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The workflow for automatic treatment plan generation using the RayStation scripting API. The native Python language and built-in functions within this environment allow for dose prediction with a DL model. Dose-volume metrics from the predicted dose were used to create and execute a patient-specific optimization and generate a plan with the built-in TPS optimizer.

Journal: Physics and Imaging in Radiation Oncology

Article Title: Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization

doi: 10.1016/j.phro.2024.100641

Figure Lengend Snippet: The workflow for automatic treatment plan generation using the RayStation scripting API. The native Python language and built-in functions within this environment allow for dose prediction with a DL model. Dose-volume metrics from the predicted dose were used to create and execute a patient-specific optimization and generate a plan with the built-in TPS optimizer.

Article Snippet: The RayStation (RaySearch Laboratories, Stockholm) Python scripting application programming interface (API) was used to automatically optimize treatment plans (autoplans) using DVH metrics extracted from the predicted dose distributions.

Techniques: