Automated VMAT treatment planning using sequential convex programming: algorithm development and clinical implementation.
Pınar DursunLinda HongGourav JhanwarQijie HuangYing ZhouJie YangHai PhamLaura CervinoJean M MoranJoseph O DeasyMasoud ZarepishehPublished in: Physics in medicine and biology (2023)
by solving a series of convex approximation problems. We directly integrate two novel convex surrogate metrics to improve plan delivery efficiency and reduce plan complexity by promoting aperture shape regularity and neighboring aperture similarity. The entire workflow is automated using the Eclipse treatment planning system (TPS) application program interface (API) scripting and provided to users as a plug-in, requiring the users to solely provide the contours and their preferred arcs. Our program provides the optimal machine parameters and does not utilize the Eclipse optimization engine, however, it utilizes the Eclipse final dose calculation engine. We have tested our program on 60 patients of different disease sites and prescriptions for stereotactic body radiotherapy (SBRT) (paraspinal (24Gy x 1, 9Gy x 3), oligometastis (9Gy x 3), lung (18Gy x 3, 12Gy x 4)) and retrospectively compared the automated plans with the manual plans used for treatment. The program is currently deployed in our clinic and being used in our daily clinical routine to treat patients. 
Main results: The automated plans found dosimetrically comparable or superior to the manual plans. For paraspinal (24Gy x 1), the automated plans especially improved tumor coverage (the average PTV95% from 96% to 98% and CTV100% from 95% to 97%) and homogeneity (the average PTV maximum dose from 120% to 116%). For other sites/prescriptions, the automated plans especially improved the duty cycle (23%-39.4%).
Significance: This work proposes a fully automated approach to the mathematically challenging VMAT problem. It also shows how the capabilities of the existing FDA-approved commercial TPS can be enhanced using an in-house developed optimization algorithm that completely replaces the TPS optimization engine. The code and pertained models along with a sample dataset will be released on our ECHO-VMAT GitHub (https://github.com/PortPy-Project/ECHO-VMAT).
Keyphrases
- deep learning
- machine learning
- high throughput
- end stage renal disease
- quality improvement
- health insurance
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- radiation therapy
- magnetic resonance
- primary care
- computed tomography
- prognostic factors
- magnetic resonance imaging
- healthcare
- early stage
- clinical practice
- squamous cell carcinoma
- physical activity
- radiation induced
- diffusion weighted imaging
- locally advanced
- patient reported
- single cell
- neural network