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Magnetic resonance imaging texture analysis classification of primary breast cancer

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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.

Key Points

MR-derived entropy features, representing heterogeneity, provide important information on tissue composition.

Entropy features can differentiate between histological and immunohistochemical subtypes of breast cancer.

Differing entropy features between breast cancer subtypes implies differences in lesion heterogeneity.

Texture analysis of breast cancer potentially provides added information for decision making.

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Acknowledgments

The scientific guarantor of this publication is Dr Shelley Waugh. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional Review Board approval was not required because it was a retrospective study of anonymised images.

Written informed consent was obtained from all subjects (patients) in this study. Methodology: retrospective, experimental, performed at one institution.

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Correspondence to S. A. Waugh.

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Waugh, S.A., Purdie, C.A., Jordan, L.B. et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 26, 322–330 (2016). https://doi.org/10.1007/s00330-015-3845-6

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  • DOI: https://doi.org/10.1007/s00330-015-3845-6

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