Skip to main content
SpringerLink
Log in

Multiparametric Imaging: Cutting-Edge Sequences and Techniques Including Diffusion-Weighted Imaging, Magnetic Resonance Spectroscopy, and PET/CT or PET/MRI

  • Chapter
  • First Online:
Breast Oncology: Techniques, Indications, and Interpretation

  • 1178 Accesses

Abstract

Magnetic resonance imaging (MRI) of the breast is an indispensible tool in breast imaging, with several indications. Dynamic contrast-enhanced MRI (DCE-MRI) provides mainly morphological, and, to some extent, functional information about perfusion and vascularity, resulting in excellent sensitivity and good specificity for breast cancer diagnosis. Multiparametric imaging of the breast aims to quantify and visualize biological, physiological, and pathological processes at the cellular and molecular levels. Multiparametric imaging of the breast can be performed at different field-strengths (1.5–7 T) and comprises established and emerging MRI parameters, such as diffusion-weighted imaging (DWI), MR spectroscopy (MRS), sodium imaging (23Na MRI), chemical exchange saturation transfer (CEST) imaging, blood oxygen level–dependent (BOLD) MRI, nuclear imaging, such as positron emission tomography (PET) with different radiotracers, and combinations of techniques (e.g., PET/CT and PET/MRI).

Several functional parameters with MRI and PET have also been assessed for imaging of breast tumors, and their combined application is defined as multiparametric imaging. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the hallmarks of cancer and may provide additional specificity.

This chapter aims to provide a comprehensive overview of the current possibilities and emerging techniques in multiparametric imaging of the breast with cutting-edge sequences and techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. DeSantis CE, Bray F, Ferlay J, et al. International variation in female breast cancer incidence and mortality rates. Cancer Epidemiol Biomarkers Prev. 2015. doi:10.1158/1055-9965.EPI-15-0535.

    PubMed  Google Scholar 

  2. Spick C, Szolar DHM, Preidler KW, et al. Breast MRI used as a problem-solving tool reliably excludes malignancy. Eur J Radiol. 2015;84:61–4. doi:10.1016/j.ejrad.2014.10.005.

    Article  PubMed  Google Scholar 

  3. Sardanelli F, Boetes C, Borisch B, et al. Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer. 2010;46:1296–316. doi:10.1016/j.ejca.2010.02.015.

    Article  PubMed  Google Scholar 

  4. for the European Society of Breast Imaging (EUSOBI), Sardanelli F, Helbich TH. Mammography: EUSOBI recommendations for women’s information. Insights Imaging. 2012;3:7–10. doi:10.1007/s13244-011-0127-y.

    Article  Google Scholar 

  5. Mann RM, Balleyguier C, Baltzer PA, et al. Breast MRI: EUSOBI recommendations for women’s information. Eur Radiol. 2015. doi:10.1007/s00330-015-3807-z.

    PubMed Central  Google Scholar 

  6. Lin D, Moy L, Axelrod D, Smith J. Utilization of magnetic resonance imaging in breast cancer screening. Curr Oncol. 2015;22:e332–5. doi:10.3747/co.22.2882.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pinker K, Bogner W, Baltzer P, et al. Clinical application of bilateral high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging of the breast at 7 T. Eur Radiol. 2014;24:913–20. doi:10.1007/s00330-013-3075-8.

    Article  CAS  PubMed  Google Scholar 

  8. Kuhl CK, Schild HH, Morakkabati N. Dynamic bilateral contrast-enhanced MR imaging of the breast: trade-off between spatial and temporal resolution. Radiology. 2005;236:789–800. doi:10.1148/radiol.2363040811.

    Article  PubMed  Google Scholar 

  9. Gruber S, Pinker K, Zaric O, et al. Dynamic contrast-enhanced magnetic resonance imaging of breast tumors at 3 and 7 T: a comparison. Invest Radiol. 2014;49:354–62. doi:10.1097/RLI.0000000000000034.

    Article  PubMed  Google Scholar 

  10. Rahbar H, Partridge SC. Multiparametric MR imaging of breast cancer. Magn Reson Imaging Clin N Am. 2016;24:223–38. doi:10.1016/j.mric.2015.08.012.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Pinker K, Bogner W, Baltzer P, et al. Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging. Invest Radiol. 2014;49:421–30. doi:10.1097/RLI.0000000000000029.

    Article  CAS  PubMed  Google Scholar 

  12. Pinker K, Baltzer P, Bogner W, et al. Multiparametric MR imaging with high-resolution dynamic contrast-enhanced and diffusion-weighted imaging at 7 T improves the assessment of breast tumors: a feasibility study. Radiology. 2015;276:360–70. doi:10.1148/radiol.15141905.

    Article  PubMed  Google Scholar 

  13. Pinker K, Bickel H, Helbich TH, et al. Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the “Breast Imaging Reporting and Data System” for multiparametric 3-T imaging of breast lesions. Eur Radiol. 2013;23:1791–802. doi:10.1007/s00330-013-2771-8.

    Article  CAS  PubMed  Google Scholar 

  14. Boné B, Szabó BK, Perbeck LG, et al. Can contrast-enhanced MR imaging predict survival in breast cancer? Acta Radiol. 2003;44:373–8.

    Article  PubMed  Google Scholar 

  15. Buckley DL, Drew PJ, Mussurakis S, et al. Microvessel density of invasive breast cancer assessed by dynamic Gd-DTPA enhanced MRI. J Magn Reson Imaging. 1997;7:461–4.

    Article  CAS  PubMed  Google Scholar 

  16. Helbich TH. Contrast-enhanced magnetic resonance imaging of the breast. Eur J Radiol. 2000;34:208–19.

    Article  CAS  PubMed  Google Scholar 

  17. Wilmes LJ, Pallavicini MG, Fleming LM, et al. AG-013736, a novel inhibitor of VEGF receptor tyrosine kinases, inhibits breast cancer growth and decreases vascular permeability as detected by dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Imaging. 2007;25:319–27. doi:10.1016/j.mri.2006.09.041.

    Article  CAS  PubMed  Google Scholar 

  18. Kaiser CG, Reich C, Dietzel M, et al. DCE-MRI of the breast in a stand-alone setting outside a complementary strategy – results of the TK-study. Eur Radiol. 2015;25:1793–800. doi:10.1007/s00330-014-3580-4.

    Article  PubMed  Google Scholar 

  19. Kuhl C. The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology. 2007;244:356–78. doi:10.1148/radiol.2442051620.

    Article  PubMed  Google Scholar 

  20. Kuhl CK. Current status of breast MR imaging. Part 2. Clinical applications. Radiology. 2007;244:672–91. doi:10.1148/radiol.2443051661.

    Article  PubMed  Google Scholar 

  21. Pinker K, Grabner G, Bogner W, et al. A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol. 2009;44:553–8. doi:10.1097/RLI.0b013e3181b4c127.

