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Epidermal growth factor receptor (EGFR) tumour genotyping is crucial to guide treatment decisions regarding the use of EGFR tyrosine kinase inhibitors in nonsmall cell lung cancer (NSCLC). However, some patients may not be able to obtain tumour testing, either because tissue is limited and/or tests are not routinely offered. Here, we aimed to build a model-based nomogram to allow for prediction of the presence of EGFR mutations in NSCLC. We retrospectively collected clinical and pathological data on 3,006 patients with NSCLC who had their tumours genotyped for EGFR mutations at five institutions worldwide. Variables of interest were integrated in a multivariate logistic regression model. In the 2,392 non-Asian patients with lung adenocarcinomas, the most important predictors of harbouring EGFR mutation were: lower tobacco smoking exposure (OR 0.41, 95% CI 0.37-0.46), longer time interval between smoking cessation and diagnosis (OR 2.19, 95% CI 1.71-2.80), advanced stage (OR 1.58, 95% CI 1.18-2.13), and papillary (OR 4.57, 95% CI 3.14-6.66) or bronchioloalveolar (OR 2.84, 95% CI 1.98-4.06) histologically predominant subtype. A nomogram was established and showed excellent discriminating accuracy: the concordance index on an independent validation dataset was 0.84. As clinical practices transition to incorporating genotyping as part of routine care, this nomogram could be highly useful to predict the presence of EGFR mutations in lung adenocarcinoma in non-Asian patients when mutational profiling is not available or possible.