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马军燕, 吴琼, 董量, 李春阳, 王志武. 中医证素参与的晚期非小细胞肺癌PD-1抑制剂近期疗效预测模型构建与验证[J]. 肿瘤防治研究, 2023, 50(10): 960-967. DOI: 10.3971/j.issn.1000-8578.2023.22.1513
引用本文: 马军燕, 吴琼, 董量, 李春阳, 王志武. 中医证素参与的晚期非小细胞肺癌PD-1抑制剂近期疗效预测模型构建与验证[J]. 肿瘤防治研究, 2023, 50(10): 960-967. DOI: 10.3971/j.issn.1000-8578.2023.22.1513
MA Junyan, WU Qiong, DONG Liang, LI Chunyang, WANG Zhiwu. Construction and Validation of A Predictive Model Including TCM Pathogenic Syndrome for Short-term Efficacy of PD-1 Inhibitors in Advanced Non-small Cell Lung Cancer[J]. Cancer Research on Prevention and Treatment, 2023, 50(10): 960-967. DOI: 10.3971/j.issn.1000-8578.2023.22.1513
Citation: MA Junyan, WU Qiong, DONG Liang, LI Chunyang, WANG Zhiwu. Construction and Validation of A Predictive Model Including TCM Pathogenic Syndrome for Short-term Efficacy of PD-1 Inhibitors in Advanced Non-small Cell Lung Cancer[J]. Cancer Research on Prevention and Treatment, 2023, 50(10): 960-967. DOI: 10.3971/j.issn.1000-8578.2023.22.1513

中医证素参与的晚期非小细胞肺癌PD-1抑制剂近期疗效预测模型构建与验证

Construction and Validation of A Predictive Model Including TCM Pathogenic Syndrome for Short-term Efficacy of PD-1 Inhibitors in Advanced Non-small Cell Lung Cancer

  • 摘要:
    目的 评估PD-1抑制剂治疗非小细胞肺癌(NSCLC)近期疗效的预测因素,构建预测模型。
    方法 前瞻性纳入2019年10月—2021年11月间,符合入组标准、应用PD-1抑制剂的晚期NSCLC患者221例,2021年5月1日前入组的为建模组(n=149例),之后的为验证组(n=72例)。采集患者的一般临床资料及中医四诊信息,并进行中医证素辨别。使用R软件4.0.4版本构建客观缓解率的列线图临床预测模型,通过受试者工作特征曲线及校准曲线来评价该模型的预测能力和区分度,并通过验证组进行外部验证。
    结果 221例患者经PD-1抑制剂治疗2~4个周期后,总的客观缓解率为44.80%。建模组多因素Logistic回归分析发现,TPS评分(OR=0.261, P=0.001)、治疗线数(OR=3.749, P=0.002)、治疗模式(OR=2.796, P=0.019)、气虚病性证素(OR=2.296, P=0.043)、阴虚病性证素(OR=3.228, P=0.005)是PD-1抑制剂近期疗效的独立预测因素。基于以上5个独立预测因子构建PD-1抑制剂近期疗效的列线图预测模型,建模组和验证组的AUC值分别为0.8317和0.7535,两组校准曲线在预测值与真实值之间符合的平均绝对误差分别为0.053和0.039,显示出较高吻合度,表明该模型的预测性能良好。
    结论 基于中医气虚病性证素、阴虚病性证素以及TPS评分、治疗线数和治疗模式构建的列线图模型是预测晚期非小细胞肺癌PD-1抑制剂近期疗效的稳定有效工具。

     

    Abstract:
    Objective To evaluate predictive factors affecting the short-term efficacy of PD-1 inhibitors in non-small cell lung cancer (NSCLC) and to construct a prediction model.
    Methods From October 2019 to November 2021, 221 patients with advanced NSCLC who met the inclusion criteria and were treated with PD-1 inhibitors were prospectively enrolled. Patients who were enrolled before May 1st, 2021 were included inthe modeling group (n=149), whereas those who enrolled thereafter were included in the validation group (n=72). The general clinical data of patients, information of the four TCM diagnoses were collected, and TCM syndrome elements were identified. R software version 4.0.4 was used in constructing a nomogram clinical prediction model of objective response rate. The predictive ability and discrimination of the model were evaluated and externally validated by using a validation group.
    Results After two to four cycles of PD-1 inhibitor therapy in 221 patients, the overall objective response rate was 44.80%. Multivariate logistic regression analysis of the modeling group showed that the TPS score (OR=0.261, P=0.001), number of treatment lines (OR=3.749, P=0.002), treatment mode (OR=2.796, P=0.019), qi deficiency disease syndrome elements (OR=2.296, P=0.043), and syndrome elements of yin deficiency disease (OR=3.228, P=0.005) were the independent predictors of the short-term efficacy of PD-1 inhibitors. Based on the above five independent predictors, a nomogram prediction model for the short-term efficacy of PD-1 inhibitors was constructed. The AUC values of the modeling and validation groups were 0.8317 and 0.7535, respectively. The calibration curves of the two groups showed good agreement between the predicted and true values. The mean absolute errors were 0.053 and 0.039, indicating that the model has good predictive performance.
    Conclusion The nomogram model constructed on the basis of the syndrome elements of Qi-deficiency disease and Yin-deficiency syndrome of TCM, as well as TPS score, number of treatment lines and treatment mode, is a stable and effective tool for predicting the short-term efficacy of PD-1 inhibitors in advanced non-small cell lung cancer.

     

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