    Article  PubMed  Google Scholar 

  22. Kuhl CK. Breast MR imaging at 3 T. Magn Reson Imaging Clin N Am. 2007;15:315–20, vi. doi:10.1016/j.mric.2007.08.003.

    Article  PubMed  Google Scholar 

  23. Baltzer PAT, Dietzel M. Breast lesions: diagnosis by using proton MR spectroscopy at 1.5 and 3.0 T – systematic review and meta-analysis. Radiology. 2013;267:735–46. doi:10.1148/radiol.13121856.

    Article  PubMed  Google Scholar 

  24. Chen X, Li W, Zhang Y, et al. Meta-analysis of quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesions. BMC Cancer. 2010;10:693. doi:10.1186/1471-2407-10-693.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Folkman J. Seminars in medicine of the Beth Israel Hospital, Boston. Clinical applications of research on angiogenesis. N Engl J Med. 1995;333:1757–63. doi:10.1056/NEJM199512283332608.

    Article  CAS  PubMed  Google Scholar 

  26. Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med. 1971;285:1182–6. doi:10.1056/NEJM197111182852108.

    Article  CAS  PubMed  Google Scholar 

  27. Kaiser WA, Zeitler E. MR imaging of the breast: fast imaging sequences with and without Gd-DTPA. Preliminary Observations Radiol. 1989;170:681–6. doi:10.1148/radiology.170.3.2916021.

    CAS  Google Scholar 

  28. Helbich TH, Becherer A, Trattnig S, et al. Differentiation of benign and malignant breast lesions: MR imaging versus Tc-99 m sestamibi scintimammography. Radiology. 1997;202:421–9. doi:10.1148/radiology.202.2.9015068.

    Article  CAS  PubMed  Google Scholar 

  29. Heywang-Köbrunner SH, Viehweg P, Heinig A, Küchler C. Contrast-enhanced MRI of the breast: accuracy, value, controversies, solutions. Eur J Radiol. 1997;24:94–108.

    Article  PubMed  Google Scholar 

  30. Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211:101–10. doi:10.1148/radiology.211.1.r99ap38101.

    Article  CAS  PubMed  Google Scholar 

  31. Malich A, Fischer DR, Wurdinger S, et al. Potential MRI interpretation model: differentiation of benign from malignant breast masses. AJR Am J Roentgenol. 2005;185:964–70. doi:10.2214/AJR.04.1073.

    Article  PubMed  Google Scholar 

  32. Kaiser WA. Breast magnetic resonance imaging: principles and techniques. Semin Roentgenol. 2007;42:228–35. doi:10.1053/j.ro.2007.07.003.

    Article  PubMed  Google Scholar 

  33. Fischer DR, Wurdinger S, Boettcher J, et al. Further signs in the evaluation of magnetic resonance mammography: a retrospective study. Invest Radiol. 2005;40:430–5.

    Article  PubMed  Google Scholar 

  34. Berg WA, Gutierrez L, NessAiver MS, et al. Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology. 2004;233:830–49. doi:10.1148/radiol.2333031484.

    Article  PubMed  Google Scholar 

  35. Tofts PS, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med. 1991;17:357–67.

    Article  CAS  PubMed  Google Scholar 

  36. Tofts PS, Berkowitz B, Schnall MD. Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn Reson Med. 1995;33:564–8.

    Article  CAS  PubMed  Google Scholar 

  37. Huang W, Tudorica LA, Li X, et al. Discrimination of benign and malignant breast lesions by using shutter-speed dynamic contrast-enhanced MR imaging. Radiology. 2011;261:394–403. doi:10.1148/radiol.11102413.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Li L, Wang K, Sun X, et al. Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer. Med Sci Monit. 2015;21:376–82. doi:10.12659/MSM.892534.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hylton NM, Blume JD, Bernreuter WK, et al. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy – results from ACRIN 6657/I-SPY TRIAL. Radiology. 2012;263:663–72. doi:10.1148/radiol.12110748.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Marinovich ML, Macaskill P, Irwig L, et al. Meta-analysis of agreement between MRI and pathologic breast tumour size after neoadjuvant chemotherapy. Br J Cancer. 2013;109:1528–36. doi:10.1038/bjc.2013.473.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kuhl CK, Kooijman H, Gieseke J, Schild HH. Effect of B1 inhomogeneity on breast MR imaging at 3.0 T. Radiology. 2007;244:929–30. doi:10.1148/radiol.2443070266.

    Article  PubMed  Google Scholar 

  42. Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10:223–32.

    Article  CAS  PubMed  Google Scholar 

  43. Yankeelov TE, Luci JJ, Lepage M, et al. Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magn Reson Imaging. 2005;23:519–29. doi:10.1016/j.mri.2005.02.013.

    Article  CAS  PubMed  Google Scholar 

  44. Mann RM, Mus RD, van Zelst J, et al. A novel approach to contrast-enhanced breast magnetic resonance imaging for screening: high-resolution ultrafast dynamic imaging. Invest Radiol. 2014;49:579–85. doi:10.1097/RLI.0000000000000057.

    Article  PubMed  Google Scholar 

  45. Tudorica LA, Oh KY, Roy N, et al. A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings. Magn Reson Imaging. 2012;30:1257–67. doi:10.1016/j.mri.2012.04.009.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Huang W, Li X, Chen Y, et al. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Transl Oncol. 2014;7:153–66.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kinkel K, Helbich TH, Esserman LJ, et al. Dynamic high-spatial-resolution MR imaging of suspicious breast lesions: diagnostic criteria and interobserver variability. AJR Am J Roentgenol. 2000;175:35–43. doi:10.2214/ajr.175.1.1750035.

    Article  CAS  PubMed  Google Scholar 

  48. Turnbull LW. Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR Biomed. 2009;22:28–39. doi:10.1002/nbm.1273.

    Article  PubMed  Google Scholar 

  49. Goto M, Ito H, Akazawa K, et al. Diagnosis of breast tumors by contrast-enhanced MR imaging: comparison between the diagnostic performance of dynamic enhancement patterns and morphologic features. J Magn Reson Imaging. 2007;25:104–12. doi:10.1002/jmri.20812.

    Article  PubMed  Google Scholar 

  50. Veltman J, Stoutjesdijk M, Mann R, et al. Contrast-enhanced magnetic resonance imaging of the breast: the value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in classifying lesions. Eur Radiol. 2008;18:1123–33. doi:10.1007/s00330-008-0870-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kuhl CK, Jost P, Morakkabati N, et al. Contrast-enhanced MR imaging of the breast at 3.0 and 1.5 T in the same patients: initial experience. Radiology. 2006;239:666–76. doi:10.1148/radiol.2392050509.

    Article  PubMed  Google Scholar 

  52. Lourenco AP, Donegan L, Khalil H, Mainiero MB. Improving outcomes of screening breast MRI with practice evolution: initial clinical experience with 3 T compared to 1.5 T. J Magn Reson Imaging. 2014;39:535–9. doi:10.1002/jmri.24198.

