Automated Knowledge-Based Radiation Treatment Planning in Canine and Feline Nasal Tumors

This study aims to develop an automated knowledge-based planning (KBP) system for small animal radiation treatment, specifically targeting nasal tumors. The potential impact of this system on the field of veterinary medicine and radiation therapy is significant, as it can revolutionize the planning process, making it more efficient and less resource-intensive. A total of twenty previous radiation treatment plans were collected to generate an averaged dose-volume histogram (DVH), which was then used to set the optimization parameters for the automated KBP process. The application programming interfaces (API) scripting was used to automate the planning process, including adding beams, optimization, and dose calculation at CT images. To validate the efficiency of the automated system, another set of twenty prior treatment plans was used for comparison. The study evaluated both the time required to generate the plans and the quality of the plans produced by the automated KBP system against the original manual plans. Most of the dose statistics from automated plans were similar to the original plan, such as tumor dose coverage and dose at OARs. The only exception is the dose at the right eye (D_2cc), which is lower in the automated plan compared with the original plan. The automated plans generated by KBP using API scripting demonstrated comparable plan quality to the original plans, especially in terms of tumor coverage and OAR sparing. It also significantly reduced planning time from 33 min to just 5 min compared with manual optimization. This capability is particularly beneficial for high-workload departments with limited medical physicist resources, enabling them to consistently generate high-quality treatment plans.