Use of a constrained hierarchical optimization dataset enhances knowledge‐based planning as a quality assurance tool for prostate bed irradiation

Purpose To investigate whether building a knowledge‐based planning (KBP) model with prostate bed plans constructed from constrained hierarchical optimization (CHO) would result in more efficient model construction with more consistent output than a model built using plans from a traditional, trial‐and‐error‐based optimization (TEO) technique. Methods Three KBP models were constructed from plans from subsets of 58 post‐prostatectomy patients treated with intensity‐modulated radiation therapy. TEO54 was built from 54 TEO plans, selected to represent typical clinical variations in target and organ‐at‐risk sizes and shapes. CHO30 and TEO30 were built from the same 30 patients populated with CHO and TEO plans, respectively. The three models were each applied to a new set of 18 patient scans and dose–volume histogram estimates (DVHEs) were generated for rectal and bladder walls and compared for each patient. Results CHO30 resulted in a significantly tighter range in DVHEs (P