Lung nodule detection, especially ground glass opacity (GGO) detection, in helical computed tomography (CT) images is a challenging Computer-Aided Detection (CAD) task due to the enormous variances in nodules' volumes, shapes, appearances, and the structures nearby. Most of the detection algorithms employ some efficient candidate generation (CG) algorithms to spot the suspicious volumes with high sensitivity at the cost of low specificity, e.g. tens even hundreds of false positives per volume. This paper proposes a learning based method to reduce the number of false positives given by CG based on a new general 3D volume shape descriptor. The 3D volume shape descriptor is constructed by concatenating spatial histograms of gradient orientations, which is robust to large, variabilities in intensity levels, shapes, and appearances. The proposed method achieves promising performance on a difficult mixture lung nodule dataset with average 8.1% detection rate and 4.3 false positives per volume.