The majority of commercial radiotherapy treatment planning systems requires planners to iteratively adjust the plan parameters in order to find a satisfactory plan. This iterative trial-and-error nature of radiotherapy treatment planning results in an inefficient planning process and in order to reduce such inefficiency, plans can be accepted without achieving the best attainable quality.

We propose a quality assessment method based on data envelopment analysis (DEA) to address this inefficiency. This method compares a plan of interest to a set of past delivered plans and searches for evidence of potential further improvement.

During the past two decades, radiotherapy treatment techniques have advanced significantly due to the development of dynamic multileaf collimators coupled with commercially available inverse planning systems. This has led to the widespread use of intensity modulated radiation therapy (IMRT) which facilitates improved conformance to the treatment volume while minimizing dose to surrounding avoidance structures... This research paper looks at using data envelopment analysis (DEA) to address potential inefficiencies in planning adjustment.