Deep learning predicts HER2 status in invasive breast cancer from multimodal ultrasound and MRI.

Journal: Biomolecules & biomedicine
Published Date: May 16, 2025

Abstract

The preoperative human epidermal growth factor receptor type 2 (HER2) status of breast cancer is typically determined by pathological examination of a core needle biopsy, which influences the efficacy of neoadjuvant chemotherapy (NAC). However, the highly heterogeneous nature of breast cancer and the limitations of needle aspiration biopsy increase the instability of pathological evaluation. The aim of this study was to predict HER2 status in preoperative breast cancer using deep learning (DL) models based on ultrasound (US) and magnetic resonance imaging (MRI). The study included women with invasive breast cancer who underwent US and MRI at our institution between January 2021 and July 2024. US images and dynamic contrast-enhanced T1-weighted MRI images were used to construct DL models (DL-US: the DL model based on US; DL-MRI: the model based on MRI; and DL-MRI&US: the combined model based on both MRI and US). All classifications were based on postoperative pathological evaluation. Receiver operating characteristic analysis and the DeLong test were used to compare the diagnostic performance of the DL models. In the test cohort, DL-US differentiated the HER2 status of breast cancer with an AUC of 0.842 (95% CI: 0.708-0.931), and sensitivity and specificity of 89.5% and 79.3%, respectively. DL-MRI achieved an AUC of 0.800 (95% CI: 0.660-0.902), with sensitivity and specificity of 78.9% and 79.3%, respectively. DL-MRI&US yielded an AUC of 0.898 (95% CI: 0.777-0.967), with sensitivity and specificity of 63.2% and 100.0%, respectively.

Authors

  • Yuhong Fan
    Department of Ultrasound Diagnosis, Daping Hospital, Army Medical University, Chongqing, China.
  • Kaixiang Sun
    School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China.
  • Yao Xiao
    School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China; Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Peng Zhong
    Department of Pathology, Daping Hospital, Army Medical University, Chongqing, China.
  • Yun Meng
    Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Zhenwei Du
    Department of Medical engineering, Daping Hospital, Army Medical University, Chongqing, China.
  • Jingqin Fang
    Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China.

Keywords

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