MTANN (Massive Training Artificial Neural Network) is a promising tool, which applied to eliminate false-positive for thoracic CT in recent years. In order to evaluate whether this method is feasible to eliminate false-positive of different CAD schemes, especially, when it is applied to commercial CAD software, this paper evaluate the performance of the method for eliminating false-positives produced by three different versions of commercial CAD software for lung nodules detection in chest radiographs. Experimental results demonstrate that the approach is useful in reducing FPs for different computer aided lung nodules detection software in chest radiographs.