Performance of a Knowledge-Based Model for Optimization of Volumetric Modulated Arc Therapy Plans for Single and Bilateral Breast Irradiation

Abstract

PurposeTo evaluate the performance of a model-based optimisation process for volumetric modulated arc therapy, VMAT, applied to whole breast irradiation.Methods and MaterialsA set of 150 VMAT dose plans with simultaneous integrated boost were selected to train a model for the prediction of dose-volume constraints. The dosimetric validation was done on different groups of patients from three institutes for single (50 cases) and bilateral breast (20 cases).ResultsQuantitative improvements were observed between the model-based and the reference plans, particularly for heart dose. Of 460 analysed dose-volume objectives, 13% of the clinical plans failed to meet the constraints while the respective model-based plans succeeded. Only in 5 cases did the reference plans pass while the respective model-based failed the criteria. For the bilateral breast analysis, the model-based plans resulted in superior or equivalent dose distributions to the reference plans in 96% of the cases.ConclusionsPlans optimised using a knowledge-based model to determine the dose-volume constraints showed dosimetric improvements when compared to earlier approved clinical plans. The model was applicable to patients from different centres for both single and bilateral breast irradiation. The data suggests that the dose-volume constraint optimisation can be effectively automated with the new engine and could encourage its application to clinical practice.

An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

“…These results echoed the superiority of knowledge‐based solution over the conventional trial‐and‐error manual planning, in line with previous publications 17, 20, 22, 23, 24, 25, 26, 27. It suggested that knowledge‐ and geometry‐based dosimetric predictions can help avoid selecting suboptimal or conflict optimization constraints as manual limitations.…”

“…Well‐trained RapidPlan models have outperformed conventional trial and error‐based manual planning by reducing excess organs‐at‐risk (OAR) dose with greater consistency 17, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30. Should the model performance be highly dependent on the library volume31 and average quality of the training plans,17, 32 incorporating the model‐improved constituent training plans into the model (closed‐loop)25 may potentially evolve the model as a cycle of interactive improvement.…”

A dosimetric evaluation of knowledge‐based VMAT planning with simultaneous integrated boosting for rectal cancer patients

“…RapidPlan‐associated dose reduction to the bladder 7 , 13 and femoral head 7 , 12 , 13 were macroscopic and significant. Similar improvement of OAR sparing and consistency has also been widely reported in other clinical sites 14 , 15 , 16 , 17 …”

“…Relative to the conventional experience‐based planning, superior or comparable results of RapidPlan have been reported in the preliminary applications to head and neck, lung, oesophageal, breast, hepatocellular, and prostate cancer patients 12 , 13 , 14 , 15 , 16 , 17 . However, consensus has been reached by these studies that both the model training and plan evaluation should be investigated further in a larger population and other cancer types in order to gain more experience and confidence before it is extensively applied clinically world‐wide.…”

Fully automated searching for the optimal VMAT jaw settings based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge‐based planning

“…Although the RapidPlan model generated identical optimization objectives for the same patient anatomy and beam geometry (except jaws), the knowledge‐based planning module in the proposed optimal jaw searching method is intended to avoid subjective planner dependence, and to personalize the automated optimization in case of different patient anatomy, prescription, field geometry and energies, which were all modeled by RapidPlan in dose prediction 9, 17, 18, 19, 20. However, it is highly desired that, the next versions of RapidPlan should model the actual jaw settings for more accurate dose estimation, which may potentially serve as a fast and sensitive indicator of dosimetric changes with various jaw settings.…”