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Clinical Article
Prediction of HER-2 expression in breast cancer patients based on DCE-MRI intratumor and peritumoral imaging combined with TIC typing and Ki-67
ZHANG Chengmeng  DING Zhimin  CHEN Peng  LIU Qifeng  REN Chao 

Cite this article as: ZHANG C M, DING Z M, CHEN P, et al. Prediction of HER-2 expression in breast cancer patients based on DCE-MRI intratumor and peritumoral imaging combined with TIC typing and Ki-67[J]. Chin J Magn Reson Imaging, 2023, 14(4): 68-75. DOI:10.12015/issn.1674-8034.2023.04.012.

[Abstract] Objective To investigate the value of dynamic contrast enhancement MRI (DCE-MRI) based intratumoral and peritumoral radiomics models in combination with clinical and imaging indicators to predict the expression status of human epidermal growth factor receptor 2 (HER-2) in breast cancer patients.Materials and Methods A total of 272 patients' information with pathologically confirmed breast cancer from June 2018 to September 2022 were retrospectively collected, including 139 patients with positive HER-2 and 133 patients with negative HER-2. All cases underwent DCE-MRI examination before treatment. All 272 patients were divided into training set and validation set with a ratio of 7:3 by complete randomization method. In the training set Pearson correlation coefficients, recursive feature elimination and logistic regression were used to perform dimensionality reduction and model construction of intratumoral and peritumoral radiomics data. Multivariate logistic regression was used to screen the independent risk factors in clinical and imaging data, so as to construct the clinical model. Finally, the combined model was constructed by using intratumoral, peritumoral and clinical features. Area under the curve (AUC) was used to evaluate the efficacy of the model, and decision curve analysis (DCA) was used to evaluate the clinical value of the model.Results The AUC of clinical model, intratumoral model, peritumoral model, intratumoral + peritumoral model and combined model in the training set were 0.736, 0.784, 0.806, 0.831, 0.854, and the accuracy was 69.5%, 70.5%, 75.8%, 73.7%, 76.8%, respectively. The sensitivity was 87.6%, 53.6%, 71.1%, 62.9%, 72.2%, and the specificity was 50.5%, 88.2%, 80.6%, 84.9%, 81.7%, respectively. In the verification set, the AUC was 0.731, 0.724, 0.713, 0.780, 0.799, the accuracy was 73.2%, 70.7%, 68.3%, 73.1%, 78.0%, and the sensitivity was 76.2%, 61.9%, 88.1%, 76.2%, 78.6%, respectively. The specificity was 70.0%, 80.0%, 47.5%, 70.0% and 77.5%, respectively. By DeLong's test, in the training set there were statistically significant differences between combined model and the clinical model, the intratumoral model and the peritumoral model (Z=3.660, 2.791, 2.201, P=0.0003, 0.005, 0.028). There was no significant difference between the combined model and the intratumoral + peritumoral model (Z=1.583, P=0.114). The results showed that the combined model in the training set and validation set was better than the clinical model, intratumoral model, peritumoral model and intratumoral + peritumoral model in predicting the status of HER-2. DCA showed that the combined model had higher clinical utility than the clinical model, intratumoral model, peritumoral model and intratumoral + peritumoral model at risk thresholds of 13%-60% in the training set.Conclusions The combined model based on DCE-MRI intratumoral and peritumoral radiomics combined with clinical and imaging features can better predict the expression status of HER-2 in breast cancer patients.
[Keywords] radiomics;time-signal intensity curve;human epidermal growth factor receptor 2;predictive model;breast cancer;magnetic resonance imaging

ZHANG Chengmeng   DING Zhimin*   CHEN Peng   LIU Qifeng   REN Chao  

Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, China

Corresponding author: Ding ZM, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Medical Emollient Research Project of Red Cross Foundation of China (No. XM_HR_YXFN_2021_05_24); Health Research Project of Anhui Province (No. AHWJ2022b044).
Received  2022-11-20
Accepted  2023-04-11
DOI: 10.12015/issn.1674-8034.2023.04.012
Cite this article as: ZHANG C M, DING Z M, CHEN P, et al. Prediction of HER-2 expression in breast cancer patients based on DCE-MRI intratumor and peritumoral imaging combined with TIC typing and Ki-67[J]. Chin J Magn Reson Imaging, 2023, 14(4): 68-75. DOI:10.12015/issn.1674-8034.2023.04.012.

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