    Article  PubMed  Google Scholar 

  53. Pinker-Domenig K, Bogner W, Gruber S, et al. High resolution MRI of the breast at 3 T: which BI-RADS® descriptors are most strongly associated with the diagnosis of breast cancer? Eur Radiol. 2012;22:322–30. doi:10.1007/s00330-011-2256-6.

    Article  CAS  PubMed  Google Scholar 

  54. van de Bank BL, Voogt IJ, Italiaander M, et al. Ultra high spatial and temporal resolution breast imaging at 7 T. NMR Biomed. 2013;26:367–75. doi:10.1002/nbm.2868.

    Article  PubMed  Google Scholar 

  55. Korteweg MA, Veldhuis WB, Visser F, et al. Feasibility of 7 Tesla breast magnetic resonance imaging determination of intrinsic sensitivity and high-resolution magnetic resonance imaging, diffusion-weighted imaging, and (1)H-magnetic resonance spectroscopy of breast cancer patients receiving neoadjuvant therapy. Invest Radiol. 2011;46:370–6. doi:10.1097/RLI.0b013e31820df706.

    Article  CAS  PubMed  Google Scholar 

  56. Klomp DWJ, van de Bank BL, Raaijmakers A, et al. 31P MRSI and 1H MRS at 7 T: initial results in human breast cancer. NMR Biomed. 2011;24:1337–42. doi:10.1002/nbm.1696.

    Article  PubMed  Google Scholar 

  57. Stehouwer BL, Klomp DWJ, Korteweg MA, et al. 7 T versus 3 T contrast-enhanced breast magnetic resonance imaging of invasive ductulolobular carcinoma: first clinical experience. Magn Reson Imaging. 2013;31:613–7. doi:10.1016/j.mri.2012.09.005.

    Article  PubMed  Google Scholar 

  58. Brown R, Storey P, Geppert C, et al. Breast MRI at 7 Tesla with a bilateral coil and T1-weighted acquisition with robust fat suppression: image evaluation and comparison with 3 Tesla. Eur Radiol. 2013;23:2969–78. doi:10.1007/s00330-013-2972-1.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Baltzer PA, Renz DM, Herrmann K-H, et al. (2009) Diffusion-weighted imaging (DWI) in MR mammography (MRM): clinical comparison of echo planar imaging (EPI) and half-Fourier single-shot turbo spin echo (HASTE) diffusion techniques. Eur Radiol 19:1612–1620. doi: 10.1007/s00330-009-1326-5

  60. Baltzer PAT, Benndorf M, Dietzel M, et al. Sensitivity and specificity of unenhanced MR mammography (DWI combined with T2-weighted TSE imaging, ueMRM) for the differentiation of mass lesions. Eur Radiol. 2010;20:1101–10. doi:10.1007/s00330-009-1654-5.

    Article  PubMed  Google Scholar 

  61. Woodhams R, Kakita S, Hata H, et al. Identification of residual breast carcinoma following neoadjuvant chemotherapy: diffusion-weighted imaging – comparison with contrast-enhanced MR imaging and pathologic findings. Radiology. 2010;254:357–66. doi:10.1148/radiol.2542090405.

    Article  PubMed  Google Scholar 

  62. Marini C, Iacconi C, Giannelli M, et al. Quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesion. Eur Radiol. 2007;17:2646–55. doi:10.1007/s00330-007-0621-2.

    Article  CAS  PubMed  Google Scholar 

  63. Thomassin-Naggara I, De Bazelaire C, Chopier J, et al. Diffusion-weighted MR imaging of the breast: advantages and pitfalls. Eur J Radiol. 2013;82:435–43. doi:10.1016/j.ejrad.2012.03.002.

    Article  CAS  PubMed  Google Scholar 

  64. Cho GY, Moy L, Kim SG, et al. Comparison of contrast enhancement and diffusion-weighted magnetic resonance imaging in healthy and cancerous breast tissue. Eur J Radiol. 2015;84:1888–93. doi:10.1016/j.ejrad.2015.06.023.

    Article  PubMed  Google Scholar 

  65. Bogner W, Gruber S, Pinker K, et al. Diffusion-weighted MR for differentiation of breast lesions at 3.0 T: how does selection of diffusion protocols affect diagnosis? Radiology. 2009;253:341–51. doi:10.1148/radiol.2532081718.

    Article  PubMed  Google Scholar 

  66. Dorrius MD, Dijkstra H, Oudkerk M, Sijens PE. Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5-T breast DWI: a systematic review and meta-analysis. Eur Radiol. 2014;24:2835–47. doi:10.1007/s00330-014-3338-z.

    Article  PubMed  Google Scholar 

  67. Guo Y, Cai Y-Q, Cai Z-L, et al. Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging. 2002;16:172–8. doi:10.1002/jmri.10140.

    Article  PubMed  Google Scholar 

  68. Woodhams R, Ramadan S, Stanwell P, et al. Diffusion-weighted imaging of the breast: principles and clinical applications. Radiographics. 2011;31:1059–84. doi:10.1148/rg.314105160.

    Article  PubMed  Google Scholar 

  69. Bogner W, Pinker-Domenig K, Bickel H, et al. Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T. Radiology. 2012;263:64–76. doi:10.1148/radiol.12111494.

    Article  PubMed  Google Scholar 

  70. Tsushima Y, Takahashi-Taketomi A, Endo K. Magnetic resonance (MR) differential diagnosis of breast tumors using apparent diffusion coefficient (ADC) on 1.5-T. J Magn Reson Imaging. 2009;30:249–55. doi:10.1002/jmri.21854.

    Article  PubMed  Google Scholar 

  71. Costantini M, Belli P, Rinaldi P, et al. Diffusion-weighted imaging in breast cancer: relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol. 2010;65:1005–12. doi:10.1016/j.crad.2010.07.008.

    Article  CAS  PubMed  Google Scholar 

  72. Hatakenaka M, Soeda H, Yabuuchi H, et al. Apparent diffusion coefficients of breast tumors: clinical application. Magn Reson Med Sci. 2008;7:23–9.

    Article  PubMed  Google Scholar 

  73. Woodhams R, Matsunaga K, Iwabuchi K, et al. Diffusion-weighted imaging of malignant breast tumors: the usefulness of apparent diffusion coefficient (ADC) value and ADC map for the detection of malignant breast tumors and evaluation of cancer extension. J Comput Assist Tomogr. 2005;29:644–9.

    Article  PubMed  Google Scholar 

  74. Spick C, Pinker-Domenig K, Rudas M, et al. MRI-only lesions: application of diffusion-weighted imaging obviates unnecessary MR-guided breast biopsies. Eur Radiol. 2014;24:1204–10. doi:10.1007/s00330-014-3153-6.

    Article  PubMed  Google Scholar 

  75. Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol. 2012;22:1519–28. doi:10.1007/s00330-012-2403-8.

