Organism-level models: When mechanisms and statistics fail us

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

物理学;计算机科学

Phillips, M.H.^1 ; Meyer, J.^1 ; Smith, W.P.^2 ; Rockhill, J.K.^1

Department of Radiation Oncology, University of Washington, Seattle, WA, United States^1

Stratton VA Medical Center, Albany, NY, United States^2

关键词: Bayesian probabilistic models;    Clinical decision;    Critical variables;    Probabilistic models;    Prostate cancers;    Statistical model buildings;    Surrogate model;    Traditional models;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012096/pdf
DOI  :  10.1088/1742-6596/489/1/012096

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Purpose: To describe the unique characteristics of models that represent the entire course of radiation therapy at the organism level and to highlight the uses to which such models can be put. Methods: At the level of an organism, traditional model-building runs into severe difficulties. We do not have sufficient knowledge to devise a complete biochemistry-based model. Statistical model-building fails due to the vast number of variables and the inability to control many of them in any meaningful way. Finally, building surrogate models, such as animal-based models, can result in excluding some of the most critical variables. Bayesian probabilistic models (Bayesian networks) provide a useful alternative that have the advantages of being mathematically rigorous, incorporating the knowledge that we do have, and being practical. Results: Bayesian networks representing radiation therapy pathways for prostate cancer and head & neck cancer were used to highlight the important aspects of such models and some techniques of model-building. A more specific model representing the treatment of occult lymph nodes in head & neck cancer were provided as an example of how such a model can inform clinical decisions. A model of the possible role of PET imaging in brain cancer was used to illustrate the means by which clinical trials can be modelled in order to come up with a trial design that will have meaningful outcomes. Conclusions: Probabilistic models are currently the most useful approach to representing the entire therapy outcome process.