Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes—Internal mammary and/or supraclavicular regions

Abstract

Purpose To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions. Methods and materials Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures. Results All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average Conclusions All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.

Can knowledge based treatment planning of VMAT for post-mastectomy locoregional radiotherapy involving internal mammary chain and supraclavicular fossa improve performance efficiency?

“…Out of 8 papers discussed, 6 papers are on RapidPlan™ while 2 papers are on Raystaion TPS and MD Anderson Cancer Center Auto Plan (MDAP) system. Many authors have hypothesized the potential of automated radiotherapy treatment planning for increasing consistency, improving plan quality, and reducing workloads for all routinely challenging treatments involving complex anatomical sites or involving multiple dose levels ( 15 , 19 ). Most of these papers reported on the conventional fractionation regime while we are reporting on the KBPs for both moderately hypo fractionated and ultra-hypo fractionated (fast forward) regimens.…”

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

“…The application of KBP planning with RapidPlan to breast treatments has been explored by different groups, particularly in the last few years [30][31][32][33][34][35], showing interest in such an approach.…”

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives

“…What is missing is the implementation of an AI system which simulates the reasoning behaviour of a human planner to automatically adjust the optimisation parameters during the online optimisation. The KBP approach could be interesting for the MRgRT application to obtain a fast prediction with dose constraints compliance given the daily relationship among target and OARs, as already demonstrated in different experiences [167,168].…”