Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients?

Abstract

BackgroundKnowledge-based planning (KBP) is a promising technique that can improve plan quality and increase planning efficiency. However, no attempts have been made to extend the domain of KBP for planners with different planning experiences so far. The purpose of this study was to quantify the potential gains for planners with different planning experiences after implementing KBP in intensity modulated radiation therapy (IMRT) plans for left-sided breast cancer patients.MethodsThe model libraries were populated with 80 expert clinical plans from treated patients who previously received left-sided breast-conserving surgery and IMRT with simultaneously integrated boost. The libraries were created on the RapidPlanTM. 6 planners with different planning experiences (2 beginner planners, 2 junior planners and 2 senior planners) generated manual and KBP optimized plans for additional 10 patients, similar to those included in the model libraries. The plan qualities were compared between manual and KBP plans.ResultsAll plans were capable of achieving the prescription requirement. There were almost no statistically significant differences in terms of the planning target volume (PTV) coverage and dose conformality. It was demonstrated that the doses for most of organs-at-risk (OARs) were on average lower or equal in KBP plans compared to manual plans except for the senior planners, where the very small differences were not statistically significant. KBP data showed a systematic trend to have superior dose sparing at most parameters for the heart and ipsilateral lung. The observed decrease in the doses to these OARs could be achieved, particularly for the beginner and junior planners. Many differences were statistically significant.ConclusionsIt is feasible to generate acceptable IMRT plans after implementing KBP for left-sided breast cancer. KBP helps to effectively improve the quality of IMRT plans against the benchmark of manual plans for less experienced planners without any manual intervention. KBP showed promise for homogenizing the plan quality by transferring planning expertise from more experienced to less experienced planners.

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy

“…KBP library models are a good way to incorporate the expert knowledge base found from prior patient planning and rapidly utilize this knowledge to predict planning outcomes for new patients. Many groups have utilized in‐house created models applied to a variety of body sites . Most have started with prostate planning due to the simplicity of the site in terms of ease of planning and the bonus of having a database that includes a large number of consistent plans from which to draw.…”

An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

“…Knowledge‐based radiotherapy treatment planning is deemed to reduce the inter‐planner varieties of plan quality1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and expedite the planning process 14, 15, 16, 1718, 19 and displayed good compatibility across patient orientations, treatment techniques, and systems 20, 21…”

Artificial intelligence in radiation oncology treatment planning: a brief overview

“…KBP methods have been commercialized in treatment planning systems (63)(64)(65)(66)(67)(68). For example, RapidPlan™ (Varian Medical Systems, Palo Alto, CA) trains on past clinically acceptable plan DICOM files with beam geometry and the structure set on which the planner wishes to create objectives (per-structure minimum of 20 examples) to output an automatic plan with predicted DVHs for all objective OARs.…”

“…For example, RapidPlan™ (Varian Medical Systems, Palo Alto, CA) trains on past clinically acceptable plan DICOM files with beam geometry and the structure set on which the planner wishes to create objectives (per-structure minimum of 20 examples) to output an automatic plan with predicted DVHs for all objective OARs. RapidPlan™ can generate plans comparable to expertgenerated plans (64,65) and superior to beginner and juniorgenerated plans (66). The RapidPlan™ ML algorithm details are proprietary but are inspired by Yuan et al (69), who used stepwise multiple regression to learn anatomic and spatial image features that are germane to radiotherapy planning and then used principal component analysis to determine which of those features accounted for the greatest variance in OAR dose deposition.…”