Prior-knowledge treatment planning for volumetric arc therapy using feature-based database mining.

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 in

SUBMITTER: Schreibmann E 

PROVIDER: S-EPMC5875469 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature