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Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study

Author

Listed:
  • Jeff Wang
  • Fumi Kato
  • Noriko Oyama-Manabe
  • Ruijiang Li
  • Yi Cui
  • Khin Khin Tha
  • Hiroko Yamashita
  • Kohsuke Kudo
  • Hiroki Shirato

Abstract

Objectives: To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying “triple-negative" breast cancers. Materials and Methods: In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation. Results: Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p

Suggested Citation

  • Jeff Wang & Fumi Kato & Noriko Oyama-Manabe & Ruijiang Li & Yi Cui & Khin Khin Tha & Hiroko Yamashita & Kohsuke Kudo & Hiroki Shirato, 2015. "Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:plo:pone00:0143308
    DOI: 10.1371/journal.pone.0143308
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    References listed on IDEAS

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    2. Ella F Jones & Sumedha P Sinha & David C Newitt & Catherine Klifa & John Kornak & Catherine C Park & Nola M Hylton, 2013. "MRI Enhancement in Stromal Tissue Surrounding Breast Tumors: Association with Recurrence Free Survival following Neoadjuvant Chemotherapy," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    3. Hugo J.W.L. Aerts & Emmanuel Rios Velazquez & Ralph T.H. Leijenaar & Chintan Parmar & Patrick Grossmann & Sara Carvalho & Johan Bussink & René Monshouwer & Benjamin Haibe-Kains & Derek Rietveld & Fran, 2014. "Correction: Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach," Nature Communications, Nature, vol. 5(1), pages 1-1, December.
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