摘要
目的基于真实世界数据和机器学习技术,构建拉帕替尼治疗乳腺癌患者无进展生存期(PFS)的预测模型。方法回顾性收集复旦大学附属肿瘤医院2016年7月至2017年6月收治的150例病例。预测模型的结局指标为患者PFS是否≤1年。使用序列前向选择算法进行特征选择,并比较极限梯度提升(XGBoost)、分类提升(CatBoost)、随机森林(RF)、光梯度提升机(LightGBM)、梯度提升决策树(GBDT)、逻辑回归(LR)、支持向量机(SVR)、人工神经网络(ANN)和TabNet算法的预测性能。结果挖掘得到的重要变量包括给药方案、年龄、化疗次数、蒽环类药物、铂类药物、雌激素受体阳性、肿瘤分期、转移部位数量。XGBoost模型预测性能最佳,对PFS≤1年的预测准确率为93%,召回率为87%;对PFS>1年的预测准确率为71%,召回率为83%。结论本研究构建的拉帕替尼治疗乳腺癌患者的预后预测模型性能和稳健性良好,可为乳腺癌临床治疗提供更好的辅助决策依据。
AIM Based on real world data and machine learning technology,a predictive model of progression free survival(PFS)of patients with breast cancer treated with lapatinib was constructed.METHODS A retrospective collection of 150 patients admitted to the Fudan University Shanghai Cancer Center from July 2016 to June 2017 was conducted.The outcome indicator of the prediction model was whether the patient’s PFS was≤1 year.Using sequential forward selection algorithms for feature selection,and comparing the predictive performance of 9 algorithms for building models,including extreme gradient boost(XGBoost),classification boost(CatBoost),random forest(RF),light gradient boost(LightGBM),gradient boost decision tree(GBDT),logistic regression(LR),support vector regression(SVR),artificial neural network(ANN),and TabNet.RESULTS Important variables included medication regimen,age,frequency of chemotherapy,anthracycline drugs,platinum drugs,estrogen receptor,disease stage,and number of metastatic sites.The XGBoost model had the best prediction performance,with a prediction accuracy of 93%and a recall rate of 87%for PFS≤1 year.And a prediction accuracy was 71%,and a recall rate was 83%for PFS>1 year.CONCLUSION The performance and robustness of the prognosis prediction model for patients with breast cancer treated with lapatinib established are good,which can provide a better auxiliary decision-making basis for clinical treatment of breast cancer.
作者
于泽
叶璇
吕春明
张津源
郝昕
王瑞文
翟青
高飞
YU Ze;YE Xuan;LÜChun-ming;ZHANG Jin-yuan;HAO Xin;WANG Rui-wen;ZHAI Qing;GAO Fei(Institute of Interdisciplinary Integrative Medicine Research,Shanghai University of Traditional Chinese Medicine,SHANGHAI 201203,China;Experiment Center for Science and Technology,Shanghai University of Traditional Chinese Medicine,SHANGHAI 201203,China;Fudan University Shanghai Cancer Center,SHANGHAI 200032,China;Beijing Medicinovo Technology Co.Ltd.,BEIJING 100071,China;Dalian Medicinovo Technology Co.Ltd.,Dalian LIAONING 116000,China)
出处
《中国新药与临床杂志》
CAS
CSCD
北大核心
2024年第1期44-50,共7页
Chinese Journal of New Drugs and Clinical Remedies
基金
国家重点研发计划项目(2020YFC2005503)。
