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中华腔镜泌尿外科杂志(电子版) ›› 2021, Vol. 15 ›› Issue (04) : 280 -284. doi: 10.3877/cma.j.issn.1674-3253.2021.04.003

临床研究

机器学习算法模型预测体外冲击波碎石治疗输尿管结石的疗效
侯祺1, 相洋2, 吴娜珊1, 肖月1, 肖龙1, 李潇1, 王锐1, 孙中义1,()   
  1. 1. 518055 深圳大学总医院泌尿外科
    2. 518000 深圳,鹏城实验室
  • 收稿日期:2021-04-29 出版日期:2021-08-01
  • 通信作者: 孙中义
  • 基金资助:
    国家自然科学基金(82002716); 广东省普通高校特色创新类项目(2019KTSCX146); 深圳市自然科学基金(JCYJ20190808164209301); 深圳市新引进高端人才科研启动基金、深圳大学总医院科研启动基金(SUGH2020QD005)

Machine learning prediction of stone-free rate in patients with ureter stone after treatment of extracorporeal shock wave lithotripsy

Qi Hou1, Yang Xiang2, Nashan Wu1, Yue Xiao1, Long Xiao1, Xiao Li1, Rui Wang1, Zhongyi Sun1,()   

  1. 1. Department of Urology, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518055, China
    2. Pengcheng Laboratory, Shenzhen 518000, China
  • Received:2021-04-29 Published:2021-08-01
  • Corresponding author: Zhongyi Sun
引用本文:

侯祺, 相洋, 吴娜珊, 肖月, 肖龙, 李潇, 王锐, 孙中义. 机器学习算法模型预测体外冲击波碎石治疗输尿管结石的疗效[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2021, 15(04): 280-284.

Qi Hou, Yang Xiang, Nashan Wu, Yue Xiao, Long Xiao, Xiao Li, Rui Wang, Zhongyi Sun. Machine learning prediction of stone-free rate in patients with ureter stone after treatment of extracorporeal shock wave lithotripsy[J/OL]. Chinese Journal of Endourology(Electronic Edition), 2021, 15(04): 280-284.

目的

探讨基于机器学习算法构建体外冲击波碎石术(ESWL)治疗输尿管结石疗效的预测模型,比较各种算法模型的预测效果及优劣。

方法

纳入接受ESWL治疗的输尿管结石患者1 116例。利用患者性别、年龄、身高、体重、病程、临床症状、结石长径和短径等特征因素以及体外冲击波碎石治疗3个月后的清石结局数据,分别构建随机森林(RF)和极致梯度提升树(XGBoost)预测模型,与Logistic回归预测模型相互比较。

结果

Logistic回归预测模型对体外冲击波碎石疗效的预测准确率为84.67%,ROC曲线下面积(AUC)为0.70。随机森林和极致梯度提升树预测模型的预测准确率分别为91.76%、98.75%,AUC分别为0.9904和0.9998。三种预测模型结果提示结石部位与结石负荷影响治疗的成功率。

结论

机器学习算法的随机森林和极致梯度提升树算法可以极大提高ESWL治疗输尿管结石疗效的预测准确率。

Objective

To compare the performance of machine learning prediction model for success rate of extracorporeal shock wave lithotripsy (ESWL) treating ureter stone.

Methods

1 116 patients who underwent ESWL for ureter stone were enrolled. Clinical data including patient’s gender, age, height, weight, disease course, clinical symptom and characteristics of stone and the outcome of treatment were collected. Prediction model was established by random forest, extreme gradient boosting trees and logistic regression in Python 3.7.

Results

Overall predictive accuracy and area under curve (AUC) calculated by Logistic regression prediction model were 84.67% and 0.70. Results of random forest and extreme gradient boosting trees prediction model were 91.76%, 0.9904 and 98.75%, 0.9998. The three prediction models revealed stone-free rate impacted by patients’ stone site and stone burden.

Conclusions

Machine learning models are better than logistic regression for predicting success rate of ESWL treating ureter stone.

表1 ESWL治疗的输尿管结石患者纳入临床数据的基线特征
图1 三种模型在测试模式1条件下的预测ROC曲线
表2 三种算法模型的预测性能比较
图2 随机森林模型中变量的重要性指数
图3 极致梯度提升树模型中变量的重要性指数
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