    Article  PubMed  Google Scholar 

  76. Uematsu T, Kasami M, Yuen S. Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology. 2009;250:638–47. doi:10.1148/radiol.2503081054.

    Article  PubMed  Google Scholar 

  77. Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med. 2010;363:1938–48. doi:10.1056/NEJMra1001389.

    Article  CAS  PubMed  Google Scholar 

  78. Dogan BE, Gonzalez-Angulo AM, Gilcrease M, et al. Multimodality imaging of triple receptor-negative tumors with mammography, ultrasound, and MRI. AJR Am J Roentgenol. 2010;194:1160–6. doi:10.2214/AJR.09.2355.

    Article  PubMed  Google Scholar 

  79. Kawashima H. Imaging findings of triple-negative breast cancer. Breast Cancer. 2011;18:145. doi:10.1007/s12282-010-0247-0.

    Article  PubMed  Google Scholar 

  80. Bickel H, Pinker-Domenig K, Bogner W, et al. Quantitative apparent diffusion coefficient as a noninvasive imaging biomarker for the differentiation of invasive breast cancer and ductal carcinoma in situ. Invest Radiol. 2015;50:95–100. doi:10.1097/RLI.0000000000000104.

    Article  CAS  PubMed  Google Scholar 

  81. Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11:102–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Pickles MD, Gibbs P, Lowry M, Turnbull LW. Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging. 2006;24:843–7. doi:10.1016/j.mri.2005.11.005.

    Article  PubMed  Google Scholar 

  83. Park SH, Moon WK, Cho N, et al. Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology. 2010;257:56–63. doi:10.1148/radiol.10092021.

    Article  PubMed  Google Scholar 

  84. Richard R, Thomassin I, Chapellier M, et al. Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Eur Radiol. 2013;23:2420–31. doi:10.1007/s00330-013-2850-x.

    Article  PubMed  Google Scholar 

  85. Sharma U, Danishad KKA, Seenu V, Jagannathan NR. Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed. 2009;22:104–13. doi:10.1002/nbm.1245.

    Article  PubMed  Google Scholar 

  86. Iacconi C, Giannelli M, Marini C, et al. The role of mean diffusivity (MD) as a predictive index of the response to chemotherapy in locally advanced breast cancer: a preliminary study. Eur Radiol. 2010;20:303–8. doi:10.1007/s00330-009-1550-z.

    Article  PubMed  Google Scholar 

  87. Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168:497–505. doi:10.1148/radiology.168.2.3393671.

    Article  CAS  PubMed  Google Scholar 

  88. Cho GY, Moy L, Kim SG, et al. Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol. 2015. doi:10.1007/s00330-015-4087-3.

    PubMed Central  Google Scholar 

  89. Bokacheva L, Kaplan JB, Giri DD, et al. Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma. J Magn Reson Imaging. 2014;40:813–23. doi:10.1002/jmri.24462.

    Article  PubMed  Google Scholar 

  90. Iima M, Yano K, Kataoka M, et al. Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol. 2015;50:205–11. doi:10.1097/RLI.0000000000000094.

    Article  PubMed  Google Scholar 

  91. Liu C, Liang C, Liu Z, et al. Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol. 2013;82:e782–9. doi:10.1016/j.ejrad.2013.08.006.

    Article  PubMed  Google Scholar 

  92. Cho GY, Moy L, Zhang JL, et al. Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer. Magn Reson Med. 2015;74:1077–85. doi:10.1002/mrm.25484.

    Article  PubMed  Google Scholar 

  93. Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432–40. doi:10.1002/mrm.20508.

    Article  PubMed  Google Scholar 

  94. Nogueira L, Brandão S, Matos E, et al. Application of the diffusion kurtosis model for the study of breast lesions. Eur Radiol. 2014;24:1197–203. doi:10.1007/s00330-014-3146-5.

    Article  PubMed  Google Scholar 

  95. Sun K, Chen X, Chai W, et al. Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors. Radiology. 2015;277:46–55. doi:10.1148/radiol.15141625.

    Article  PubMed  Google Scholar 

  96. Baltzer PAT, Schäfer A, Dietzel M, et al. Diffusion tensor magnetic resonance imaging of the breast: a pilot study. Eur Radiol. 2011;21:1–10. doi:10.1007/s00330-010-1901-9.

    Article  PubMed  Google Scholar 

  97. Cakir O, Arslan A, Inan N, et al. Comparison of the diagnostic performances of diffusion parameters in diffusion weighted imaging and diffusion tensor imaging of breast lesions. Eur J Radiol. 2013;82:e801–6. doi:10.1016/j.ejrad.2013.09.001.

    Article  PubMed  Google Scholar 

  98. Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001;13:534–46.

    Article  CAS  PubMed  Google Scholar 

  99. Eyal E, Shapiro-Feinberg M, Furman-Haran E, et al. Parametric diffusion tensor imaging of the breast. Invest Radiol. 2012;47:284–91. doi:10.1097/RLI.0b013e3182438e5d.

    Article  PubMed  Google Scholar 

  100. Partridge SC, Ziadloo A, Murthy R, et al. Diffusion tensor MRI: preliminary anisotropy measures and mapping of breast tumors. J Magn Reson Imaging. 2010;31:339–47. doi:10.1002/jmri.22045.

    Article  PubMed  Google Scholar 

  101. Partridge SC, DeMartini WB, Kurland BF, et al. Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. AJR Am J Roentgenol. 2009;193:1716–22. doi:10.2214/AJR.08.2139.

    Article  PubMed  Google Scholar 

  102. Jagannathan NR, Kumar M, Seenu V, et al. Evaluation of total choline from in-vivo volume localized proton MR spectroscopy and its response to neoadjuvant chemotherapy in locally advanced breast cancer. Br J Cancer. 2001;84:1016–22. doi:10.1054/bjoc.2000.1711.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Baik H-M, Su M-Y, Yu H, et al. Quantification of choline-containing compounds in malignant breast tumors by 1H MR spectroscopy using water as an internal reference at 1.5 T. MAGMA. 2006;19:96–104. doi:10.1007/s10334-006-0032-4.

    Article  CAS  PubMed  Google Scholar 

  104. Meisamy S, Bolan PJ, Baker EH, et al. Adding in vivo quantitative 1H MR spectroscopy to improve diagnostic accuracy of breast MR imaging: preliminary results of observer performance study at 4.0 T. Radiology. 2005;236:465–75. doi:10.1148/radiol.2362040836.

    Article  PubMed  Google Scholar 

  105. Bolan PJ. Magnetic resonance spectroscopy of the breast: current status. Magn Reson Imaging Clin N Am. 2013;21:625–39. doi:10.1016/j.mric.2013.04.008.

    Article  PubMed  Google Scholar 

  106. Shin HJ, Baek H-M, Cha JH, Kim HH. Evaluation of breast cancer using proton MR spectroscopy: total choline peak integral and signal-to-noise ratio as prognostic indicators. AJR Am J Roentgenol. 2012;198:W488–97. doi:10.2214/AJR.11.7292.

