Abstract
Treatment planning for volumetric arc therapy (VMAT) is a lengthy process that requires many rounds of optimizations to obtain the best treatment settings and optimization constraints for a given patient's geometry. We propose a feature‐selection search engine that explores previously treated cases of similar anatomy, returning the optimal plan configurations and attainable DVH constraints. Using an institutional database of 83 previously treated cases of prostate carcinoma treated with volumetric‐modulated arc therapy, the search procedure first finds the optimal isocenter position with an optimization procedure, then ranks the anatomical similarity as the mean distance between targets. For the best matching plan, the planning information is reformatted to the DICOM format and imported into the treatment planning system to suggest isocenter, arc directions, MLC patterns, and optimization constraints that can be used as starting points in the optimization process. The approach was tested to create prospective treatment plans based on anatomical features that match previously treated cases from the institution database. By starting from a near‐optimal solution and using previous optimization constraints, the best matching test only required simple optimization steps to further decrease target inhomogeneity, ultimately reducing time spend by the therapist in planning arcs' directions and lengths.PACS number: 87.55.D‐, 87.55.de
Highlights
20 Schreibmann et al.: Automated TP for volumetric arc therapy (VMAT) in clinical practice, information of the attainable dose-volume histograms of a given patient’s anatomy is unavailable.Current treatment planning systems are built on complex database systems, but this information is rarely used in the treatment planning process
The physician can select a plan in the left box, and the corresponding dose-volume histogram (DVH) will be displayed in the right panel
These previous DVHs can be used to suggest constraints based on previously achieved objectives on plans with similar anatomy
Summary
20 Schreibmann et al.: Automated TP for VMAT in clinical practice, information of the attainable dose-volume histograms of a given patient’s anatomy is unavailable.Current treatment planning systems are built on complex database systems, but this information is rarely used in the treatment planning process. On the basis of that observation, we propose a clinical solution that reproduces a dosimetrist’s experience by searching the information within the database to query and retrieve previous cases of similar anatomy. In this database mining approach, the patient’s segmentation is used as an input, compared to previously treated patients to retrieve previous solutions and achievable DVH constraints. The planner uses these comparisons as guidance for the treatment process. The approach speeds up the planning process by starting the optimization from previous fluences and DVH constraints as obtained in similar anatomy
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