Epidemiological investigation is an important approach to assessing the risk of late effects after radiotherapy, and organ dosimetry is a crucial part of such analysis. Computed tomography (CT) images, if available, can be a valuable resource for individualizing the dosimetry, because they describe the specific anatomy of the patient. However, CT images acquired for radiation treatment planning purposes cover only a portion of the body near the target volume, whereas for epidemiology, the interest lies in the more distant normal tissues, which may be located outside the scan range. To address this challenge, we developed a novel method, called the Anatomically Predictive Extension (APE), to extend a partial-body CT image stack using images of a computational human phantom matched to the patient based on their height and weight. To test our method, we created five APE phantoms from chest and abdominal images extracted from the chest-abdomen-pelvis (CAP) CT scans of five patients. Organ doses were calculated for simple chest and prostate irradiations that were planned on the reference computational phantom (assumed patient geometry if no CT images are available), APE phantoms (patient-phantom hybrid given a partial-body patient CT) and full patient CAP CT scans (ground truth). The APE phantoms and patient CAP CT scans resulted in nearly identical dosimetry for those organs that were fully included in the partial-body CT used to construct the APE. The calculated doses to these same organs in the reference phantoms differed by up to 20% and 52% for the chest and prostate cases, respectively. For organs outside the scan coverage, the reference phantom showed, on average, dose differences of 31% (chest case) and 41% (prostate case). For the APE phantoms, these values were 26% (chest) and 17% (prostate). The APE method combines patient and phantom images to improve organ dosimetry both inside and outside the scan range. We intend to use the APE method for estimating dose for organs peripheral to the treatment fields; however, this method is quite generalizable with many potential applications.