Artificial intelligence technology for radiation oncology understaff mitigation and cost-effective treatment planning

Abstract

Treatment with radiation therapy can be relatively inexpensive and highly effective, reducing the overall cost of healthcare, as well as saving lives of cancer patients. To face the posed challenges of laborious tasks and understaff in radiotherapy, the use of knowledge-based models (artificial Intelligence) to reduce the treatment planning times up to 95% might be a promising solution. One such tool, called RapidPlan (Varian Medical Systems, Palo Alto-CA), could be acquired with an investment of a small fraction of the treatment planning system cost. RapidPlan’s support during treatment planning results in a considerable increase in plan quality while reducing plan variability and elaboration time. The goal of this dissertation was to estimate the break-even point from where the time saved during treatment time would pay the initial investment on RapidPlan. Published data demonstrates that RapidPlan can largely benefit radiation therapy institutions by streamlining the treatment planning process and the break-even point started to be achieved after treating 112 to 2668 patients, depending on the cancer types treated for each group. Therefore, it may be possible to realize a return on investment within a reasonable time frame, while benefiting from gains in efficiency, and possibly mitigating understaffing and lack of experience in treatment planning.