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

Huang, Yuliang; Yue, Haizhen; Wang, Meijiao; Li, Sha; Zhang, Jian; Liu, Zhuolun; Zhang, Yibao

doi:10.1002/acm2.12313

Cited by 10 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, [25] applied a combination of grid search and linear interpolation to find parameter values that improved PTV coverage while satisfying OAR constraints, [7] developed a recursive random search method to optimize a plan score function based on clinical objectives, [26] used a library of optimized plans with similar patient geometry to guide weight selection and adjustment to improve the plan DVH, [12] used Bayesian optimization to update weights to meet or improve upon dose objectives predicted using KBP, and [27] used deep reinforcement learning to automate the trial-and-error process of tuning weights to lower OAR doses. Other aspects of the treatment planning workflow have also been explored, including a combination of KBP for personalized patient objective selection and an automated search for multileaf collimator (MLC) jaw configurations to lower OAR doses [28].…”

Section: Literature Surveymentioning

confidence: 99%

A hyperparameter‐tuning approach to automated inverse planning

2022

View full text Add to dashboard Cite

In current practice, radiotherapy inverse planning requires treatment planners to modify multiple parameters in the objective function to produce clinically acceptable plans. Due to the manual steps in this process, plan quality can vary widely depending on the planning time available and the planner's skills. The purpose of this study is to automate the inverse planning process to reduce the planner's active planning time while maintaining plan quality. We propose a hyperparameter tuning approach for automated inverse planning, where a treatment plan utility is maximized with respect to the limit dose parameters and weights of each organ-atrisk (OAR) objective. Using 6 patient cases, we investigated the impact of the choice of dose parameters, random and Bayesian search methods, and utility function form on planning time and plan quality. For given parameters, the plan was optimized in RayStation, using the scripting interface to obtain the dose distributions deliverable. We normalized all plans to have the same target coverage and compared the OAR dose metrics in the automatically generated plans with those in the manually generated clinical plans. Using 100 samples was found to produce satisfactory plan quality, and the average planning time was 2.3 hours. The OAR doses in the automatically generated plans were lower than the clinical plans by up to 76.8%. When the OAR doses were larger than the clinical plans, they were still between 0.57% above and 98.9% below the limit doses, indicating they are clinically acceptable. For a challenging case, a dimensionality reduction strategy produced a 92.9% higher utility using only 38.5% of the time needed to optimize over the original problem. This study demonstrates our hyperparameter tuning framework for automated inverse planning can significantly reduce the treatment planner's planning time with plan quality that is similar to or better than manually generated plans.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A hyperparameter‐tuning approach to automated inverse planning

2022

View full text Add to dashboard Cite

show abstract

“…There are a number of articles that aim to determine beam‐related parameters such as the number and angle of beams and jaw settings …”

Section: Resultsmentioning

confidence: 99%

Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

2019

Medical Physics

180

150

View full text Add to dashboard Cite

Purpose Intensity‐Modulated Radiation Therapy ( IMRT ), including its variations (including IMRT , Volumetric Arc Therapy ( VMAT ), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning ( KBP ) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results. Methods In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here. Results The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improve...

show abstract

“…Deep learning based methods have been studied for multiple tasks in medical imaging and radiation therapy including segmentation [2] and classification [3]. Other knowledge based methods are employed to optimize beam related parameters in intensity modulated radiation therapy (IMRT) [4,5] or to optimize beam orientations, positions, shapes, and weights directly (direct aperture optimization). The latter either requires solving a computationally demanding mixed integer problem [6] or they combine the dose in the target, dose constraints, and apertures in the objective function [7], not allowing setting hard constraints on the doses of critical organ structures.…”

Section: Problemmentioning

confidence: 99%

Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

Gerlach

Fürweger

Hofmann³

et al. 2020

Current Directions in Biomedical Engineering

View full text Add to dashboard Cite

Although robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.

show abstract

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

Cited by 10 publications

References 22 publications

A hyperparameter‐tuning approach to automated inverse planning

A hyperparameter‐tuning approach to automated inverse planning

Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

Contact Info

Product

Resources

About