Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method

Zilong Xu; Qiwei Yang; Minghao Li; Jiabing Gu; Changping Du; Yang Chen; Baosheng Li

doi:10.3389/fonc.2022.829041

Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method

Front Oncol. 2022 Feb 16:12:829041. doi: 10.3389/fonc.2022.829041. eCollection 2022.

Authors

Zilong Xu¹, Qiwei Yang², Minghao Li², Jiabing Gu¹, Changping Du¹, Yang Chen¹, Baosheng Li^{1

3}

Affiliations

¹ Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China.
² Laboratory of Radiation Oncology, School of Medicine, Shandong University, Jinan, China.
³ Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

Abstract

Purpose: The expression of human epidermal growth factor receptor 2 (HER2) in breast cancer is critical in the treatment with targeted therapy. A 3-block-DenseNet-based deep learning model was developed to predict the expression of HER2 in breast cancer by ultrasound images.

Methods: The data from 144 breast cancer patients with preoperative ultrasound images and clinical information were retrospectively collected from the Shandong Province Tumor Hospital. An end-to-end 3-block-DenseNet deep learning classifier was built to predict the expression of human epidermal growth factor receptor 2 by ultrasound images. The patients were randomly divided into a training (n = 108) and a validation set (n = 36).

Results: Our proposed deep learning model achieved an encouraging predictive performance in the training set (accuracy = 85.79%, AUC = 0.87) and the validation set (accuracy = 80.56%, AUC = 0.84). The effectiveness of our model significantly exceeded the clinical model and the radiomics model. The score of the proposed model showed significant differences between HER2-positive and -negative expression (p < 0.001).

Conclusions: These results demonstrate that ultrasound images are predictive of HER2 expression through a deep learning classifier. Our method provides a non-invasive, simple, and feasible method for the prediction of HER2 expression without the manual delineation of the regions of interest (ROI). The performance of our deep learning model significantly exceeded the traditional texture analysis based on the radiomics model.

Keywords: DenseNet; breast cancer; deep learning; human epidermal growth factor receptor 2; ultrasound.