Abstract
PurposeTo implement Knowledge Based (KB) automatic planning for right and left-sided whole breast treatment through a new volumetric technique (ViTAT, Virtual Tangential-fields Arc Therapy) mimicking conventional tangential fields (TF) irradiation.Materials and MethodA total of 193 clinical plans delivering TF with wedged or field-in-field beams were selected to train two KB-models for right(R) and left(L) sided breast cancer patients using the RapidPlan (RP) tool implemented in the Varian Eclipse system. Then, a template for ViTAT optimization, incorporating individual KB-optimized constraints, was interactively fine-tuned. ViTAT plans consisted of four arcs (6 MV) with start/stop angles consistent with the TF geometry variability within our population; the delivery was completely blocked along the arcs, apart from the first and last 20° of rotation for each arc. Optimized fine-tuned KB templates for automatic plan optimization were generated. Validation tests were performed on 60 new patients equally divided in R and L breast treatment: KB automatic ViTAT-plans (KB-ViTAT) were compared against the original TF plans in terms of OARs/PTVs dose-volume parameters. Wilcoxon-tests were used to assess the statistically significant differences.ResultsKB models were successfully generated for both L and R sides. Overall, 1(3%) and 7(23%) out of 30 automatic KB-ViTAT plans were unacceptable compared to TF for R and L side, respectively. After the manual refinement of the start/stop angles, KB-ViTAT plans well fitted TF-performances for these patients as well. PTV coverage was comparable, while PTV D1% was improved with KB-ViTAT by R:0.4/L:0.2 Gy (p < 0.05); ipsilateral OARs Dmean were similar with a slight (i.e., few % volume) improvement/worsening in the 15–35 Gy/2–15 Gy range, respectively. KB-ViTAT better spared contralateral OARs: Dmean of contralateral OARs was 0.1 Gy lower (p < 0.05); integral dose was R:5%/L:8% lower (p < 0.05) than TF. The overall time for the automatic plan optimization and final dose calculation was 12 ± 2 minutes.ConclusionsFully automatic KB-optimization of ViTAT can efficiently replace manually optimized TF planning for whole breast irradiation. This approach was clinically implemented in our institute and may be suggested as a large-scale strategy for efficiently replacing manual planning with large sparing of time, elimination of inter-planner variability and of, seldomly occurring, sub-optimal manual plans.
Highlights
Post-operative irradiation of the whole breast is a well assessed and effective therapeutic option in the treatment of localized breast cancer [1]
Regarding the tangential fields (TF) approach for whole breast irradiation, a relatively weak point of auto-planning is the difficulty to take into account the interpatient variations in assessing the best position of the fields to limit the dose to the adjacent organs, concomitantly assuring Planning Target Volume (PTV) coverage and highly homogenous dose distribution within PTV
In order to obtain this objective, within a project for the large-scale implementation of automatic KB plan optimization (MIKAPOCo, MultiInstitutional Knowledge-based Approach to Plan Optimization for the Community), we previously demonstrated the possibility to mimic the performances of TF irradiation through a partially blocked multi-arcs approach using RapidArc®, named ViTAT [Virtual Tangential-fields Arc Therapy [36]]
Summary
Post-operative irradiation of the whole breast is a well assessed and effective therapeutic option in the treatment of localized breast cancer [1]. More than 70% of women submitted to breast-conserving surgery receives post-operative radiotherapy and a large fraction of them is treated to sterilize the whole breast, typically delivering 38–40 Gy in 15–16 fractions or 50 Gy in 25 fractions. Due to the large and increasing incidence of breast cancer in the female population [2], this treatment represents a quite relevant fraction of the patients daily treated in the radiation oncology departments worldwide. Standardization in radiotherapy treatment planning is an important goal aimed to guarantee to all patients a high quality treatment, independent of the planner time and skills; this seems still more urgent in countries with a rapidly growing incidence of cancer and low/middle income [2]. Automatic solutions for this application were rarely reported using in-house systems, intrinsically difficult to extend on a larger scale [11, 30, 32]
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