Abstract: Purpose: To prove the feasibility of establishing a whole-body organ dose database platform for radiotherapy treatment planning purposes. Methods: The automatic segmentation software DeepViewer^® that is based on deep learning was used to delineate organs on the whole-body CT dataset of a patient from the First Affiliated Hospital of Zhengzhou University. The GPU-accelerated Monte Carlo software, ARCHER, was used to calculate the dose distributions at the voxel level for an IMRT treatment plan. Finally the Lyman Kutcher Burman (LKB) model was used to calculate the normal tissue complication probability (NTCP) following the QUANTEC recommendation. Results: For this case, the whole-body organ dose database was established successfully using existing tools including DeepViewer^®, ARCHER and LKB model. It was found that the closer to the target, the greater the organ doses, as expected. The heart was closest to the target, with a dose of 14.11Gy, but its NTCP value was 0.00%. The doses of left and right lung were 3.19 Gy and 1.16 Gy, respectively, but their NTCP values are 2.13% and 1.60%, respectively, which are significant showing that there is a great risk of radiation damage to lungs. For head and neck organs (such as the optic chiasm, optic nerve and eyes) and abdominal organs (such as the rectum, bladder and femoral head ) which are far from the target, their doses were about 9 m Gy and 2 m Gy, respectively. And their NTCP values were approximately 0. Conclusions: This study proves that the establishment of whole-body organ dose database can be realized to yield clinically useful information that can help to carry out dosimetry research about whole-body organ and optimize radiotherapy plan.

whole-body organ dose database; DeepViewer; ARCHER; normal tissue complication probability