Advertisement

Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features

  • Wenjuan Ma
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Department of Biomedical and Engineering, Tianjin Medical University, Tianjin, China
    Search for articles by this author
  • Yumei Zhao
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China
    Search for articles by this author
  • Yu Ji
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China
    Search for articles by this author
  • Xinpeng Guo
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China
    Search for articles by this author
  • Xiqi Jian
    Affiliations
    Department of Biomedical and Engineering, Tianjin Medical University, Tianjin, China
    Search for articles by this author
  • Peifang Liu
    Correspondence
    Address correspondence to: S.W.; P.L.
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China

    Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China
    Search for articles by this author
  • Shandong Wu
    Correspondence
    Address correspondence to: S.W.; P.L.
    Affiliations
    Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213
    Search for articles by this author
Published:March 08, 2018DOI:https://doi.org/10.1016/j.acra.2018.01.023

      Rationale and Objectives

      This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer.

      Materials and Methods

      In this institutional review board–approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions. A set of 39 quantitative radiomic features, including morphologic, grayscale statistic, and texture features, were extracted from the segmented lesion area. Three binary classifications of the subtypes were performed: triple-negative vs non–triple-negative, HER2-enriched vs non–HER2-enriched, and luminal (A + B) vs nonluminal. The Naive Bayes machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method was used to select the most predictive features for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve and accuracy.

      Results

      The model that used the combination of both the craniocaudal and the mediolateral oblique view images achieved the overall best performance than using either of the two views alone, yielding an area under receiver operating characteristic curve (or accuracy) of 0.865 (0.796) for triple-negative vs non–triple-negative, 0.784 (0.748) for HER2-enriched vs non–HER2-enriched, and 0.752 (0.788) for luminal vs nonluminal subtypes. Twelve most predictive features were selected by the least absolute shrink age and selection operator method and four of them (ie, roundness, concavity, gray mean, and correlation) showed a statistical significance (P< .05) in the subtype classification.

      Conclusions

      Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Goldhirsch A.
        • Wood W.C.
        • Coates A.S.
        • et al.
        Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011.
        Ann Oncol. 2013; 18: 1133-1144
        • Lam S.W.
        • Jimenez C.R.
        • Boven E.
        Breast cancer classification by proteomic technologies: current state of knowledge.
        Cancer Treat Rev. 2014; 40: 129-138
        • Huber K.E.
        • Carey L.A.
        • Wazer D.E.
        Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy.
        Semin Radiat Oncol. 2009; 19: 204-210
        • Lambin P.
        • Riosvelazquez E.
        • Leijenaar R.
        • et al.
        Radiomics: extracting more information from medical images using advanced feature analysis.
        Eur J Cancer. 2012; 48: 441-446
        • Çelebi F.
        • Pilancı K.N.
        • Ordu Ç.
        • et al.
        The role of ultrasonographic findings to predict molecular subtype, histologic grade, and hormone receptor status of breast cancer.
        Diagn Interv Radiol. 2015; 21: 448-453
        • Uematsu T.
        • Kasami M.
        • Yuen S.
        Triple-negative breast cancer: correlation between MR imaging and pathologic findings.
        Radiology. 2009; 250: 638-647
        • Wu M.
        • Jie M.
        Association between imaging characteristics and different molecular subtypes of breast cancer.
        Acad Radiol. 2016; 24: 426-434
        • Luck A.A.
        • Evans A.J.
        • James J.J.
        • et al.
        Breast carcinoma with basal phenotype: mammographic findings.
        AJR Am J Roentgenol. 2008; 191: 346-351
        • Kumar V.
        • Gu Y.
        • Basu S.
        • et al.
        Radiomics: the process and the challenges.
        Magn Reson Imaging. 2012; 30: 1234-1248
        • Limkin E.J.
        • Sun R.
        • Dercle L.
        • et al.
        Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.
        Ann Oncol. 2017; 28: 1191-1206
        • Li H.
        • Zhu Y.
        • Burnside E.S.
        • et al.
        Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.
        NPJ Breast Cancer. 2016; 2: 16012
        • Wu J.
        • Sun X.
        • Wang J.
        • et al.
        Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: model discovery and external validation.
        J Magn Reson Imaging. 2017; 46: 1017-1027
        • Sutton E.J.
        • Dashevsky B.Z.
        • Oh J.H.
        • et al.
        Breast cancer molecular subtype classifier that incorporates MRI features.
        J Magn Reson Imaging. 2016; 44: 122-129
        • Tang J.
        • Rangayyan R.M.
        • Xu J.
        • et al.
        Computer-aided detection and diagnosis of breast cancer with mammography: recent advances.
        IEEE Trans Inf Technol Biomed. 2009; 13: 236-251
        • Baltzer P.A.T.
        • Dietzel M.
        • Kaiser W.A.
        A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography.
        Eur Radiol. 2013; 23: 2051-2060
        • Mu T.
        • Nandi A.K.
        • Rangayyan R.M.
        Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers.
        J Digit Imaging. 2008; 21: 153-169
        • Karsten R.
        • Ewert B.
        A feature set for cytometry on digitized microscopic images.
        Anal Cell Pathol. 2003; 25: 1-36
        • Haralick R.
        • Shanmugam K.
        • Dinstein I.
        Texture parameters for image classification.
        IEEE Trans SMC. 1973; 3: 610-621
        • Peter H.
        Machine learning in action.
        Manning Publications, Shelter Island, NY2012
        • Tibshirani R.
        The lasso method for variable selection in the Cox model.
        Stat Med. 1997; 16: 385-395
        • Chawla V, N.
        • Bowyer K.W.
        • Hall L.O.
        • et al.
        SMOTE: synthetic minority over-sampling technique.
        J Artif Intell Res. 2002; 16: 321-357
        • Emaminejad N.
        • Wang Y.
        • Qian W.
        • et al.
        Applying a radiomics approach to predict prognosis of lung cancer patients.
        in: Medical imaging 2016: computer-aided diagnosis. 2016 (97851E)
        • Maciejewski T.
        • Stefanowski J.
        Local neighbourhood extension of SMOTE for mining imbalanced data.
        in: Proceeding of the IEEE symposium on computational intelligence and data mining. IEEE, Paris, France2011: 104-111
        • Mazurowski M.A.
        • Zhang J.
        • Grimm L.J.
        • et al.
        Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
        Radiology. 2014; 273: 365-372
        • Chang R.F.
        • Chen H.H.
        • Chang Y.C.
        • et al.
        Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI.
        Magn Reson Imaging. 2016; 34: 809-819
        • Fan M.
        • Li H.
        • Wang S.
        • et al.
        Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.
        PLoS ONE. 2017; 12 (e0171683)