Adoption of Knowledge-Based Treatment Planning Models

Baker, J.; Sharma, Amit; Cao, Yejie; Antone, J.; J, Rogers; Hamilton, Betti A.; Potters, Louis

doi:10.1016/j.ijrobp.2018.07.1478

Cited by 2 publications

(1 citation statement)

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“…Therefore, this group of models aims to replace planner's experiencebased and protocol-driven generic DVH objectives with model-based and patient-specific predictions. Clinical implementations of these models have demonstrated better plan quality and consistency (12)(13)(14). This group of models is also known as knowledge-based planning (KBP) models (8,10,11,(15)(16)(17)(18)(19)(20)(21).…”

Section: Dvh Prediction Modelsmentioning

confidence: 99%

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Yang¹,

Zhang²,

Ge³

et al. 2021

Quant Imaging Med Surg

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Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning.With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: DVH, Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.

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