Magnetic resonance imaging texture analysis classification of primary breast cancer

S. A. Waugh (Lead / Corresponding author), C. A. Purdie, L. B. Jordan, S. Vinnicombe, R. A. Lerski, P. Martin, A. M. Thompson

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    Abstract

    Objectives: Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Methods: Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Results: Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Conclusion: Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. 

    Original languageEnglish
    Pages (from-to)322-330
    Number of pages9
    JournalEuropean Radiology
    Volume26
    Issue number2
    Early online date12 Jun 2015
    DOIs
    Publication statusPublished - Feb 2016

    Keywords

    • Breast cancer
    • Classification
    • Histological subtypes and immunohistochemical profiles
    • Magnetic Resonance Imaging (MRI)
    • Texture analysis (TA)

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    Waugh, S. A., Purdie, C. A., Jordan, L. B., Vinnicombe, S., Lerski, R. A., Martin, P., & Thompson, A. M. (2016). Magnetic resonance imaging texture analysis classification of primary breast cancer. European Radiology, 26(2), 322-330. https://doi.org/10.1007/s00330-015-3845-6