Bayesian Decision Support for Adaptive Lung Treatments

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

物理学;计算机科学

McShan, Daniel^1 ; Luo, Yi^1 ; Schipper, Matt^1 ; Tenhaken, Randall^1

Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, United States^1

关键词: Bayesian approaches;    Bayesian decision;    Bayesian decision networks;    Clinical decision support;    Planned treatments;    Probabilistic data;    Response prediction;    Treatment management;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012053/pdf
DOI  :  10.1088/1742-6596/489/1/012053

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827