PURPOSE: At our institution, all proton patient plans undergo patient-specific quality assurance (PSQA) prior to treatment delivery. For intensity-modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency. METHODS: We used the treatment planning system's (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer, and storage. To improve the analysis of the measurement data, a Python script was developed to automatically perform a 2D-3D γ-index analysis comparing measurements in the plane of a two-dimensional detector array with TPS predictions in a water phantom for each acquired measurement. RESULTS: Device connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ-index analysis was even more pronounced, dropping by a factor of 10. On average, we observed an overall time savings of 55% in completion of the PSQA per patient plan. CONCLUSIONS: The automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue, and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.
PURPOSE: At our institution, all proton patient plans undergo patient-specific quality assurance (PSQA) prior to treatment delivery. For intensity-modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency. METHODS: We used the treatment planning system's (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer, and storage. To improve the analysis of the measurement data, a Python script was developed to automatically perform a 2D-3D γ-index analysis comparing measurements in the plane of a two-dimensional detector array with TPS predictions in a water phantom for each acquired measurement. RESULTS: Device connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ-index analysis was even more pronounced, dropping by a factor of 10. On average, we observed an overall time savings of 55% in completion of the PSQA per patient plan. CONCLUSIONS: The automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue, and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.
Authors: Dennis Mackin; Yupeng Li; Michael B Taylor; Matthew Kerr; Charles Holmes; Narayan Sahoo; Falk Poenisch; Heng Li; Jim Lii; Richard Amos; Richard Wu; Kazumichi Suzuki; Michael T Gillin; X Ronald Zhu; Xiaodong Zhang Journal: Med Phys Date: 2013-12 Impact factor: 4.071
Authors: Kiley B Pulliam; Jessie Y Huang; Rebecca M Howell; David Followill; Ryan Bosca; Jennifer O'Daniel; Stephen F Kry Journal: Med Phys Date: 2014-02 Impact factor: 4.071
Authors: X Ronald Zhu; Falk Poenisch; Xiaofei Song; Jennifer L Johnson; George Ciangaru; M Brad Taylor; MingFwu Lii; Craig Martin; Bijan Arjomandy; Andrew K Lee; Seungtaek Choi; Quynh Nhu Nguyen; Michael T Gillin; Narayan Sahoo Journal: Int J Radiat Oncol Biol Phys Date: 2011-02-06 Impact factor: 7.038
Authors: Heng Li; Narayan Sahoo; Falk Poenisch; Kazumichi Suzuki; Yupeng Li; Xiaoqiang Li; Xiaodong Zhang; Andrew K Lee; Michael T Gillin; X Ronald Zhu Journal: Med Phys Date: 2013-02 Impact factor: 4.071
Authors: X Ronald Zhu; Yupeng Li; Dennis Mackin; Heng Li; Falk Poenisch; Andrew K Lee; Anita Mahajan; Steven J Frank; Michael T Gillin; Narayan Sahoo; Xiaodong Zhang Journal: Cancers (Basel) Date: 2015-04-10 Impact factor: 6.639
Authors: Liyong Lin; Minglei Kang; Timothy D Solberg; Thierry Mertens; Christian Baeumer; Christopher G Ainsley; James E McDonough Journal: J Appl Clin Med Phys Date: 2015-05-08 Impact factor: 2.102
Authors: Jie Shan; Yunze Yang; Steven E Schild; Thomas B Daniels; William W Wong; Mirek Fatyga; Martin Bues; Terence T Sio; Wei Liu Journal: Med Phys Date: 2020-10-13 Impact factor: 4.071
Authors: Michael D Chuong; Christopher L Hallemeier; Heng Li; Xiaorong Ronald Zhu; Xiaodong Zhang; Erik J Tryggestad; Jen Yu; Ming Yang; J Isabelle Choi; Minglei Kang; Wei Liu; Antje Knopf; Arturs Meijers; Jason K Molitoris; Smith Apisarnthanarax; Huan Giap; Bradford S Hoppe; Percy Lee; Joe Y Chang; Charles B Simone; Steven H Lin Journal: Front Oncol Date: 2021-10-19 Impact factor: 6.244
Authors: James E Younkin; Danairis Hernandez Morales; Jiajian Shen; Jie Shan; Martin Bues; Jarrod M Lentz; Steven E Schild; Joshua B Stoker; Xiaoning Ding; Wei Liu Journal: Technol Cancer Res Treat Date: 2019 Jan-Dec