Background: Computed tomography (CT) imaging is an important tool to guide further investigation and treatment in patients with lung cancer. For patients with early stage lung cancer, surgery remains an optimal treatment option. Artificial intelligence applied to pretreatment CTs may have the ability to quantify mortality risk and stratify patients for more individualized diagnostic, treatment and monitoring decisions. Methods: A fully automated, end-to-end model was designed to localize the 36cm x 36cm x 36cm space centered on the lungs and learn deep prognostic features using a 3-dimensional convolutional neural network (3DCNN) to predict 5-year mortality risk. The 3DCNN was trained and validated in a 5-fold cross-validation using 2,924 CTs of 1,689 lung cancer patients from 6 public datasets made available in The Cancer Imaging Archive. We evaluated 3DCNN’s ability to stratify stage I & II patients who received surgery into mortality risk quintiles using the Cox proportional hazards model. Results: 260 of the 1,689 lung cancer patients in the withheld validation dataset were diagnosed as stage I or II, received a surgical resection within 6 months of their pretreatment CT and had known 5-year disease and survival outcomes. Based on the 3DCNN’s predicted mortality risk, patients in the highest risk quintile had a 14.2-fold (95% CI 4.3-46.8,p< 0.001) increase in 5-year mortality hazard compared to patients in the lowest risk quintile. Conclusions: Deep learning applied to pretreatment CTs provides personalised prognostic insights for early stage lung cancer patients who received surgery and has the potential to inform treatment and monitoring decisions.