and molecular factors. Pathologic TNM descriptors (group A), clinical variables (group B), laboratory parameters (group C) and molecular markers (tissue microarrays; group D) were collected from 512 early-stage nonsmall cell lung cancer (NSCLC) patients with complete resection. A multivariate analysis stepped supervised learning classification algorithm was used. The prognostic performance by groups was: areas under the receiver operating characteristic curve (C-index): 0.67 (group A), 0.65 (Group B), 0.57 (group C) and 0.65 (group D). Considering all variables together selected for each of the four groups (integrated group) the C-index was 0.74 (95% CI 0.70-0.79), with statistically significant differences compared with each isolated group (from p = 0.006 to p < 0.001). Variables with the greatest prognostic discrimination were the presence of another ipsilobar nodule and tumour size > 3 cm, followed by other anatomical and clinical factors, and molecular expressions of phosphorylated mammalian target of rapamycin (phospho-mTOR), Ki67cell proliferation index and phosphorylated acetyl-coenzyme A carboxylase. This study on early-stage NSCLC shows the benefit from integrating pathological TNM, clinical and molecular factors into a composite prognostic model. The model of the integrated group classified patients with significantly higher accuracy compared to the TNM 2010 staging.