Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors

Abstract

Background To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS). Methods CT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization. Results The PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation. Conclusions The RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors.

Study on the transferability of the knowledge-based VMAT model to predict IMRT plans in prostate cancer radiotherapy

“…The dose distribution predicted by the model for the IMRT plan was comparable to the dose distribution expected for an IMRT plan with 178 small fields. Therefore, the technique of using the VMAT model to predict IMRT plans does not impose any limitations on the dose distribution [ 13 ].…”

“…Based on statistical discrepancies, Batumalai V [ 5 ] observed that even the engagement of experienced plan developers does not necessarily guarantee the variations in plan quality. Wu Hao et al [ 13 ] also concluded that intensity-modulated planning using KBP, along with manual fine-tuning, was not only able to achieve a clinically acceptable level in almost all of the plans but also significantly reduced the planning time. This was based on a comparison of automated IMRT plans with conventional manual plans in 80 patients with cervical cancer.…”

“…The test design is continuously optimized using the model until it satisfies the clinical requirements. As the automated planning increasing in popularity, many issues regarding the application of models deserve consideration [ 13 ]. Based on a review of the literature, we discovered that different radiotherapy centers frequently use various irradiation techniques depending on the actual situation when developing plans for the same disease, whereas the currently established KBP models were derived from a database of the same techniques and are only used to predict plans for the same techniques [ 16 ].…”

Optimising craniospinal irradiation for medulloblastoma: a dosimetric comparison of two VMAT planning methods

“…Similar strategies could be applied to CSI to minimize dose to adjacent OARs. Many studies have demonstrated the utilisation of avoidance sectors in VMAT planning to minimize radiation exposure to the OARs, a technique that could be adapted for the avoidance of critical structures in CSI as well [ 6 – 7 ].…”

Knowledge-based versus deep learning based treatment planning for breast radiotherapy

“…Knowledge-based planning (KBP) is a type of radiotherapy planning which uses previously acquired knowledge of dose-volume histogram (DVH) parameters in relation to planning target volume and organ at risk locations to create a treatment plan, rather than relying solely on information from the current image used for dose calculation [13] . Several in-house models have been developed for multiple disease sites, with some models also being incorporated within commercial treatment planning software for several clinical scenarios [14] , [15] , [16] , [17] . These models employ geometrical features and the associated dosimetry of plans included in the model library to predict a range of achievable DVH for organs at risk (OAR) of new patients [18] .…”