Colorectal cancer risk mapping through Bayesian networks

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

Background and Objective:

Only about 14% of eligible EU citizens finally participate in colorectal cancer (CRC) screening programs despite it being the third most common type of cancer worldwide. The development of CRC risk models can enable predictions to be embedded in decision-support tools facilitating CRC screening and treatment recommendations. This paper develops a predictive model that aids in characterizing CRC risk groups and assessing the influence of a variety of risk factors on the population.

Methods:

A CRC Bayesian Network is learnt by aggregating extensive expert knowledge and data from an observational study and making use of structure learning algorithms to model the relations between variables. The network is then parametrised to characterize these relations in terms of local probability distributions at each of the nodes. It is finally used to predict the risks of developing CRC together with the uncertainty around such predictions.

Results:

A graphical CRC risk mapping tool is developed from the model and used to segment the population into risk subgroups according to variables of interest. Furthermore, the network provides insights on the predictive influence of modifiable risk factors such as alcohol consumption and smoking, and medical conditions such as diabetes or hypertension linked to lifestyles that potentially have an impact on an increased risk of developing CRC.

Conclusion:

CRC is most commonly developed in older individuals. However, some modifiable behavioral factors seem to have a strong predictive influence on its potential risk of development. Modeling these effects facilitates identifying risk groups and targeting influential variables which are subsequently helpful in the design of screening and treatment programs.

通过贝叶斯网络绘制大肠癌风险图
背景和目的:尽管结肠直肠癌(CRC)是全球第三大常见癌症,但最终只有约 14% 符合条件的欧盟公民参加了该筛查项目。开发 CRC 风险模型可以将预测结果嵌入决策支持工具中,从而为 CRC 筛查和治疗建议提供便利。本文开发了一种预测模型,可帮助确定 CRC 风险群体的特征,并评估各种风险因素对人群的影响。方法:通过汇总广泛的专家知识和一项观察研究的数据,并利用结构学习算法对变量之间的关系进行建模,从而学习 CRC 贝叶斯网络。然后对该网络进行参数化,根据每个节点的局部概率分布来描述这些关系。结果:根据该模型开发了一个图形化的 CRC 风险绘图工具,用于根据相关变量将人群划分为不同的风险亚群。此外,该网络还提供了有关可改变的风险因素(如饮酒和吸烟)以及与生活方式相关的医疗状况(如糖尿病或高血压)的预测影响,这些因素可能会增加患上 CRC 的风险。然而,一些可改变的行为因素似乎对其潜在的发病风险有很大的预测影响。对这些影响进行建模,有助于确定风险群体和有针对性的影响变量,从而有助于筛查和治疗方案的设计。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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