PURPOSE: The objective of this study was to create a workflow for the automation and standardization of treatment plan generation and evaluation using an application programming interface (API) to access data from a commercial treatment planning system (Varian Medical Systems, Inc, Palo Alto, CA). METHODS AND MATERIALS: The automation workflow begins with converting electronic patient-specific physician treatment planning orders that specify demographics, simulation instructions, and dosimetric objectives for targets and organs at risk into XML files. These XML files are used to generate standard contour names, beam, and patient-specific intensity modulated radiation therapy (IMRT) optimization templates to be executed in a commercial treatment planning system (TPS) by the user. A set of computer programs have been developed to provide quality control (QC) reports that verify demographic information in the TPS against the treatment planning orders, ensure the existence and proper naming of organs at risk, and generate patient-specific plan evaluation reports that provide real-time feedback on the concordance of an active treatment plan to the physician-specified treatment planning goals. RESULTS: A workflow for lung IMRT was chosen as a test scenario. Contour, beam, and patient-specific IMRT optimization templates were automatically generated from the physician treatment planning orders and loaded into the planning system. The QC reports were developed for lung IMRT, including the option of patient-specific modifications to the standard templates. The API QC reporting includes a dynamic program that runs in parallel to the TPS during the planning process, providing real-time feedback as to whether physician-specified treatment plan parameters have improved or worsened from previous iterations. CONCLUSIONS: User-created computer programs to access information in the TPS database by means of a commercial TPS API enable automation and standardization of treatment plan generation and evaluation.
PURPOSE: The objective of this study was to create a workflow for the automation and standardization of treatment plan generation and evaluation using an application programming interface (API) to access data from a commercial treatment planning system (Varian Medical Systems, Inc, Palo Alto, CA). METHODS AND MATERIALS: The automation workflow begins with converting electronic patient-specific physician treatment planning orders that specify demographics, simulation instructions, and dosimetric objectives for targets and organs at risk into XML files. These XML files are used to generate standard contour names, beam, and patient-specific intensity modulated radiation therapy (IMRT) optimization templates to be executed in a commercial treatment planning system (TPS) by the user. A set of computer programs have been developed to provide quality control (QC) reports that verify demographic information in the TPS against the treatment planning orders, ensure the existence and proper naming of organs at risk, and generate patient-specific plan evaluation reports that provide real-time feedback on the concordance of an active treatment plan to the physician-specified treatment planning goals. RESULTS: A workflow for lung IMRT was chosen as a test scenario. Contour, beam, and patient-specific IMRT optimization templates were automatically generated from the physician treatment planning orders and loaded into the planning system. The QC reports were developed for lung IMRT, including the option of patient-specific modifications to the standard templates. The API QC reporting includes a dynamic program that runs in parallel to the TPS during the planning process, providing real-time feedback as to whether physician-specified treatment plan parameters have improved or worsened from previous iterations. CONCLUSIONS: User-created computer programs to access information in the TPS database by means of a commercial TPS API enable automation and standardization of treatment plan generation and evaluation.
Authors: Eric Ford; Leigh Conroy; Lei Dong; Luis Fong de Los Santos; Anne Greener; Grace Gwe-Ya Kim; Jennifer Johnson; Perry Johnson; James G Mechalakos; Brian Napolitano; Stephanie Parker; Deborah Schofield; Koren Smith; Ellen Yorke; Michelle Wells Journal: Med Phys Date: 2020-04-15 Impact factor: 4.071
Authors: Elizabeth L Covington; Xiaoping Chen; Kelly C Younge; Choonik Lee; Martha M Matuszak; Marc L Kessler; Wayne Keranen; Eduardo Acosta; Ashley M Dougherty; Stephanie E Filpansick; Jean M Moran Journal: J Appl Clin Med Phys Date: 2016-11-08 Impact factor: 2.102
Authors: Ping Xia; Danielle LaHurd; Peng Qi; Anthony Mastroianni; Daesung Lee; Anthony Magnelli; Eric Murray; Matt Kolar; Bingqi Guo; Tim Meier; Samual T Chao; John H Suh; Naichang Yu Journal: J Appl Clin Med Phys Date: 2020-07-17 Impact factor: 2.102
Authors: Vikren Sarkar; Adam Paxton; Jeremy Kunz; Martin Szegedi; Geoff Nelson; Prema Rassiah-Szegedi; Hui Zhao; Y Jessica Huang; Frances Su; Bill J Salter Journal: J Appl Clin Med Phys Date: 2019-08-05 Impact factor: 2.102