Advertisement

Quantitative MRI Phenotyping of Breast Cancer across Molecular Classification Subtypes

  • Maryellen L. Giger
  • Hui Li
  • Li Lan
  • Hiroyuki Abe
  • Gillian M. Newstead
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8539)

Abstract

The goal of our study was to investigate the potential usefulness of quantitative MRI analysis (i.e., phenotyping) in characterizing and data mining the molecular subtypes of breast cancer in order to better understand the difference among HER2, ER, and PR expression, triple negative, and other molecular classifications. Analyses were performed on 168 biopsy-proven breast cancer MRI studies acquired between November 2008 and August 2011, on which molecular classification was known. MRI-based phenotyping analysis included: 3D lesion segmentation based on a fuzzy c-means clustering algorithm, computerized feature extraction, leave-one-out linear stepwise feature selection, and discriminant score estimation using Linear Discriminant Analysis (LDA). The classification performance between the molecular subtypes of breast cancer was evaluated using ROC analysis with area under the ROC curve (AUC) as the figure of merit. AUC values obtained for 26 HER2+ vs. 142 HER2-, 118 ER+ vs. 50 ER-, 93 PR+ vs. 75 PR-, 40 Triple Negative (ER-, PR-, and HER2-) vs. 128 all others are 0.65, 0.70, 0.57, and 0.68, respectively for the combined datasets that included images from both 1.5T and 3T scanners. Contributions to the classifiers come from the shape, texture, and kinetics of the lesion, triple negative cases exhibiting increased margin variability, distinct kinetics, and increased surface area. Analyzing the datasets within magnet strength substantially improved performances, e.g., the AUC for triple negative vs. all other cancer subtypes increased from 0.69 (SE=0.05) to 0.88 (SE=0.05). The results from this study indicate that quantitative MRI analysis shows promise as a means for high-throughput image-based phenotyping in the discrimination of breast cancer subtypes.

Keywords

Computer-aided diagnosis Breast MRI image-based phenotype molecular classifications 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer Statistics. CA Cancer J. Clin. 63, 11–30 (2013)CrossRefGoogle Scholar
  2. 2.
    Hylton, N.: MR imaging for assessment of breast cancer response to neoadjuvant chemotherapy. Magn. Reson. Imaging Clin. N. Am. 14, 383–389 (2006)CrossRefGoogle Scholar
  3. 3.
    Kuhl, C.K., Schild, H.H.: Dynamic image interpretation of MRI of the breast. J. Magn. Reson. Imaging 12, 965–974 (2000)CrossRefGoogle Scholar
  4. 4.
    Saslow, D., Boetes, C., Burke, W., Harms, S., Leach, M.O., Lehman, C.D., Morris, E., Pisano, E., Schnall, M., Sener, S., Smith, R.A., Warner, E., Yaffe, M., Andrews, K.S., Russell, C.A.: American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J. Clin. 57, 75–89 (2007)CrossRefGoogle Scholar
  5. 5.
    Schnitt, S.J.: Classification and prognosis of invasive breast cancer: from morphology to molecular taxonomy. Mod. Pathol. 64, S60–S64 (2010)CrossRefGoogle Scholar
  6. 6.
    Chen, W., Giger, M.L., Bick, U.: A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad. Radiol. 13, 63–72 (2006)CrossRefGoogle Scholar
  7. 7.
    Chen, W., Giger, M.L., Lan, L., Bick, U.: Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med. Phys. 31, 1076–1082 (2004)CrossRefGoogle Scholar
  8. 8.
    Chen, W., Giger, M.L., Bick, U., Newstead, G.M.: Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med. Phys. 33, 2878–2887 (2006)CrossRefGoogle Scholar
  9. 9.
    Chen, W., Giger, M.L., Li, H., Bick, U., Newstead, G.M.: Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn. Reson. Med. 58, 562–571 (2007)CrossRefGoogle Scholar
  10. 10.
    Metz, C.E.: ROC methodology in radiographic imaging. Invest. Radiol. 21, 720–733 (1986)CrossRefGoogle Scholar
  11. 11.
    Metz, C.E.: Some practical issues of experimental design and data analysis in radiological ROC studies. Invest. Radiol. 24, 234–245 (1989)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Giger, M.L., Li, H., Lan, L.: Visualization of image-based breast cancer tumor signatures. RSNA 2012, 243 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maryellen L. Giger
    • 1
  • Hui Li
    • 1
  • Li Lan
    • 1
  • Hiroyuki Abe
    • 1
  • Gillian M. Newstead
    • 1
  1. 1.Department of RadiologyThe University of ChicagoChicagoUSA

Personalised recommendations