Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans?

Abstract

BackgroundTreatment plan quality assurance (QA) is important for clinical studies and for institutions aiming to generate near-optimal individualized treatment plans. However, determining how good a given plan is for that particular patient (individualized patient/plan QA, in contrast to running through a checklist of generic QA parameters applied to all patients) is difficult, time consuming and operator-dependent. We therefore evaluated the potential of RapidPlan, a commercial knowledge-based planning solution, to automate this process, by predicting achievable OAR doses for individual patients based on a model library consisting of historical plans with a range of organ-at-risk (OAR) to planning target volume (PTV) geometries and dosimetries.MethodsA 90-plan RapidPlan model, generated using previously created automatic interactively optimized (AIO) plans, was used to predict achievable OAR dose-volume histograms (DVHs) for the parotid glands, submandibular glands, individual swallowing muscles and oral cavities of 20 head and neck cancer (HNC) patients using a volumetric modulated (RapidArc) simultaneous integrated boost technique. Predicted mean OAR doses were compared with mean doses achieved when RapidPlan was used to make a new plan. Differences between the achieved and predicted DVH-lines were analyzed. Finally, RapidPlan predictions were used to evaluate achieved OAR sparing of AIO and manual interactively optimized plans.ResultsFor all OARs, strong linear correlations (R2 = 0.94–0.99) were found between predicted and achieved mean doses. RapidPlan generally overestimated the amount of achievable sparing for OARs with a large degree of OAR-PTV overlap. RapidPlan QA using predicted doses alone identified that for 50 % (10/20) of the manually optimized plans, sparing of the composite salivary glands, oral cavity or composite swallowing muscles could be improved by at least 3 Gy, 5 Gy or 7 Gy, respectively, while this was the case for 20 % (4/20) AIO plans. These predicted gains were validated by replanning the identified patients using RapidPlan.ConclusionsStrong correlations between predicted and achieved mean doses indicate that RapidPlan could accurately predict achievable mean doses. This shows the feasibility of using RapidPlan DVH prediction alone for automated individualized head and neck plan QA. This has applications in individual centers and clinical trials.

A method for a priori estimation of best feasible DVH for organs‐at‐risk: Validation for head and neck VMAT planning

“…However, while the method is different, the results are largely determined by the same physics principles, and certain elements of the described algorithm by necessity overlap with the previous body of work on KBP. [6][7][8][9][10][11][12][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] In particular, the geometry of the OAR in relation to the target is the major driver of the achievable OAR DVH. To that effect, Petit et al 29 used a subset of previous plans with less favorable PTV/OAR configuration to find the minimum achievable OAR dose for a new patient.…”

“…15 As for KBP, although the details of the specific methods differ, a common theme is to build a database derived from previously designed plans for generally similar disease presentations, and from them predict the achievable results for any new patient dataseta machine learning approach. This method was applied to quality control [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] and, more recently, to the design 6-12 of the plans. One challenge of KBP is that it requires careful selection of learning plans.…”

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…”

Guided undersampling classification for automated radiation therapy quality assurance of prostate cancer treatment

“…Improving specific components of an RT plan via knowledge-based techniques has been shown to substantially increase plan quality. 10,11,12 However, the implementation of such techniques with the goal of optimizing the RT plan as a whole would entail a paradigm shift in the manner in which cancer centers gather and store data. We conclude that a binary classification of erroneous plans remains the best feasible A natural extension of this work is to evaluate these methods across data from multiple institutions, although there are two key challenges.…”