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Medical image repositories contain very large amounts of computer tomography (CT) scans. When querying a particular CT scan, the user is often not interested in the complete scan but in a certain region of interest (ROI). Unfortunately, specifying the ROI in terms of scan coordinates is usually not an option because an ROI is usually specified w.r.t. the scan content, e.g. an example region in another scan. Thus, the system usually retrieves the complete scan and the user has to navigate to the ROI manually. In addition to the time to navigate, there is a large overhead for loading and transferring the irrelevant parts of the scan. In this paper, we propose a method for answering ROI queries which are specified by an example ROI in another scan. An important feature of our new approach is that it is not necessary to annotate the query or the result scan before query processing. Since our method is based on image similarity, it is very flexible w.r.t. the size and the position of the scanned region. To answer ROI queries, our new method employs instance-based regression in combination with interpolation techniques for mapping the slices of a scan to a height model of the human body. Furthermore, we propose an efficient search algorithm on the result scan for retrieving the ROI with high accuracy. In the experimental evaluation, we examine the prediction accuracy and the saved I/O costs of our new method on a repository of 2 526 CT scans.