Automatic detection of lung nodules from chest CT has been researched intensively over the last decades resulting also in several commercial products. However, solutions are adopted only slowly into daily clinical routine as many current CAD systems still potentially miss true nodules while at the same time generating too many false positives (FP). While many earlier approaches had to rely on rather few cases for development, larger databases become now available and can be used for algorithmic development. In this paper, we address the problem of lung nodule detection via a cascaded SVM classifier. The idea is to sequentially perform two classification tasks in order to select from an extremely large pool of potential candidates the few most likely ones. As the initial pool is allowed to contain thousands of candidates, very loose criteria could be applied during this pre-selection. In this way, the chances that a true nodule is falsely rejected as a candidate are reduced significantly. The final algorithm is trained and tested on the full LIDC/IDRI database. Comparison is done against two previously published CAD systems. Overall, the algorithm achieved sensitivity of 0.859 at 2.5 FP/volume where the other two achieved sensitivity values of 0.321 and 0.625, respectively. On low dose data sets, only slight increase in the number of FP/volume was observed, while the sensitivity was not affected.