    Article  PubMed  Google Scholar 

  107. Roebuck JR, Cecil KM, Schnall MD, Lenkinski RE. Human breast lesions: characterization with proton MR spectroscopy. Radiology. 1998;209:269–75. doi:10.1148/radiology.209.1.9769842.

    Article  CAS  PubMed  Google Scholar 

  108. Lenkinski RE, Wang X, Elian M, Goldberg SN. Interaction of gadolinium-based MR contrast agents with choline: implications for MR spectroscopy (MRS) of the breast. Magn Reson Med. 2009;61:1286–92. doi:10.1002/mrm.21937.

    Article  CAS  PubMed  Google Scholar 

  109. Baltzer PAT, Dietzel M, Kaiser WA. MR-spectroscopy at 1.5 tesla and 3 tesla. Useful? A systematic review and meta-analysis. Eur J Radiol. 2012;81 Suppl 1:S6–9. doi:10.1016/S0720-048X(12)70003-7.

    Article  PubMed  Google Scholar 

  110. Gruber S, Debski B-K, Pinker K, et al. Three-dimensional proton MR spectroscopic imaging at 3 T for the differentiation of benign and malignant breast lesions. Radiology. 2011;261:752–61. doi:10.1148/radiol.11102096.

    Article  PubMed  Google Scholar 

  111. Danishad KKA, Sharma U, Sah RG, et al. Assessment of therapeutic response of locally advanced breast cancer (LABC) patients undergoing neoadjuvant chemotherapy (NACT) monitored using sequential magnetic resonance spectroscopic imaging (MRSI). NMR Biomed. 2010;23:233–41. doi:10.1002/nbm.1436.

    CAS  PubMed  Google Scholar 

  112. Sharma U, Baek HM, Su MY, Jagannathan NR. In vivo 1H MRS in the assessment of the therapeutic response of breast cancer patients. NMR Biomed. 2011;24:700–11. doi:10.1002/nbm.1654.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Glunde K, Bhujwalla ZM, Ronen SM. Choline metabolism in malignant transformation. Nat Rev Cancer. 2011;11:835–48. doi:10.1038/nrc3162.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Aboagye EO, Bhujwalla ZM. Malignant transformation alters membrane choline phospholipid metabolism of human mammary epithelial cells. Cancer Res. 1999;59:80–4.

    CAS  PubMed  Google Scholar 

  115. Ramadan S, Arm J, Silcock J, et al. Lipid and metabolite deregulation in the breast tissue of women carrying BRCA1 and BRCA2 genetic mutations. Radiology. 2015;275:675–82. doi:10.1148/radiol.15140967.

    Article  PubMed  Google Scholar 

  116. Kul S, Cansu A, Alhan E, et al. Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors. AJR Am J Roentgenol. 2011;196:210–7. doi:10.2214/AJR.10.4258.

    Article  PubMed  Google Scholar 

  117. Ei Khouli RH, Jacobs MA, Mezban SD, et al. Diffusion-weighted imaging improves the diagnostic accuracy of conventional 3.0-T breast MR imaging. Radiology. 2010;256:64–73. doi:10.1148/radiol.10091367.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Baltzer A, Dietzel M, Kaiser CG, Baltzer PA. Combined reading of contrast enhanced and diffusion weighted magnetic resonance imaging by using a simple sum score. Eur Radiol. 2015. doi:10.1007/s00330-015-3886-x.

    PubMed Central  Google Scholar 

  119. Schmitz AMT, Veldhuis WB, Menke-Pluijmers MBE, et al. Multiparametric MRI with dynamic contrast enhancement, diffusion-weighted imaging, and 31-phosphorus spectroscopy at 7 T for characterization of breast cancer. Invest Radiol. 2015;50:766–71. doi:10.1097/RLI.0000000000000183.

    Article  CAS  PubMed  Google Scholar 

  120. Madelin G, Regatte RR. Biomedical applications of sodium MRI in vivo. J Magn Reson Imaging. 2013;38:511–29. doi:10.1002/jmri.24168.

    Article  PubMed  PubMed Central  Google Scholar 

  121. Ouwerkerk R. Sodium MRI. Methods Mol Biol. 2011;711:175–201. doi:10.1007/978-1-61737-992-5_8.

    Article  CAS  PubMed  Google Scholar 

  122. Ouwerkerk R, Jacobs MA, Macura KJ, et al. Elevated tissue sodium concentration in malignant breast lesions detected with non-invasive 23Na MRI. Breast Cancer Res Treat. 2007;106:151–60. doi:10.1007/s10549-006-9485-4.

    Article  CAS  PubMed  Google Scholar 

  123. Zaric O, Pinker K, Zbyn S, Strasser S, Robinson S, Minarikova L, Gruber S, Farr A, Singer C, TH H, Trattnig S, Bogner W. Quantitative sodium MR imaging at 7 tesla – initial results and comparison with diffusion-weighted imaging in patients with breast tumors. Radiology. 2016;280(1):39–48.

    Article  PubMed  Google Scholar 

  124. Ackerstaff E, Glunde K, Bhujwalla ZM. Choline phospholipid metabolism: a target in cancer cells? J Cell Biochem. 2003;90:525–33. doi:10.1002/jcb.10659.

    Article  CAS  PubMed  Google Scholar 

  125. Arias-Mendoza F, Payne GS, Zakian KL, et al. In vivo 31P MR spectral patterns and reproducibility in cancer patients studied in a multi-institutional trial. NMR Biomed. 2006;19:504–12. doi:10.1002/nbm.1057.

    Article  CAS  PubMed  Google Scholar 

  126. Barzilai A, Horowitz A, Geier A, Degani H. Phosphate metabolites and steroid hormone receptors of benign and malignant breast tumors. A Nuclear Magnetic Resonance study. Cancer. 1991;67:2919–25.

    Article  CAS  PubMed  Google Scholar 

  127. Wijnen JP, van der Kemp WJM, Luttje MP, et al. Quantitative 31P magnetic resonance spectroscopy of the human breast at 7 T. Magn Reson Med. 2012;68:339–48. doi:10.1002/mrm.23249.

    Article  PubMed  Google Scholar 

  128. Ward KM, Aletras AH, Balaban RS. A new class of contrast agents for MRI based on proton chemical exchange dependent saturation transfer (CEST). J Magn Reson. 2000;143:79–87. doi:10.1006/jmre.1999.1956.

    Article  CAS  PubMed  Google Scholar 

  129. Schmitt B, Trattnig S, Schlemmer H-P. CEST-imaging: a new contrast in MR-mammography by means of chemical exchange saturation transfer. Eur J Radiol. 2012;81 Suppl 1:S144–6. doi:10.1016/S0720-048X(12)70060-8.

