Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer

Author:

Li Hao1,Hou Yan1,Xue Lin-Yan2,Fan Wen-Long2,Gao Bu-Lang1,Yin Xiao-Ping1

Affiliation:

1. Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation-related Tumors, No. 212 Eastern Yuhua Road, Baoding City, Hebei Province, 071000, People’s Republic of China

2. College of Quality and Technical Supervision, Hebei University, No. 180, Wusi East Road, Baoding City, Hebei Province, 071000, People’s Republic of China

Abstract

Objective: To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer. Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model. Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value. Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

Funder

Outstanding Young Scientific Research and Innovation Team of Hebei University

Publisher

Bentham Science Publishers Ltd.

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