This paper is one of the first steps towards the development of a mass-screening tool, well-suited for quantizing the extend of calcific deposits in the lumbar aorta, which should deliver reliable and easily reproducible data. The major problem is that non-calcified parts of the aorta are not visible on conventional x-ray images. We investigate whether or not it is possible to predict the location of the lumbar aorta, using the first four lumbar vertebrae as prior. We build a conditional probabilistic model from 90 manually annotated datasets. Using this model we made inferences on the position of the aortic walls given the position and shape of the four vertebrae.Of particular interest is the performance of the probabilistic model in comparison to the mean aortic shape. Due to the fact that our data set for this particular study only contained 90 hand-annotated images, we evaluated the model using the "leave-one-out" method. The resulting distance from the predicted to the actual aorta was then compared to the distance from the mean aorta to the actual aorta.The obtained results are encouraging; our conditional model provides results that are up to 38% better than the prediction using only the mean shape, and yields an overlap index of 0.89, whereas the mean shape only produces 0.83.