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Lung cancer is the most common fatal malignancy in adults worldwide, and non-small-cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of patients with NSCLC. We trained and evaluated LungNet on four independent cohorts of patients with NSCLC from four medical centres: Stanford Hospital (n = 129), H. Lee Moffitt Cancer Center and Research Institute (n = 185), MAASTRO Clinic (n = 311) and Charité – Universitätsmedizin, Berlin (n = 84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3 and 4, respectively. Furthermore, the …