Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution

Purpose: A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality. Methods: Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination. Results: The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths. Conclusions: This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality. K E Y W O R D S aperture shape controller, complexity metrics, MLC, plan complexity, plan quality metric, treatment planning 1 | INTRODUCTION Volumetric modulated arc therapy (VMAT) is nowadays the standard treatment technique in high-quality radiation therapy delivered with clinical LINACs. The rapid dose fall-off outside target boundaries grants highly conformed dose distributions and near-optimal sparing of surrounding critical structures. Volumetric modulated arc therapy has generally replaced intensity-modulated radiation therapy (IMRT) because of the shorter treatment time obtained through the simultaneous variation of gantry speed, dose rate and MLC position. 1,2 Moreover, VMAT tends to use fewer monitor units (MU) per fraction than IMRT and thus reduces the burden of second malignancies. 3-5

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“…PQM was first introduced by Nelms 27 ) and prior experience. 28,29 The detailed description can be found in the supplementary materials.…”

Section: C | Plan Quality Assessmentmentioning

confidence: 99%

Limiting treatment plan complexity by applying a novel commercial tool

Scaggion

Fusella

Agnello

et al. 2020

J Applied Clin Med Phys

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“…Several studies reported that the performance of RapidPlan TM was comparable with that of manually optimized plans for different treatment techniques and that the sites and sub-potential manual plans could be improved with RapidPlan TM [12–16]. However, some studies also reported that this algorithm could not be applied to automation for all treatment plans [17, 18] and that it was still limited by the inherent information present in the hand-crafted features. In addition, the feature quantity could only capture low-level features, and this algorithm was not sufficiently accurate for prediction [19].…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients

Kajikawa

Kadoya

Ito

et al. 2019

Journal of Radiation Research

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The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose–volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

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“…Castriconi et al [17] implemented a KB optimization strategy for adaptive treatments which resulted in robust, better or equivalent clinical plans and a significantly lower dose to the OARs (up to 3 Gy to the bowel bag). For other sites, such as the prostate [18][19][20], head and neck [21] and esophagus [22], automated planning software showed superior consistency and less variation than manual planning, which demonstrates the reduction of intra-and inter-planner subjective dependency. However, to establish an acceptable model for automated planning, it is important to manually fine-tune the optimization objectives during model training [23] and to use a large variety of patients to validate the model, i.e.…”

Section: Discussionmentioning

confidence: 97%

Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics

Vaniqui

Canters

Vaassen

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and purpose: Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis. Material and methods: A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort). Results: For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and − 4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: − 0.1 ± 1.1 Gy and − 0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning. Conclusion: An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation.

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Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution

Cited by 71 publications

References 25 publications

Limiting treatment plan complexity by applying a novel commercial tool

Limiting treatment plan complexity by applying a novel commercial tool

A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients

Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics

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