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CT影像加权组学评分预测非小细胞肺癌的免疫治疗疗效
CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer

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朱振宸 1   陈闽江 2   宋兰 1 *   王金华 1   胡歌 3   韩伟 4   谭卫雄 5   周振 5   隋昕 1   宋伟 1   金征宇 1  
文摘 目的通过治疗前胸部增强CT肺内多病灶组学特征构建加权组学评分模型,预测非小细胞肺癌患者接受程序性死亡受体1 (PD-1)/ PD配体1 (PD-L1)免疫治疗的疗效。方法回顾性收集北京协和医院2015年6月至2022年2月接受PD-1/ PD-L1免疫治疗的非小细胞肺癌病例。根据临床结局分为有效组(部分缓解或完全缓解)和无效组(疾病稳定或疾病进展)。从治疗前动脉期CT图像提取多病灶影像组学,通过基于注意力机制的多示例学习算法在病例水平获得加权组学特征,采用Logistic回归建立加权评分模型。采用受试者工作特征曲线下面积(AUC)分别比较加权评分模型、PD-L1模型、临床模型、加权评分+ PD-L1模型和综合模型预测肿瘤治疗疗效的效能。结果最终纳入237例患者,随机分为训练集(n =165)和测试集(n =72)。训练集和测试集患者的平均年龄分别为(64 ±9)岁和(62 ±8)岁。加权评分模型预测免疫治疗疗效的AUC在训练集和测试集中分别为0.85和0.80,高于PD-L1-1模型(Z = 37.30, P < 0.001和Z = 5.69, P =0.017)、PD-L1-50模型(Z =38.36, P <0.001和Z =17.99, P <0.001)和临床模型(Z =11.40, P < 0.001和Z =5.76, P =0.016)的预测能力,与加权评分+ PD-L1模型和综合模型比较差异无统计学意义(P均>0.05)。结论基于治疗前增强CT影像多病灶加权组学评分能够预测非小细胞肺癌患者接受免疫治疗的疗效。
其他语种文摘 Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1 (PD-1)/ PD-ligand 1 (PD-L1)immunotherapy in patients with non-small cell lung cancer. Methods The patients with non-small cell lung cancer treated by PD-1/ PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders (partial or complete response)and non-responders (stable or progressive disease). Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase, and then weighted and summed by an attention-based multiple instances learning algorithm. Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated. The area under the receiver operating characteristic curve (AUC)was used to compare the weighted radiomic scoring model, PD-L1 model, clinical model, weighted radiomic scoring + PD-L1 model, and comprehensive prediction model. Results A total of 237 patients were included in the study and randomized into a training set (n =165)and a test set (n =72), with the mean ages of (64 ±9)and (62 ±8)years, respectively. The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set, respectively, which was higher than that of the PD-L1-1 model (Z = 37.30, P < 0.001 and Z = 5.69, P = 0.017), PD-L1-50 model (Z = 38.36, P < 0.001 and Z = 17.99, P < 0.001), and clinical model (Z = 11.40, P <0.001 and Z =5.76, P =0.016). The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model (both P >0.05). Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.
来源 中国医学科学院学报 ,2023,45(5):794-802 【核心库】
DOI 10.3881/j.issn.1000-503X.15705
关键词 非小细胞肺癌 ; 免疫检查点抑制剂 ; CT ; 加权组学评分
地址

1. 中国医学科学院北京协和医学院北京协和医院放射科, 北京, 100730  

2. 中国医学科学院北京协和医学院北京协和医院呼吸与危重症医学科, 北京, 100730  

3. 中国医学科学院北京协和医学院北京协和医院转化医学中心, 北京, 100730  

4. 中国医学科学院基础医学研究所流行病与卫生统计学系, 北京, 100005  

5. 北京深睿博联科技有限责任公司, 北京, 100081

语种 中文
文献类型 研究性论文
ISSN 1000-503X
学科 临床医学;肿瘤学
基金 中国医学科学院医学与健康科技创新工程临床与转化医学研究专项重点项目 ;  国家自然科学基金 ;  北京市科学技术委员会AI +健康协同创新培育项目 ;  中华国际医学交流基金会SKY影像科研基金 ;  科技创新2030-新一代人工智能重大项目
文献收藏号 CSCD:7587661

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引证文献 1 篇

1 孙天生 非小细胞肺癌免疫治疗疗效的预测生物标志物研究进展 中国肺癌杂志,2024,27(6):459-465
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