Radiomics models for PD-L1 Level prediction in breast cancer based on dynamic contrast-enhanced MRI
10.3760/cma.j.cn113855-20240326-00223
- VernacularTitle:基于多模态MRI影像组学预测模型预测乳腺癌PD-L1表达水平的研究
- Author:
Xuege HU
1
;
Yuan PENG
;
Yulu LIU
;
Dingbao CHEN
;
Yi WANG
;
Shu WANG
Author Information
1. 北京大学人民医院乳腺外科，北京 100044
- Keywords:
Breast neoplasms;
Magnetic resonance imaging;
Programmed cell death ligand-1;
Radiomics;
Machine learning
- From:
Chinese Journal of General Surgery
2024;39(8):620-625
- CountryChina
- Language:Chinese
-
Abstract:
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.