    Article  PubMed  Google Scholar 

  130. Klomp DWJ, Dula AN, Arlinghaus LR, et al. Amide proton transfer imaging of the human breast at 7 T: development and reproducibility. NMR Biomed. 2013;26:1271–7. doi:10.1002/nbm.2947.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Rivlin M, Horev J, Tsarfaty I, Navon G. Molecular imaging of tumors and metastases using chemical exchange saturation transfer (CEST) MRI. Sci Rep. 2013;3:3045. doi:10.1038/srep03045.

    Article  PubMed  Google Scholar 

  132. Desmond KL, Moosvi F, Stanisz GJ. Mapping of amide, amine, and aliphatic peaks in the CEST spectra of murine xenografts at 7 T. Magn Reson Med. 2014;71:1841–53. doi:10.1002/mrm.24822.

    Article  PubMed  Google Scholar 

  133. Chan KWY, McMahon MT, Kato Y, et al. Natural D-glucose as a biodegradable MRI contrast agent for detecting cancer. Magn Reson Med. 2012;68:1764–73. doi:10.1002/mrm.24520.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Walker-Samuel S, Ramasawmy R, Torrealdea F, et al. In vivo imaging of glucose uptake and metabolism in tumors. Nat Med. 2013;19:1067–72. doi:10.1038/nm.3252.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Nasrallah FA, Pagès G, Kuchel PW, et al. Imaging brain deoxyglucose uptake and metabolism by glucoCEST MRI. J Cereb Blood Flow Metab. 2013;33:1270–8. doi:10.1038/jcbfm.2013.79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Robinson SP, Howe FA, Rodrigues LM, et al. Magnetic resonance imaging techniques for monitoring changes in tumor oxygenation and blood flow. Semin Radiat Oncol. 1998;8:197–207.

    Article  CAS  PubMed  Google Scholar 

  137. O’Flynn EAM, DeSouza NM. Functional magnetic resonance: biomarkers of response in breast cancer. Breast Cancer Res. 2011;13:204. doi:10.1186/bcr2815.

    Article  PubMed  PubMed Central  Google Scholar 

  138. Rakow-Penner R, Daniel B, Glover GH. Detecting blood oxygen level-dependent (BOLD) contrast in the breast. J Magn Reson Imaging. 2010;32:120–9. doi:10.1002/jmri.22227.

    Article  PubMed  Google Scholar 

  139. Li SP, Taylor NJ, Makris A, et al. Primary human breast adenocarcinoma: imaging and histologic correlates of intrinsic susceptibility-weighted MR imaging before and during chemotherapy. Radiology. 2010;257:643–52. doi:10.1148/radiol.10100421.

    Article  PubMed  Google Scholar 

  140. Jiang L, Weatherall PT, McColl RW, et al. Blood oxygenation level-dependent (BOLD) contrast magnetic resonance imaging (MRI) for prediction of breast cancer chemotherapy response: a pilot study. J Magn Reson Imaging. 2013;37:1083–92. doi:10.1002/jmri.23891.

    Article  PubMed  Google Scholar 

  141. Tatum JL, Kelloff GJ, Gillies RJ, et al. Hypoxia: importance in tumor biology, noninvasive measurement by imaging, and value of its measurement in the management of cancer therapy. Int J Radiat Biol. 2006;82:699–757. doi:10.1080/09553000601002324.

    Article  CAS  PubMed  Google Scholar 

  142. Ardenkjaer-Larsen JH, Fridlund B, Gram A, et al. Increase in signal-to-noise ratio of >10,000 times in liquid-state NMR. Proc Natl Acad Sci U S A. 2003;100:10158–63. doi:10.1073/pnas.1733835100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Brindle KM, Bohndiek SE, Gallagher FA, Kettunen MI. Tumor imaging using hyperpolarized 13C magnetic resonance spectroscopy. Magn Reson Med. 2011;66:505–19. doi:10.1002/mrm.22999.

    Article  PubMed  Google Scholar 

  144. Jóhannesson H, Macholl S, Ardenkjaer-Larsen JH. Dynamic nuclear polarization of [1-13C]pyruvic acid at 4.6 tesla. J Magn Reson. 2009;197:167–75. doi:10.1016/j.jmr.2008.12.016.

    Article  PubMed  CAS  Google Scholar 

  145. Golman K, Ardenkjaer-Larsen JH, Petersson JS, et al. Molecular imaging with endogenous substances. Proc Natl Acad Sci U S A. 2003;100:10435–9. doi:10.1073/pnas.1733836100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Golman K, Zandt RI, Lerche M, et al. Metabolic imaging by hyperpolarized 13C magnetic resonance imaging for in vivo tumor diagnosis. Cancer Res. 2006;66:10855–60. doi:10.1158/0008-5472.CAN-06-2564.

    Article  CAS  PubMed  Google Scholar 

  147. Park I, Larson PEZ, Zierhut ML, et al. Hyperpolarized 13C magnetic resonance metabolic imaging: application to brain tumors. Neuro Oncol. 2010;12:133–44. doi:10.1093/neuonc/nop043.

    Article  PubMed  PubMed Central  Google Scholar 

  148. Chen AP, Albers MJ, Cunningham CH, et al. Hyperpolarized C-13 spectroscopic imaging of the TRAMP mouse at 3 T-initial experience. Magn Reson Med. 2007;58:1099–106. doi:10.1002/mrm.21256.

    Article  CAS  PubMed  Google Scholar 

  149. Kurhanewicz J, Bok R, Nelson SJ, Vigneron DB. Current and potential applications of clinical 13C MR spectroscopy. J Nucl Med. 2008;49:341–4. doi:10.2967/jnumed.107.045112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Albers MJ, Bok R, Chen AP, et al. Hyperpolarized 13C lactate, pyruvate, and alanine: noninvasive biomarkers for prostate cancer detection and grading. Cancer Res. 2008;68:8607–15. doi:10.1158/0008-5472.CAN-08-0749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Zierhut ML, Yen Y-F, Chen AP, et al. Kinetic modeling of hyperpolarized 13C1-pyruvate metabolism in normal rats and TRAMP mice. J Magn Reson. 2010;202:85–92. doi:10.1016/j.jmr.2009.10.003.

    Article  CAS  PubMed  Google Scholar 

  152. Keshari KR, Sai V, Wang ZJ, et al. Hyperpolarized [1-13C]dehydroascorbate MR spectroscopy in a murine model of prostate cancer: comparison with 18F-FDG PET. J Nucl Med. 2013;54:922–8. doi:10.2967/jnumed.112.115402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Asghar Butt S, Søgaard LV, Ardenkjaer-Larsen JH, et al. Monitoring mammary tumor progression and effect of tamoxifen treatment in MMTV-PymT using MRI and magnetic resonance spectroscopy with hyperpolarized [1-(13) C]pyruvate. Magn Reson Med. 2014. doi:10.1002/mrm.25095.

    PubMed  Google Scholar 

  154. Taillefer R. Clinical applications of 99mTc-sestamibi scintimammography. Semin Nucl Med. 2005;35:100–15. doi:10.1053/j.semnuclmed.2004.11.002.

