PD-L1 combined with CT radiomics and deep learning features to predict efficacy of immunotherapy in patient with non-small cell lung cancer
10.3969/j.issn.1002-1671.2024.08.007
- VernacularTitle:PD-L1联合CT影像组学与深度学习特征预测非小细胞肺癌免疫治疗疗效
- Author:
Liyou HUANG
1
;
Xiancong GAO
;
Xiaowei JIN
Author Information
1. 徐州医科大学附属宿迁医院肿瘤科,江苏 宿迁 223800
- Keywords:
radiomics;
immunotherapy;
deep learning;
computed tomography
- From:
Journal of Practical Radiology
2024;40(8):1248-1252
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the predictive value of combined PD-L1 expression,radiomics,and deep learning features for the efficacy of immunotherapy in patient with non-small cell lung cancer(NSCLC).Methods A total of 83 NSCLC patients who underwent immunotherapy were analyzed retrospectively.The volume of interest(VOI)was segmented on CT images,and features were extracted through the Pyradiomics and ResNet18 networks.The Radiomics score(Radscore)and deep learning score(Deepscore)were constructed based on the features after dimensionality reduction.Univariate and multivariate analyses were performed on the clinical parameters,Radscore,and Deepscore,and the independent predictive risk factors were selected to establish the clinical model,radiomics model,and combined prediction model,respectively.The receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)of the three models was calculated.Decision curve analysis(DCA)was used to compare the clinical practicability of the three models.Results Three radiomics features and three deep learning features were selected to calculate Radscore and Deepscore,respectively.PD-L1 expression,Radscore,and Deepscore were independent predictors of the efficacy of immunotherapy for NSCLC.The AUC of the combined prediction model in the training set and validation set were 0.885 and 0.877,respectively,which were higher than that of the clinical model(0.654 and 0.640),and the difference in AUC was statistically significant(P=0.006,0.029,respectively).The DCA showed that the combined prediction model achieved better clinical practicability at the threshold of 0-0.25 and 0.3-1.Conclusion The combined prediction model can better predict the efficacy of immunotherapy in NSCLC patients.
