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目的:探讨建立基于核磁共振图像（magnetic resonance imaging，MRI）的影像组学以及结合临床特征的综合模型预测乳腺癌程序性细胞死亡配体1（programmed cell death ligand-1，PD-L1）水平的可行性。方法:本研究回顾性纳入了139例乳腺癌患者，包含79例PD-L1阴性及60例PD-L1阳性患者。将这些患者按照7∶3的比例随机分为训练集（97例）与验证集（42例）。提取MRI影像组学特征并通过方差分析和套索回归的方法对影像组学特征进行筛选与降维，采用逻辑回归作为分类器构建预测PD-L1的影像组学模型以及结合临床特征的综合模型。使用受试者工作特征曲线评估模型效能，采用Delong test进行模型间效能比较。结果:影像组学模型在训练集和验证集中均表现出良好的性能，AUC分别为0.847（95% CI：0.770~0.924）和0.826（95% CI：0.699~0.954）；综合预测模型显示出更优的结果，AUC分别为0.919（95% CI：0.868~0.970）和0.882（95% CI：0.782~0.982）；但两个模型的预测效能差异无统计学意义（ Z=1.32， P=0.19）。 结论:基于MRI和临床特征的影像组学模型可以为治疗前无创评估乳腺癌PD-L1水平提供帮助，有望作为病理学诊断的补充为临床决策提供支持。

Objective:To investigate the feasibility of developing a radiomics model based on MRI and clinical features to predict the PD-L1 level in breast cancer.Methods:A total of 139 consecutive patients with breast cancer confirmed by pathology were enrolled retrospectively, including 79 PD-L1 negative patients and 60 PD-L1 positive patients. All patients were randomly assigned to a training dataset( n=97) and a validation dataset( n=42). Radiomics features were extracted from dynamic contrast-enhanced MRI. Radiomics feature selection was generated through the analysis of variance(ANOVA), least absolute shrinkage and selection operator(LASSO). Radiomics model and comprehensive model were developed for predicting the level of PD-L1. The receiver operating characteristic curve(ROC) was used to evaluate the predictive capacity of the models. Results:The radiomics model exhibited good performance in the training and validation datasets, with an area under the curve(AUC) of 0.847(95% confidence interval CI: 0.770-0.924) and 0.826(95% CI: 0.699-0.954), respectively. Compared with the radiomics model, the clinical feature combined prediction model showed better results, with AUC of 0.919(95% CI: 0.868-0.970) and 0.882(95% CI: 0.782-0.982), respectively, but without statistically significant difference( Z=1.32, P=0.19), respectively, but without statistically significant difference. Conclusions:The radiomi.Conclusions:The radiomics model has a certain value in preoperative prediction of PD-L1 expression level in breast cancer, which may be used as a supplement and improvement to the pathological gold standard to provide support for clinical decision-making.