Colorectal cancer risk mapping through Bayesian Networks

TL;DR

This paper presents a Bayesian Network model for colorectal cancer risk mapping, integrating expert knowledge and data to predict individual risk and identify influential modifiable factors for better screening strategies.

Contribution

It introduces a novel CRC risk prediction model using Bayesian Networks that combines expert insights and observational data to improve risk stratification.

Findings

The model segments population into risk subgroups effectively.

Modifiable factors like smoking and alcohol significantly influence CRC risk.

The tool aids in designing targeted screening and prevention programs.

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 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.