文章：

预测胃肠道癌症患者接受5-FU化疗后严重血液毒性的贝叶斯网络模型研究

Predicting Severe Haematological Toxicity in Gastrointestinal Cancer Patients Undergoing 5-FU-Based Chemotherapy: A Bayesian Network Approach

原文发布日期：22 August 2023

DOI: 10.3390/cancers15174206

类型: Article

开放获取: 是

英文摘要：

Purpose: Severe toxicity is reported in about 30% of gastrointestinal cancer patients receiving 5-Fluorouracil (5-FU)-based chemotherapy. To date, limited tools exist to identify at risk patients in this setting. The objective of this study was to address this need by designing a predictive model using a Bayesian network, a probabilistic graphical model offering robust, explainable predictions. Methods: We utilized a dataset of 267 gastrointestinal cancer patients, conducting preprocessing, and splitting it into TRAIN and TEST sets (80%:20% ratio). The RandomForest algorithm assessed variable importance based on MeanDecreaseGini coefficient. The bnlearn R library helped design a Bayesian network model using a 10-fold cross-validation on the TRAIN set and the aic-cg method for network structure optimization. The model’s performance was gauged based on accuracy, sensitivity, and specificity, using cross-validation on the TRAIN set and independent validation on the TEST set. Results: The model demonstrated satisfactory performance with an average accuracy of 0.85 (±0.05) and 0.80 on TRAIN and TEST datasets, respectively. The sensitivity and specificity were 0.82 (±0.14) and 0.87 (±0.07) for the TRAIN dataset, and 0.71 and 0.83 for the TEST dataset, respectively. A user-friendly tool was developed for clinical implementation. Conclusions: Despite several limitations, our Bayesian network model demonstrated a high level of accuracy in predicting the risk of developing severe haematological toxicity in gastrointestinal cancer patients receiving 5-FU-based chemotherapy. Future research should aim at model validation in larger cohorts of patients and different clinical settings.

摘要翻译： 

目的：约30%接受5-氟尿嘧啶（5-FU）为基础化疗的胃肠道癌症患者会出现严重毒性反应。目前，该领域缺乏有效的风险患者识别工具。本研究旨在通过构建贝叶斯网络预测模型来填补这一空白，该概率图模型能够提供稳健且可解释的预测结果。方法：我们采用267例胃肠道癌症患者数据集，经过预处理后按8:2比例划分为训练集与测试集。通过随机森林算法基于平均基尼系数下降值评估变量重要性，并利用bnlearn R程序包构建贝叶斯网络模型。采用训练集10折交叉验证与aic-cg网络结构优化方法，通过训练集交叉验证和测试集独立验证，以准确率、敏感性和特异性评估模型性能。结果：该模型在训练集和测试集分别达到0.85（±0.05）和0.80的平均准确率。训练集的敏感性和特异性分别为0.82（±0.14）和0.87（±0.07），测试集则为0.71和0.83。研究同时开发了便于临床应用的友好型工具。结论：尽管存在若干局限，本贝叶斯网络模型在预测接受5-FU化疗的胃肠道癌症患者发生严重血液学毒性风险方面展现出较高准确性。未来研究需在更大患者群体及不同临床场景中进一步验证模型效能。

原文链接：

Predicting Severe Haematological Toxicity in Gastrointestinal Cancer Patients Undergoing 5-FU-Based Chemotherapy: A Bayesian Network Approach