A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials

Abstract

Background and purpose: The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient-specific anatomy. This study examined these two techniques for dose-volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge-based planning (KBP) technique and another first principle (FP) technique. Methods: This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi-institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. Results: For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re-optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ-at-risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. Conclusions: Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.

Knowledge‐based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer

“…As demonstrated in this study and others, 24 , 25 RapidPlan was effective in translating dosimetric quality of training plans into model predictions for new patients. More importantly, our data show that the model trained by automated plans was more accurate than the one trained by manual plans.…”

“…Unlike dose-volume histogram (DVH)-based checklists, which can flag unsafe plans, KBP-based QA could potentially be used to flag suboptimal plans-that is, plans that could be further improved. Several groups have reported evaluations of in-house and commercial DVH prediction models for organs at risk (OARs) in intensity-modulated radiation therapy (IMRT) planning, 3,16,[21][22][23][24] and Tol et al 25 evaluated the performance of using RapidPlan, a KBP solution available in the Eclipse TPS, for volumetric arc therapy (VMAT) plan QA. Each of these studies used plans with consistent dosimetric quality to both train and evaluate the prediction models.…”

A comparison of knowledge-based dose prediction approaches to assessing head and neck radiotherapy plan quality

“…Both techniques provide similar predictions of dose metrics, as seen in similar comparison studies in the literature. [15] Considering this, the quality assurance measure of flagging cases with suboptimal doses could be carried out with either technique. Demonstrating the suitability of both models for plan quality assessment was important to providing flexibility of choice to users who may prefer one dose prediction technique over the other.…”

Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology

“…Another study evaluated the dosimetric indices of RapidPlan™ plans on Varian LINACS, and for the H&N model they concluded that the dosimetric indices for the PTV and OARs were comparable regardless of energies and MLC types [ 22 ]. Moreover, it has been demonstrated that knowledge-based DVH predictions generated from RapidPlan™ H&N models can be used for plan quality assurance purposes, especially for the plans intended for use in clinical trials [ 7 , 8 , 23 , 24 ]. Therefore, the suitability of RapidPlan™ as a QA tool reveals not only the good plan quality achieved via RapidPlan™ but also that accurate DVH predictions (DVH estimates) can be obtained [ 8 ].…”