Predictive value of DCE-MRI features of breast cancer on hormone receptor, HER-2 and triple negative breast cancer

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• Clinical Article •

DAI Ting SU Tong WANG Rui YANG Hongyu ZHAO Qing LÜ Fajin OUYANG Zubin

Cite this article as: DAI T, SU T, WANG R, et al. Predictive value of DCE-MRI features of breast cancer on hormone receptor, HER-2 and triple negative breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(4): 57-67. DOI:10.12015/issn.1674-8034.2023.04.011.

Abstract

[Abstract] Objective To explore the predictive value of dynamic contrast-enhanced MRI (DCE-MRI) image features combined with quantitative parameters in breast cancer hormone receptor, human epidermal growth factor receptor-2 (HER-2) and triple negative breast cancer (TNBC).Materials and Methods The clinical data, DCE-MRI images, and quantitative parameters of 316 patients with breast cancer who underwent breast MRI were collected retrospectively, 242 patients in the training group and 74 patients in the validation group. According to the expression of hormone receptor, they were divided into two groups: hormone receptor positive group and negative group. HER-2 positive group and HER-2 negative group were determined by HER-2 expression. TNBC group and non-triple negative breast cancer (NTNBC) group devided according to hormone receptor and HER-2 expression status. In training group, the differences of image features and quantitative parameters among different immunohistochemical results and molecular types of breast cancer were compared. Some imaging features and quantitative parameters selected by logistic regression were used to predict hormone receptor positive, HER-2 positive and TNBC, and then the nomogram models were constructed. The verification group was used for verification. Receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the prediction model.Results In the training group, DCE-MRI image features such as lesion size, shape, spiculated margin, internal enhancement characteristics (IEC), non-mass enhancement (NME), sub-focus, increased peripheral vascularity, axillary lymphadenopathy and nipple change were significantly different among the immunohistochemical results and molecular subtypes of breast cancer (all P＜0.05). The quantitative parameter volume fraction of extravascular extra vascular space (Ve) of the hormone receptor positive group was larger than that of hormone receptor negative group (P＜0.001), while Ve of the TNBC group was smaller than that of NTNBC group (P＜0.001). Spiculated margin (P＜0.001), IEC (P=0.041), NME (P=0.006) and axillary lymphadenopathy (P=0.029) can distinguish between hormone receptor positive and negative breast cancer. In training group, combined with the above characteristics, a hormone receptor positive breast cancer prediction model was constructed, and the area under curve (AUC) of prediction model was 0.746 (95% CI: 0.681-0.811), sensitivity of 82.8%, specificity of 52.9%, accuracy of 72.3%. In verification group, the AUC of 0.829 (95% CI: 0.730-0.926), and the sensitivity, specificity and accuracy were 78.7%, 74.1% and 77.0%, respectively. Mass shape (P=0.050), spiculated margin (P=0.016), NME (P=0.013) and axillary lymphadenopathy (P＜0.001) were significantly associated with HER-2 positive breast cancer. In training group, the AUC of HER-2 positive breast cancer prediction model combined with these above characteristics was 0.733 (95% CI: 0.665-0.800), sensitivity of 55.6%, specificity of 82.0%, accuracy of 73.1%. In verification group, the AUC of HER-2 positive breast cancer prediction model was 0.649 (95% CI: 0.507-0.791), the sensitivity of 63.6%, and the specificity of 64.9%, the accuracy of 64.9%. The TNBC prediction model was combined with lesion size (P=0.010), nipple change (P=0.016) and Ve (P=0.007). In training group, the AUC of TNBC prediction model was 0.689 (95% CI: 0.600-0.779), and the sensitivity, specificity and accuracy were 80.0%, 52.5% and 57.0%, respectively. The AUC of prediction model in verification group was 0.794 (95% CI: 0.662-0.927), The sensitivity, specificity and accuracy were 86.7%, 67.8% and 71.6%, respectively. The calibration curves of hormone receptor positive, HER-2 positive and TNBC predictive models in training group and verification group showed that the consistency of the models was high. The DCA curve shows that these predictive models could be beneficial among a larger threshold range.Conclusions Some image features and quantitative parameters derived from DCE-MRI are related to the expression of hormone receptor and HER-2, which has the potential to non-invasively predict hormone receptor positive, HER-2 positive and TNBC.

[Keywords] breast cancer；triple-negative breast cancer；magnetic resonance imaging；dynamic contrast-enhanced；quantitative parameter；hormone receptor；human epidermal growth factor receptor 2；prediction model

Contributor Information

DAI Ting¹ SU Tong¹ WANG Rui¹ YANG Hongyu² ZHAO Qing¹ LÜ Fajin¹ OUYANG Zubin^1*

¹ Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

² Department of Radiology, Changshou District People's Hospital, Chongqing 401220, China

Corresponding author: Ouyang ZB, E-mail: ouyangzubin@aliyun.com

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS National Key R & D Program (No. 2020YFA0714002); Medical Scientific Research Project of Chongqing Health and Family Planning Commission (No. 2015MSXM011).

Received 2022-11-03

Accepted 2023-04-06

DOI: 10.12015/issn.1674-8034.2023.04.011

Text

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