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Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis

    Danxiang Chen‡

    Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China

    ‡Authors contributed equally

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    ,
    Xia Liu‡

    Department of Anesthesia, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China

    ‡Authors contributed equally

    Search for more papers by this author

    ,
    Chunlei Hu‡

    Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China

    ‡Authors contributed equally

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    ,
    Rutian Hao

    Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China

    ,
    Ouchen Wang

    **Author for correspondence:

    E-mail Address: woc506@126.com

    Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China

    &
    Yanling Xiao

    *Author for correspondence:

    E-mail Address: 13587631373@163.com

    Department of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China

    Published Online:https://doi.org/10.2217/fon-2022-0333

    Aim: This study aimed to predict axillary metastasis using radiology features in dynamic contrast-enhanced MRI. Methods: This study included 243 breast lesions confirmed as malignant based on axillary status. Most outcome-predictive features were selected using four machine-learning algorithms. Receiver operating characteristic analysis was used to reflect diagnostic performance. Results: Least absolute shrinkage and selection operator was used to dimensionally reduce 1137 radiomics features to three features. Three optimal radiomics features were used to model construction. The logistic regression model achieved an accuracy of 97% and 85% in the training and test groups. Clinical utility was evaluated using decision curve analysis. Conclusion: The novel combination of radiomics analysis and machine-learning algorithm could predict axillary metastasis and prevent invasive manipulation.

    Papers of special note have been highlighted as: • of interest; •• of considerable interest

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