基于贝叶斯累加回归树模型的非小细胞肺癌患者个性化疗效研究

doi:10.6040/j.issn.1671-7554.0.2021.1507

摘要/Abstract

摘要： 目的应用贝叶斯累加回归树(BART)模型对非小细胞肺癌(NSCLC)患者的仅手术与手术+化疗两种治疗方案的疗效进行个性化评价,并识别高受益患者亚组。方法提取美国SEER数据库中2012—2013年间所有NSCLC患者资料,以总生存时间不小于5年作为结局,按照治疗方案分类进行比较。运用倾向性评分匹配法使比较组间协变量均衡,应用BART模型计算个体处理效应(ITE),运用决策树模型进行亚组分析。结果本研究BART所对应的模型曲线下面积(AUC)大于0.8,结果可信度较高,99.5%患者采用手术+化疗方案获得不同程度更优疗效,得到AJCC分期、原发灶位置、转移程度等临床实践中重要的亚组分类特征,并得出疗效最优的亚组。所得亚组中,相较于仅手术的治疗方案,远处转移、原发灶位于肺中叶的女性患者更能于手术+化疗方案中受益。结论构建的BART模型可以较好地评价NSCLC患者两种治疗方案的个性化疗效,为今后同类疾病的个性化医疗实践提供依据。

关键词: 贝叶斯累加回归树, 个体处理效应, 非小细胞肺癌, 手术, 辅助化疗

Abstract: Objective Bayesian Additive Regression Tree(BART)model was applied to personalize efficacy of surgery-only and surgical adjuvant chemotherapy regimens for patients with non-small cell lung cancer(NSCLC), and to identify high yield subgroups. Methods The data of all NSCLC patients from 2012 to 2013 were extracted from the SEER database. With the overall survival time no less than 5 years as the outcome, the treatment regimens were compared. The propensity scores matching method was used to equalize the covariates between the two groups, the BART model was applied to calculate the individual treatment effect(ITE), and subgroup analysis was performed by decision tree model. Results The area under the curve(AUC)of the BART model was larger than 0.8, indicating that the results had high confidence. Most patients had better efficacy with surgical adjuvant chemotherapy regimen. Important subgroup taxonomic features in clinical practice such as AJCC stage, location of primary foci and degree of tumor metastasis were obtained and the subgroup with the best efficacy was derived. Among these subgroups, compared to surgery-only options, surgical adjuvant chemotherapy regimens produced better outcomes in female patients with distant metastasis and primary site in the middle lobe of the lung. Conclusion The constructed BART model can effectively evaluate personalized efficacy of the two treatment regimens for NSCLC patients and provide a basis for personalized medical practice for similar diseases in the future.

Key words: Bayesian Additive Regression Tree, Individual treatment effect, Non-small cell lung cancer, Surgery, Adjuvant chemotherapy

中图分类号:

R181

丁子琛,王浩桦,周立雯,丛慧文,李承圣,包绮晗,杨毅,王廉源,王素珍,石福艳. 基于贝叶斯累加回归树模型的非小细胞肺癌患者个性化疗效研究[J]. 山东大学学报 (医学版), 2022, 60(10): 92-98.

DING Zichen, WANG Haohua, ZHOU Liwen, CONG Huiwen, LI Chengsheng, BAO Qihan, YANG Yi, WANG Lianyuan, WANG Suzhen, SHI Fuyan. Study on the individualized efficacy for non-small cell lung cancer patients based on Bayesian Additive Regression Tree model[J]. Journal of Shandong University (Health Sciences), 2022, 60(10): 92-98.

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