鼻咽癌免疫治疗反应的预测：一项使用基于MRI的影像组学特征和程序性细胞死亡配体1表达评分的对比研究
Prediction of immunotherapy response in nasopharyngeal carcinoma: a comparative study using MRI-based radiomics signature and programmed cell death ligand 1 expression score.
作者信息
Mai Hui, Li Li, Xin Xin, Jiang Zhike, Tang Yongfang, Huang Jie, Lei Yanxing, Chen Lianzhi, Dong Tianfa, Zhong Xi
机构信息
Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
Department of Otolaryngology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
出版信息
Eur Radiol. 2025 Jan 24. doi: 10.1007/s00330-025-11350-5.
OBJECTIVES
To compare an MRI-based radiomics signature with the programmed cell death ligand 1 (PD-L1) expression score for predicting immunotherapy response in nasopharyngeal carcinoma (NPC).
METHODS
Consecutive patients with NPC who received immunotherapy between January 2019 and June 2022 were divided into training (n = 111) and validation (n = 66) sets. Tumor radiomics features were extracted from pretreatment MR images. PD-L1 combined positive score (CPS) was calculated using immunohistochemistry. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and radiomics signature construction. Receiver operating characteristic (ROC) curve analysis was performed to assess prediction performance.
RESULTS
A total of eleven radiomics features with the greatest discrimination capability were identified by the LASSO algorithm to construct the radiomics signature. In predicting patients with objective response to immunotherapy, radiomics score (Rd-score) yielded a significantly higher area under the ROC curve than that of CPS in both the training (0.790 vs. 0.645, p = 0.025) and the validation (0.735 vs. 0.608, p = 0.038) sets. Multivariate analysis identified the Rd-score as an independent influencing factor in predicting immunotherapy response (odds ratio = 19.963, p < 0.001). Kaplan-Meier analysis indicated that patients with Rd-score ≥ 0.5 showed longer progression-free survival than patients with Rd-score < 0.5 (log-rank p < 0.01).
CONCLUSION
An MRI-based radiomics signature demonstrated greater efficacy than the PD-L1 expression score in predicting immunotherapy response in patients with NPC.
KEY POINTS
Question How does an MRI-based radiomics signature compare with the programmed cell death ligand 1 expression score for predicting immunotherapy response in nasopharyngeal carcinoma? Findings The MRI-based radiomics signature demonstrated superior predictive value compared with programmed cell death ligand 1 expression score in identifying immunotherapy responders. Clinical relevance MRI-based radiomics are a promising novel noninvasive tool for predicting immunotherapy outcomes in nasopharyngeal carcinoma.
目的
比较基于MRI的放射组学特征与程序性细胞死亡配体1（PD-L1）表达评分对鼻咽癌（NPC）免疫治疗反应的预测价值。
方法
将2019年1月至2022年6月期间接受免疫治疗的连续性NPC患者分为训练集（n = 111）和验证集（n = 66）。从治疗前的MR图像中提取肿瘤放射组学特征。采用免疫组织化学法计算PD-L1联合阳性评分（CPS）。使用最小绝对收缩和选择算子（LASSO）算法进行特征选择和放射组学特征构建。采用受试者工作特征（ROC）曲线分析评估预测性能。
结果
LASSO算法共识别出11个具有最大鉴别能力的放射组学特征，用于构建放射组学特征。在预测免疫治疗客观反应的患者时，放射组学评分（Rd评分）在训练集（0.790对0.645，p = 0.025）和验证集（0.735对0.608，p = 0.038）中的ROC曲线下面积均显著高于CPS。多因素分析确定Rd评分是预测免疫治疗反应的独立影响因素（优势比= 19.963，p < 0.001）。Kaplan-Meier分析表明，Rd评分≥0.5的患者无进展生存期长于Rd评分< 0.5的患者（对数秩检验p < 0.01）。
结论
基于MRI的放射组学特征在预测NPC患者免疫治疗反应方面比PD-L1表达评分具有更高的效能。
关键点
问题：基于MRI的放射组学特征与程序性细胞死亡配体1表达评分在预测鼻咽癌免疫治疗反应方面如何比较？研究结果：在识别免疫治疗反应者方面，基于MRI的放射组学特征比程序性细胞死亡配体1表达评分具有更高的预测价值。临床意义：基于MRI的放射组学是一种有前景的新型非侵入性工具，可用于预测鼻咽癌的免疫治疗结果。
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