    Article  PubMed  Google Scholar 

  155. Scopinaro F, Schillaci O, Ussof W, et al. A three center study on the diagnostic accuracy of 99mTc-MIBI scintimammography. Anticancer Res. 1997;17:1631–4.

    CAS  PubMed  Google Scholar 

  156. Arslan N, Oztürk E, Ilgan S, et al. 99Tcm-MIBI scintimammography in the evaluation of breast lesions and axillary involvement: a comparison with mammography and histopathological diagnosis. Nucl Med Commun. 1999;20:317–25.

    Article  CAS  PubMed  Google Scholar 

  157. Maffioli L, Agresti R, Chiti A, et al. Prone scintimammography in patients with non-palpable breast lesions. Anticancer Res. 1996;16:1269–73.

    CAS  PubMed  Google Scholar 

  158. Brem RF, Rapelyea JA, Zisman G, et al. Occult breast cancer: scintimammography with high-resolution breast-specific gamma camera in women at high risk for breast cancer. Radiology. 2005;237:274–80. doi:10.1148/radiol.2371040758.

    Article  PubMed  Google Scholar 

  159. Brem RF, Shahan C, Rapleyea JA, et al. Detection of occult foci of breast cancer using breast-specific gamma imaging in women with one mammographic or clinically suspicious breast lesion. Acad Radiol. 2010;17:735–43. doi:10.1016/j.acra.2010.01.017.

    Article  PubMed  Google Scholar 

  160. Coover LR, Caravaglia G, Kuhn P. Scintimammography with dedicated breast camera detects and localizes occult carcinoma. J Nucl Med. 2004;45:553–8.

    PubMed  Google Scholar 

  161. Rhodes DJ, O’Connor MK, Phillips SW, et al. Molecular breast imaging: a new technique using technetium Tc 99 m scintimammography to detect small tumors of the breast. Mayo Clin Proc. 2005;80:24–30. doi:10.1016/S0025-6196(11)62953-4.

    Article  PubMed  Google Scholar 

  162. Brem RF, Floerke AC, Rapelyea JA, et al. Breast-specific gamma imaging as an adjunct imaging modality for the diagnosis of breast cancer. Radiology. 2008;247:651–7. doi:10.1148/radiol.2473061678.

    Article  PubMed  Google Scholar 

  163. Brem RF, Fishman M, Rapelyea JA. Detection of ductal carcinoma in situ with mammography, breast specific gamma imaging, and magnetic resonance imaging: a comparative study. Acad Radiol. 2007;14:945–50. doi:10.1016/j.acra.2007.04.004.

    Article  PubMed  Google Scholar 

  164. Sun Y, Wei W, Yang H-W, Liu J-L. Clinical usefulness of breast-specific gamma imaging as an adjunct modality to mammography for diagnosis of breast cancer: a systemic review and meta-analysis. Eur J Nucl Med Mol Imaging. 2013;40:450–63. doi:10.1007/s00259-012-2279-5.

    Article  PubMed  Google Scholar 

  165. Avril N, Adler LP. F-18 fluorodeoxyglucose-positron emission tomography imaging for primary breast cancer and loco-regional staging. Radiol Clin North Am. 2007;45:645–57, vi. doi:10.1016/j.rcl.2007.05.004.

    Article  PubMed  Google Scholar 

  166. Quon A, Gambhir SS. FDG-PET and beyond: molecular breast cancer imaging. J Clin Oncol. 2005;23:1664–73. doi:10.1200/JCO.2005.11.024.

    Article  CAS  PubMed  Google Scholar 

  167. Avril N, Rosé CA, Schelling M, et al. Breast imaging with positron emission tomography and fluorine-18 fluorodeoxyglucose: use and limitations. J Clin Oncol. 2000;18:3495–502.

    Article  CAS  PubMed  Google Scholar 

  168. Kumar R, Lal N, Alavi A. 18F-FDG PET in detecting primary breast cancer. J Nucl Med. 2007;48:1751 . doi:10.2967/jnumed.107.043265.author reply 1752

    Article  PubMed  Google Scholar 

  169. Tatsumi M, Cohade C, Mourtzikos KA, et al. Initial experience with FDG-PET/CT in the evaluation of breast cancer. Eur J Nucl Med Mol Imaging. 2006;33:254–62. doi:10.1007/s00259-005-1835-7.

    Article  PubMed  Google Scholar 

  170. Escalona S, Blasco JA, Reza MM, et al. A systematic review of FDG-PET in breast cancer. Med Oncol. 2010;27:114–29. doi:10.1007/s12032-009-9182-3.

    Article  CAS  PubMed  Google Scholar 

  171. Rosen EL, Eubank WB, Mankoff DA. FDG PET, PET/CT, and breast cancer imaging. Radiographics. 2007;27 Suppl 1:S215–29. doi:10.1148/rg.27si075517.

    Article  PubMed  Google Scholar 

  172. Samson DJ, Flamm CR, Pisano ED, Aronson N. Should FDG PET be used to decide whether a patient with an abnormal mammogram or breast finding at physical examination should undergo biopsy? Acad Radiol. 2002;9:773–83.

    Article  PubMed  Google Scholar 

  173. Magometschnigg HF, Baltzer PA, Fueger B, et al. Diagnostic accuracy of (18)F-FDG PET/CT compared with that of contrast-enhanced MRI of the breast at 3 T. Eur J Nucl Med Mol Imaging. 2015;42:1656–65. doi:10.1007/s00259-015-3099-1.

    Article  CAS  PubMed  Google Scholar 

  174. Kalles V, Zografos GC, Provatopoulou X, et al. The current status of positron emission mammography in breast cancer diagnosis. Breast Cancer. 2013;20:123–30. doi:10.1007/s12282-012-0433-3.

    Article  PubMed  Google Scholar 

  175. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74. doi:10.1016/j.cell.2011.02.013.

    Article  CAS  PubMed  Google Scholar 

  176. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100:57–70.

    Article  CAS  PubMed  Google Scholar 

  177. Tennant DA, Durán RV, Gottlieb E. Targeting metabolic transformation for cancer therapy. Nat Rev Cancer. 2010;10:267–77. doi:10.1038/nrc2817.

    Article  CAS  PubMed  Google Scholar 

  178. Tennant DA, Durán RV, Boulahbel H, Gottlieb E. Metabolic transformation in cancer. Carcinogenesis. 2009;30:1269–80. doi:10.1093/carcin/bgp070.

    Article  CAS  PubMed  Google Scholar 

  179. Trosko JE, Chang C-C, Upham BL, Tai M-H. Ignored hallmarks of carcinogenesis: stem cells and cell-cell communication. Ann N Y Acad Sci. 2004;1028:192–201. doi:10.1196/annals.1322.023.

    Article  CAS  PubMed  Google Scholar 

  180. Moy L, Ponzo F, Noz ME, et al. Improving specificity of breast MRI using prone PET and fused MRI and PET 3D volume datasets. J Nucl Med. 2007;48:528–37.

