Predicting the survival of cancer patients is of high importance for the medical community, e.g. for evaluating therapy strategies. This study is based on lung cancer data retrieved from seven German cancer registries according to the German basic oncology dataset. After data integration and preprocessing, we predicted the survival for 6, 12, 18 and 24 months respectively using a gradient boosting algorithm. To gain insight into the decision process of the models, we identified the features that have a high impact on patient survival using permutation feature importance scores as explainability metric. They show that age at diagnosis as well as the presence of distant metastases are key factors for long-term survival. The found factors can be used in a next step for multi-variate survival analysis.