Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Hargrave, C.^1,2,3 ; Moores, M.^2,3 ; Deegan, T.^1 ; Gibbs, A.^1 ; Poulsen, M.^1 ; Harden, F.^2,3 ; Mengersen, K.^2,3

Radiation Oncology Mater Centre, Princess Alexandra Hospital, QLD Health, Brisbane, QLD, 4101, Australia^1

Queensland University of Technology, Brisbane, QLD, 4001, Australia^2

Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia^3

关键词: Causal relationships;    Clinical decision making;    Correction algorithms;    Decision-making frameworks;    Directed acyclic graph (DAG);    Image guided radiotherapy;    Literature reviews;    Sequential decision making;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012074/pdf
DOI  :  10.1088/1742-6596/489/1/012074

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.