    Article  PubMed  Google Scholar 

  181. Domingues RC, Carneiro MP, Lopes FCR, et al. Whole-body MRI and FDG PET fused images for evaluation of patients with cancer. AJR Am J Roentgenol. 2009;192:1012–20. doi:10.2214/AJR.08.1498.

    Article  PubMed  Google Scholar 

  182. Moy L, Noz ME, Maguire GQ, et al. Role of fusion of prone FDG-PET and magnetic resonance imaging of the breasts in the evaluation of breast cancer. Breast J. 2010;16:369–76. doi:10.1111/j.1524-4741.2010.00927.x.

    PubMed  Google Scholar 

  183. Moy L, Noz ME, Maguire GQ, et al. Prone mammoPET acquisition improves the ability to fuse MRI and PET breast scans. Clin Nucl Med. 2007;32:194–8. doi:10.1097/01.rlu.0000255055.10177.80.

    Article  PubMed  Google Scholar 

  184. Pinker K, Bickel H, Magometschnigg H et al Molecular imaging of breast tumours with PET-MRI – proof of concept. In: ISMRM 2011, editor. (European Journal of Cancer, Montreal); 2011.

    Google Scholar 

  185. Pinker-Domenig K, Baltzer PA, Andrzejewski P, Magometschnigg H, Georg D, Karanikas G, Wadsak W, Kapetas P, Helbich TH. World molecular imaging congress 2015 scientific presentation dual tracer PET/MRI of breast tumors: insights into tumor biology.

    Google Scholar 

  186. Cheng J, Lei L, Xu J, et al. 18F-fluoromisonidazole PET/CT: a potential tool for predicting primary endocrine therapy resistance in breast cancer. J Nucl Med. 2013;54:333–40. doi:10.2967/jnumed.112.111963.

    Article  CAS  PubMed  Google Scholar 

  187. Blankenberg FG. In vivo detection of apoptosis. J Nucl Med. 2008;49 Suppl 2:81S–95. doi:10.2967/jnumed.107.045898.

    Article  PubMed  CAS  Google Scholar 

  188. Whisenant JG, Peterson TE, Fluckiger JU, et al. Reproducibility of static and dynamic (18)F-FDG, (18)F-FLT, and (18)F-FMISO MicroPET studies in a murine model of HER2+ breast cancer. Mol Imaging Biol. 2013;15:87–96. doi:10.1007/s11307-012-0564-0.

    Article  PubMed  PubMed Central  Google Scholar 

  189. Yang Z, Sun Y, Zhang Y, et al. Can fluorine-18 fluoroestradiol positron emission tomography-computed tomography demonstrate the heterogeneity of breast cancer in vivo? Clin Breast Cancer. 2013;13:359–63. doi:10.1016/j.clbc.2013.02.012.

    Article  CAS  PubMed  Google Scholar 

  190. van Kruchten M, de Vries EGE, Brown M, et al. PET imaging of oestrogen receptors in patients with breast cancer. Lancet Oncol. 2013;14:e465–75. doi:10.1016/S1470-2045(13)70292-4.

    Article  PubMed  CAS  Google Scholar 

  191. Smith-Jones PM, Solit DB, Akhurst T, et al. Imaging the pharmacodynamics of HER2 degradation in response to Hsp90 inhibitors. Nat Biotechnol. 2004;22:701–6. doi:10.1038/nbt968.

    Article  CAS  PubMed  Google Scholar 

  192. Smith-Jones PM, Solit D, Afroze F, et al. Early tumor response to Hsp90 therapy using HER2 PET: comparison with 18F-FDG PET. J Nucl Med. 2006;47:793–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  193. Tamura K, Kurihara H, Yonemori K, et al. 64Cu-DOTA-trastuzumab PET imaging in patients with HER2-positive breast cancer. J Nucl Med. 2013;54:1869–75. doi:10.2967/jnumed.112.118612.

    Article  CAS  PubMed  Google Scholar 

  194. Mortimer JE, Bading JR, Colcher DM, et al. Functional imaging of human epidermal growth factor receptor 2-positive metastatic breast cancer using (64)Cu-DOTA-trastuzumab PET. J Nucl Med. 2014;55:23–9. doi:10.2967/jnumed.113.122630.

    Article  CAS  PubMed  Google Scholar 

  195. Imbriaco M, Caprio MG, Limite G, et al. Dual-time-point 18F-FDG PET/CT versus dynamic breast MRI of suspicious breast lesions. AJR Am J Roentgenol. 2008;191:1323–30. doi:10.2214/AJR.07.3439.

    Article  PubMed  Google Scholar 

  196. van de Wiele C, Lahorte C, Vermeersch H, et al. Quantitative tumor apoptosis imaging using technetium-99 m-HYNIC annexin V single photon emission computed tomography. J Clin Oncol. 2003;21:3483–7. doi:10.1200/JCO.2003.12.096.

    Article  PubMed  Google Scholar 

  197. Vera P, Bohn P, Edet-Sanson A, et al. Simultaneous positron emission tomography (PET) assessment of metabolism with 18F-fluoro-2-deoxy-d-glucose (FDG), proliferation with 18F-fluoro-thymidine (FLT), and hypoxia with 18fluoro-misonidazole (F-miso) before and during radiotherapy in patients with non-small-cell lung cancer (NSCLC): a pilot study. Radiother Oncol. 2011;98:109–16. doi:10.1016/j.radonc.2010.10.011.

    Article  CAS  PubMed  Google Scholar 

  198. Rajendran JG, Wilson DC, Conrad EU, et al. [(18)F]FMISO and [(18)F]FDG PET imaging in soft tissue sarcomas: correlation of hypoxia, metabolism and VEGF expression. Eur J Nucl Med Mol Imaging. 2003;30:695–704. doi:10.1007/s00259-002-1096-7.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria

    Maria Adele Marino

  2. Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Messina, Italy

    Maria Adele Marino

  3. Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria

    Katja Pinker-Domenig MD

Authors
  1. Maria Adele Marino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katja Pinker-Domenig MD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katja Pinker-Domenig MD .

Editor information

Editors and Affiliations

  1. Department of Radiology, New York University Langone Medical Center New York University School of Medicine, New York, USA

    Samantha L. Heller

  2. Department of Radiology, New York University Langone Medical Center New York University School of Medicine, New York, USA

    Linda Moy

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Marino, M.A., Pinker-Domenig, K. (2017). Multiparametric Imaging: Cutting-Edge Sequences and Techniques Including Diffusion-Weighted Imaging, Magnetic Resonance Spectroscopy, and PET/CT or PET/MRI. In: Heller, S., Moy, L. (eds) Breast Oncology: Techniques, Indications, and Interpretation. Springer, Cham. https://doi.org/10.1007/978-3-319-42563-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42563-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42561-0

  • Online ISBN: 978-3-319-42563-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation