Technical Report: Development and Implementation of an Open Source Template Interpretation Class Library for Automated Treatment Planning.

PURPOSE: Widespread implementation of automated treatment planning in radiation therapy remains elusive owing to variability in clinic and physician preferences, making it difficult to ensure consistent plan parameters. We have developed an open-source class library with the aim to improve efficiency and consistency for automated treatment planning in radiation therapy. METHODS AND MATERIALS: An open-source class library has been developed that interprets clinical templates within a commercial treatment planning system into a treatment plan for automated planning. This code was leveraged for the automated planning of 39 patients and retrospectively compared with the 78 clinically approved manual plans. RESULTS: From the initial 39 patients, 74 of 78 plans were successfully generated without manual intervention. The target dose was more homogeneous for automated plans, with an average homogeneity index of 3.30 for manual plans versus 3.11 for automated plans (P = .107). The generalized equivalent uniform dose (gEUD) was decreased in the femurs and rectum for automated plans, with a mean gEUD of 3746 cGy versus 3338 cGy (P ≤ 0.001) and 5761 cGy versus 5634 cGy (P ≤ 0.001) for the femurs and rectum, respectively. Dose metrics for the bladder and rectum (V6500 cGy and V4000 cGy) showed recognizable but insignificant improvements. All automated plans delivered for quality assurance passed a gamma analysis (>95%), with an average composite pass rate of 99.3% for pelvis plans and 98.8% for prostate plans. Deliverability parameters such as total monitor units and aperture complexity indicated deliverable plans. CONCLUSIONS: Prostate cancer and pelvic node radiation therapy can be automated using volumetric modulated arc therapy planning and clinical templates based on a standardized clinical workflow. The class library developed in this study conveniently interfaced between the plan template and the treatment planning system to automatically generate high-quality plans on customizable templates.

Application programming interface guided QA plan generation and analysis automation.

PURPOSE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process. METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light-radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6-month period. RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field-matched junction and ball-bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%. CONCLUSION: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.