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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Breast Cancer
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1385352
This article is part of the Research Topic The Exciting Opportunities and Challenges for Targeting “HER2 low” Breast Cancers and Beyond View all articles

Preliminary study on DCE-MRI radiomics analysis for differentiation of HER2low and HER2-zero breast cancer

Provisionally accepted
Liang Yin Liang Yin 1Yun Zhang Yun Zhang 2,3*Xi Wei Xi Wei 4*Zakari Shaibu Zakari Shaibu 2*Lingling Xiang Lingling Xiang 3*Ting Wu Ting Wu 4Qing Zhang Qing Zhang 5*Rong Qin Rong Qin 6*Xiuhong Shan Xiuhong Shan 3*
  • 1 Department of Breast Surgery, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
  • 2 School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China
  • 3 Department of Medical Imaging, Jiangsu University Affiliated People's Hospital, Zhenjiang, China
  • 4 Department of Pathology, Jiangsu University Affiliated People’s Hospital, Zhenjiang, Jiangsu Province, China
  • 5 Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
  • 6 Department of Medical Oncology, Jiangsu University Affiliated People's Hospital, Zhenjiang, China

The final, formatted version of the article will be published soon.

    Purpose: This study aims to evaluate the utility of radiomic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in distinguishing HER2low from HER2-zero breast cancer.: We retrospectively analyzed 118 MRI cases, including 78 HER2-low and 40 HER2-zero patients confirmed by immunohistochemistry or fluorescence in situ hybridization. From each DCE-MRI case, 960 radiomic features were extracted. These features were screened and reduced using intraclass correlation coefficient, Mann-Whitney U test, and least absolute shrinkage to establish rad-scores. Logistic regression (LR) assessed the model's effectiveness in distinguishing HER2low from HER2-zero. A clinicopathological MRI characteristic model was constructed using univariate and multivariate analysis, and a nomogram was developed combining rad-scores with significant MRI characteristics. Model performance was evaluated using the receiver operating characteristic (ROC) curve, and clinical benefit was assessed with decision curve analysis. Results: The radiomics model, clinical model, and nomogram successfully distinguished between HER2-low and HER2-zero. The radiomics model showed excellent performance, with area under the curve (AUC) values of 0.875 in the training set and 0.845 in the test set, outperforming the clinical model (AUC = 0.691 and 0.672, respectively). HER2 status correlated with increased rad-score and Time Intensity Curve (TIC). The nomogram outperformed both models, with AUC, sensitivity, and specificity values of 0.892, 79.6%, and 82.8% in the training set, and 0.886, 83.3%, and 90.9% in the test set. Conclusions: The DCE-MRI-based nomogram shows promising potential in differentiating HER2-low from HER2-zero status in breast cancer patients.

    Keywords: HER2-low, HER2-zero, breast cancer, DCE-MRI, Radiomics analysis, nomogram

    Received: 12 Feb 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Yin, Zhang, Wei, Shaibu, Xiang, Wu, Zhang, Qin and Shan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yun Zhang, School of Medicine, Jiangsu University, Zhenjiang, 2012013, Jiangsu Province, China
    Xi Wei, Department of Pathology, Jiangsu University Affiliated People’s Hospital, Zhenjiang, Jiangsu Province, China
    Zakari Shaibu, School of Medicine, Jiangsu University, Zhenjiang, 2012013, Jiangsu Province, China
    Lingling Xiang, Department of Medical Imaging, Jiangsu University Affiliated People's Hospital, Zhenjiang, China
    Qing Zhang, Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
    Rong Qin, Department of Medical Oncology, Jiangsu University Affiliated People's Hospital, Zhenjiang, China
    Xiuhong Shan, Department of Medical Imaging, Jiangsu University Affiliated People's Hospital, Zhenjiang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.