In this paper, we propose a new method for automated delineation of tumor boundaries in whole-body PET/CT by jointly using information from both PET and diagnostic CT images. Our method takes advantage of initial robust hot spot detection and segmentation performed in PET to provide a conservative tumor structure delineation. Using this estimate as initialization, a model for tumor appearance and shape in corresponding CT structures is learned and the model provides the basis for classifying each voxel to either lesion or background class. This CT classification is then probabilistically integrated with PET classification using the joint likelihood ratio test technique to derive the final delineation. More accurate and reproducible tumor delineation is achieved as a result of such multi-modal tumor delineation, without additional user intervention. The method is particular useful to improve the PET delineation result when there are clear contrast edges in CT between tumor and healthy tissue, and to enable CT segmentation guided by PET when such contrast difference is absent in CT.