结直肠癌患者周围神经侵犯预测模型的建立与评价

Establishment and evaluation of a predictive model for perineural invasion in colorectal cancer patients

目的利用结直肠癌患者的临床信息及数据,通过XGBoost算法构建周围神经侵犯预测模型并评估其效能。方法选取我院178名结直肠癌患者真实世界临床数据并进行清洗及特征工程处理。训练及建立XGBoost模型,筛选最优特征子集并构建最终模型,利用SHAP解释模型。应用Boruta算法筛选特征并构建线性判别分类器(linear discriminant analysis,LDA)、朴素贝叶斯(naive bayes)、KNN(K Nearest Neighbor,KNN)、分类与回归树(classification and regression trees,CART)、随机森林模型(random forest)五种模型,及过采样类平衡后再次构建模型。比较各模型间效能。结果XGBoost应用顺序特征选择器筛选包含9个特征数量的最优特征子集并构建最终模型,与其他机器学习模型相比,性能更加优越,训练速度更为快速。结论基于XGBoost算法成功构建了结直肠癌患者周围神经侵犯预测模型。

Objective To construct a predictive model for perineural invasion using XGBoost based on clinical information and data of colorectal cancer patients and evaluate its effectiveness.Methods Real-world clinical data of 178 colorectal cancer patients in our hospital were selected and subjected to data cleaning and feature engineering processing.An XGBoost model was trained to select the optimal subset of features and construct the final model.The model was interpreted through SHapley Additive Explanation.The Boruta algorithm was applied to filter features and construct five models:Linear Discriminant Analysis,Naive Bayes,K Nearest Neighbor,Classification and Regression Trees,and Random Forest,and their oversampling class balance models were then generated.The performance of different models was compared.Results XGBoost applied Sequential Feature Selector to select the optimal feature subset containing 9 features and constructed the final model.Compared with other machine learning models,the performance of the XGBoost model was superior and the training speed was faster.Conclusion We have successfully constructed a predictive model for perineural invasion in colorectal cancer patients based on XGBoost.It can provide preoperative prediction of nerve invasion for clinical doctors,especially surgeons,and provide a basis for the development of comprehensive treatment plans.