Title:Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in
Breast Cancer
Volume: 20
Author(s): Hao Li, Yan Hou, Lin-Yan Xue, Wen-Long Fan, Bu-Lang Gao and Xiao-Ping Yin*
Affiliation:
- 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
Keywords:
Radiomics, Breast cancer, MRI, HER2 expression, Death, Women.
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.
