Accurate lung nodule localization during video-assisted thoracic surgery (VATS) for the treatment of early stage lung cancer is a surgical challenge. Recently, a new minimally invasive approach for nodule localization during VATS has been proposed, which consists in compensating by a biomechanical model the very large lung deformations occurring before and during surgery. This estimation of the deformations allows to transfer the position of the nodule visible on the preoperative CT to an acquisition of the lung performed during the operation using a Cone-Beam CT scanner (CBCT 2). But, in this approach, an additional CBCT acquisition (CBCT 1) must also be acquired just after the patient is placed in the operative position in order to estimate the deformations due to the change of the patient's position, from supine during the CT acquisition to lateral decubitus in the operating room. Our goal is to simplify this procedure and thus reduce the radiation dose to the patient. To this end, we propose to improve this solution by replacing the CBCT 1 acquisition by a model allowing to predict these deformations. This model is defined using the lung state information from CBCT 2 and a general statistical motion model built from the position change deformations already observed on other patients. We have data from 17 patients. The method is evaluated with a leave-one-out cross-validation on its ability to reproduce the observed deformations. The method reduces the average prediction error from 12.14 mm without prediction to 8.12 mm for an average prediction, and finally to 6.33 mm for a prediction with our model fitted to CBCT 2 only.