Automatic treatment planning implementation using a database of previously treated patients

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Moore, J.A.^1 ; Evans, K.^1 ; Yang, W.^1 ; Herman, J.^1 ; McNutt, T.^1

Department of Radiation Oncology, Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, United States^1

关键词: Automatic treatment;    Critical structures;    Dose-volume histograms;    Individual structures;    Planning objectives;    Region of interest;    Regions of interest;    Relational Database;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012054/pdf
DOI  :  10.1088/1742-6596/489/1/012054

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Purpose: Using a database of prior treated patients, it is possible to predict the dose to critical structures for future patients. Automatic treatment planning speeds the planning process by generating a good initial plan from predicted dose values. Methods: A SQL relational database of previously approved treatment plans is populated via an automated export from Pinnacle3. This script outputs dose and machine information and selected Regions of Interests as well as its associated Dose-Volume Histogram (DVH) and Overlap Volume Histograms (OVHs) with respect to the target structures. Toxicity information is exported from Mosaiq and added to the database for each patient. The SQL query is designed to ask the system for the lowest achievable dose for a specified region of interest (ROI) for each patient with a given volume of that ROI being as close or closer to the target than the current patient. Results: The additional time needed to calculate OVHs is approximately 1.5 minutes for a typical patient. Database lookup of planning objectives takes approximately 4 seconds. The combined additional time is less than that of a typical single plan optimization (2.5 mins). Conclusions: An automatic treatment planning interface has been successfully used by dosimetrists to quickly produce a number of SBRT pancreas treatment plans. The database can be used to compare dose to individual structures with the toxicity experienced and predict toxicities before planning for future patients.