基于CT影像组学评估PD-1免疫联合治疗非小细胞肺癌疗效的初步研究

Preliminary evaluation of PD-1 immunotherapy combination therapy efficacy in non-small cell lung cancer using CT-based radiomics

摘要

目的: 基于实体瘤免疫治疗疗效评价标准(immune response evaluation criteria in solid tumors，iRECIST)构建影像组学模型，以预测非小细胞肺癌(non-small cell lung cancer，NSCLC)患者对免疫联合治疗的响应情况，从而在治疗前或早期阶段实现临床获益人群的精准识别。方法: 纳入75例经病理证实为NSCLC并接受免疫联合治疗的患者，所有患者均在治疗前1个月内接受胸部CT检查，基于iRECIST对患者免疫联合治疗4~6个疗程(12~18周)后的疗效进行评估，将患者分为治疗敏感组和非敏感组。患者按2∶1的比例随机分为训练集(n=50)和测试集(n=25)，分别建立临床特征和影像组学模型。采用受试者工作特征(receiver operating characteristic，ROC)曲线、曲线下面积(area under curve，AUC)、准确率(accuracy，ACC)及决策曲线分析(decision curve analysis，DCA)对各模型的预测性能和临床应用价值进行比较。结果: 经过特征提取与筛选，共得到5个组学特征，包括2个一阶特征及3个纹理特征，经过特征融合和筛选，同时为了降低过拟合，进行3折交叉验证，最终朴素贝叶斯模型最稳定，训练集及测试集AUC分别为0.675、0.647，ACC分别为0.720、0.720。结论: 影像组学预测NSCLC患者对免疫联合治疗的疗效有一定的潜力和临床应用价值。

Abstract

Objective: To develop a radiomics-based model based on the immune response evaluation criteria in solid tumors(iRECIST)for predicting the response of patients with non-small cell lung cancer(NSCLC)to combined immunotherapy，so as to precisely screen patients who may derive clinical benefit at the pre-treatment or early treatment stage. Methods: A total of 75 patients with pathologically confirmed NSCLC and received combined immunotherapy were enrolled. All patients underwent chest computed tomography(CT)within one month before treatment. Based on the iRECIST，treatment efficacy after 4-6 cycles(12-18 weeks)of immunotherapy combination therapy was assessed，and patients were classified into the treatment-sensitive and non-sensitive groups. The patients were randomly divided into a training cohort(n=50)and a testing cohort(n=25)at a ratio of 2∶1. Clinical feature models and radiomics prediction models were developed separately. The predictive performance and clinical utility of the models were compared using receiver operating characteristic(ROC)curves，the area under the curve(AUC)，accuracy(ACC)，and decision curve analysis(DCA). Results: After feature extraction and selection，a total of five radiomics features were obtained，including two first-order features and three texture features. After feature fusion and screening，and in order to reduce over-fitting，through three-fold cross-validation，the Naive Bayes was the most stable，with an AUC of 0.675 and accuracy of 0.720 in the training set，and an AUC of 0.647 and accuracy of 0.720 in the test set，respectively. Conclusion: Radiomics has certain potential and clinical application value for predicting the efficacy of immunotherapy combination therapy in NSCLC patients.

关键词

影像组学 / 非小细胞肺癌 / PD-1 / 免疫联合治疗

Key words

radiomics / non-small cell lung cancer / PD-1 / immunotherapy combination therapy

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基金资助

医学影像人工智能专项研究基金

南京医学会第十四届放射学分会(序号13)