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Re: Association between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer

  • Xinyun Li
    Affiliations
    Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhong Shan Er Lu, Guangzhou, Guangdong Province 510080, China
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  • Yan Huang
    Affiliations
    Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, Guangdong Province 510120, China
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  • Zhou Shuqin
    Affiliations
    Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, Guangdong Province 510120, China
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  • Zhiguang Chen
    Affiliations
    Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, Guangdong Province 510120, China
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  • Siwei Zhang
    Affiliations
    Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, Guangdong Province 510120, China
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      With great interest, we read the article (
      • Wu M.
      • Ma J.
      Association between imaging characteristics and different molecular subtypes of breast cancer.
      ) “association between imaging characteristics and different molecular subtypes of breast cancer?” (by Wu et al. 2016). In the present article, the authors developed three multivariate regression prediction models (Luminal A, Luminal B, and HER2 overexpressed) to investigate the independent predictive factors associated with different molecular subtypes of breast cancer. We would like to thank the authors for this highly interesting work.
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      References

        • Wu M.
        • Ma J.
        Association between imaging characteristics and different molecular subtypes of breast cancer.
        Acad Radiol. 2017; 24: 426-434
        • Zhang Z.
        Too much covariates in a multivariable model may cause the problem of overfitting.
        J Thorac Dis. 2014; 6: E196-E197
        • Babyak M.A.
        What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models.
        Psychosom Med. 2004; 66: 411-421
        • Heinze G.
        • Dunkler D.
        Five myths about variable selection.
        Transpl Int. 2017; 30: 6-10