通过 Bayesian Networks 进行结直肠癌风险图谱绘制
Colorectal cancer risk mapping through Bayesian Networks
AI总结：
本文融合专家知识与观察性数据构建CRC Bayesian Network，量化风险因素及预测不确定性，并开发图形化风险图谱工具以划分人群、支持筛查与治疗决策。
摘要：
背景与目的：尽管结直肠癌（CRC）是全球第三大常见癌症，但仅有约14%符合条件的欧盟公民最终参与了结直肠癌筛查项目。开发CRC风险模型可使预测嵌入决策支持工具，从而促进CRC筛查和治疗建议。本文开发了一种预测模型，以帮助刻画CRC风险群体，并评估多种风险因素对人群的影响。方法：通过综合大量专家知识和一项观察性研究的数据，并利用结构学习算法对变量之间的关系进行建模，学习得到一个CRC Bayesian Network（贝叶斯网络）。随后对该网络进行参数化，以每个节点的局部概率分布来刻画这些关系。最终，该网络被用于预测发生CRC的风险以及此类预测的不确定性。结果：基于该模型开发了一种图形化CRC风险图谱工具，并根据感兴趣的变量将人群划分为不同风险亚组。此外，该网络揭示了可改变风险因素（如饮酒和吸烟）以及与生活方式相关的疾病（如糖尿病或高血压）对预测的影响，这些因素可能会增加发生CRC的风险。结论：CRC最常发生于老年个体。然而，一些可改变的行为因素似乎对其潜在发生风险具有很强的预测影响。对这些影响进行建模有助于识别风险群体并锁定有影响力的变量，进而有助于筛查和治疗项目的设计。
英文摘要：
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 parametrized 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. Conclusions: 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. Modelling these effects facilitates identifying risk groups and targeting influential variables which are subsequently helpful in the design of screening and treatment programs.
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