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Original Investigation|Articles in Press

Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study

  • Author Footnotes
    † Zhongyi Wang, Haicheng Zhang contributed equally to this work.
    Zhongyi Wang
    Footnotes
    † Zhongyi Wang, Haicheng Zhang contributed equally to this work.
    Affiliations
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
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  • Author Footnotes
    † Zhongyi Wang, Haicheng Zhang contributed equally to this work.
    Haicheng Zhang
    Footnotes
    † Zhongyi Wang, Haicheng Zhang contributed equally to this work.
    Affiliations
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
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  • Fan Lin
    Affiliations
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000

    Institute of medical imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
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  • Ran Zhang
    Affiliations
    Artificial Intelligence and Clinical Innovation Institute, Huiying Medical Technology Co., Ltd, P. R. China, 100192
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  • Heng Ma
    Affiliations
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
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  • Yinghong Shi
    Affiliations
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
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  • Ping Yang
    Affiliations
    Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
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  • Kun Zhang
    Affiliations
    Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
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  • Feng Zhao
    Affiliations
    School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China, 264000
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  • Author Footnotes
    # Haizhu Xie, Ning Mao contributed equally to this work
    Ning Mao
    Footnotes
    # Haizhu Xie, Ning Mao contributed equally to this work
    Affiliations
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
    Search for articles by this author
  • Author Footnotes
    # Haizhu Xie, Ning Mao contributed equally to this work
    Haizhu Xie
    Correspondence
    Address correspondence to H.X.
    Footnotes
    # Haizhu Xie, Ning Mao contributed equally to this work
    Affiliations
    Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
    Search for articles by this author
  • Author Footnotes
    † Zhongyi Wang, Haicheng Zhang contributed equally to this work.
    # Haizhu Xie, Ning Mao contributed equally to this work
Published:April 21, 2023DOI:https://doi.org/10.1016/j.acra.2023.02.005
Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study
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      Rationale and Objectives

      This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis.

      Materials and Methods

      A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly.

      Results

      In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor + 10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor + 10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists.

      Conclusion

      The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.

      Key Words

      Abbreviations:

      ALN (Axillary lymph node), ALND (Axillary lymph node dissection), CEM (Contrast-enhanced mammography), DCA (Decision curve analysis), ER (Estrogen receptor), ICC (Intraclass correlation coefficient), IDI (Integrated discrimination improvement), HER-2 (Human epidermal growth factor receptor), LASSO (Least absolute shrinkage and selection operator), NRI (Net reclassification improvement), PR (Progesterone receptor), SLN (Sentinel lymph node), SLNB (Sentinel lymph node biopsy), VIF (Variance inflation factor)
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      Article info

      Publication history

      Published online: April 21, 2023
      Accepted: February 3, 2023
      Received in revised form: February 3, 2023
      Received: November 14, 2022

      Publication stage

      In Press Corrected Proof

      Identification

      DOI: https://doi.org/10.1016/j.acra.2023.02.005

      Copyright

      © 2023 Published by Elsevier Inc. on behalf of The Association of University Radiologists.

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