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Breast Cancer

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Abstract

Functional imaging techniques are rapidly gaining momentum as important tools in breast cancer diagnostics that can offer additional and potentially complementary information beyond traditional imaging modalities. They hold promise to improve sensitivity and specificity of breast cancer diagnosis, enhance the detection of tumour recurrence and facilitate the early assessment of response to treatment. Breast cancer is increasingly being recognised as a complex heterogeneous disease with different molecular subtypes that have specific imaging and response characteristics. The development of quantitative imaging biomarkers, translation of novel preclinical techniques and the integration of functional imaging in parallel with molecular diagnostics and gene expression profiling will allow a more personalised approach to breast cancer management which will improve diagnostic and therapeutic pathways for patients.

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Abbreviations

[Cho]:

Total choline concentration

18F-FDG:

Fluorine-18 fluorodeoxyglucose

1H-MRS:

Proton magnetic resonance spectroscopy

ADC:

Apparent diffusion coefficient

BI-RADS:

Breast imaging reporting and data system

BOLD:

Blood oxygen level dependent

CAD:

Computer-aided detection

CEST:

Chemical exchange saturation transfer

CEUS:

Contrast-enhanced ultrasound

CT:

Computed tomography

DCE-MRI:

Dynamic contrast-enhanced magnetic resonance imaging

DCIS:

Ductal carcinoma in situ

DWI:

Diffusion-weighted imaging

EGFR:

Epidermal growth factor receptor

ER:

Oestrogen receptor

Gd-DTPA:

Gadolinium-diethylene triamine pentaacetic acid

HER2:

Human epidermal growth factor receptor 2

HRT:

Hormone replacement therapy

IDC:

Invasive ductal carcinoma

ILC:

Invasive lobular carcinoma

LCIS:

Lobular carcinoma in situ

PEM:

Positron emission mammography

PET:

Positron emission tomography

PR:

Progesterone receptor

rBF:

Relative blood flow

rBV:

Relative blood volume

SPIO:

Superparamagnetic iron oxide

TNBC:

Triple negative breast cancer

USPIO:

Ultrasmall superparamagnetic iron oxide

References

  1. Ferlay J, et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893–917.

    CAS  PubMed  Google Scholar 

  2. General Register Office for Scotland. Deaths time series data, deaths in Scotland in 2010. 201.

    Google Scholar 

  3. Northern Ireland Statistics and Research Agency. Registrar General Annual Report 2010. 2010.

    Google Scholar 

  4. Office for National Statistics. Mortality statistics: deaths registered in 2010, England and Wales. 2010. London.

    Google Scholar 

  5. Autier P, et al. Disparities in breast cancer mortality trends between 30 European countries: retrospective trend analysis of WHO mortality database. BMJ. 2010;341:c3620.

    PubMed Central  PubMed  Google Scholar 

  6. Antoniou A, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003;72:1117–30.

    CAS  PubMed Central  PubMed  Google Scholar 

  7. Moss SM, et al. Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years’ follow-up: a randomised controlled trial. Lancet. 2006;368:2053–60.

    PubMed  Google Scholar 

  8. Bhatia S, et al. Breast cancer and other second neoplasms after childhood Hodgkin’s disease. N Engl J Med. 1996;334:745–51.

    CAS  PubMed  Google Scholar 

  9. Swerdlow AJ, et al. Risk of second malignancy after Hodgkin’s disease in a collaborative British cohort: the relation to age at treatment. J Clin Oncol. 2000;18:498–509.

    CAS  PubMed  Google Scholar 

  10. McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15:1159–69.

    PubMed  Google Scholar 

  11. Collaborative Group on Hormonal Factors in Breast Cancer. Breast cancer and hormonal contraceptives: collaborative reanalysis of individual data on 53,297 women with breast cancer and 100,239 women without breast cancer from 54 epidemiological studies. Lancet. 1996;347:1713–27.

    Google Scholar 

  12. Beral V. Breast cancer and hormone-replacement therapy in the Million Women Study. Lancet. 2003;362:419–27.

    CAS  PubMed  Google Scholar 

  13. Collaborative Group on Hormonal Factors in Breast Cancer. Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer. Lancet. 1997;350:1047–59.

    Google Scholar 

  14. Ewertz M, et al. Age at first birth, parity and risk of breast cancer: a meta-analysis of 8 studies from the Nordic countries. Int J Cancer. 1990;46:597–603.

    CAS  PubMed  Google Scholar 

  15. Ma H, et al. Reproductive factors and breast cancer risk according to joint estrogen and progesterone receptor status: a meta-analysis of epidemiological studies. Breast Cancer Res. 2006;8:R43.

    PubMed Central  PubMed  Google Scholar 

  16. Reeves GK, et al. Cancer incidence and mortality in relation to body mass index in the Million Women Study: cohort study. BMJ. 2007;335:1134.

    PubMed  Google Scholar 

  17. Baan R, et al. Carcinogenicity of alcoholic beverages. Lancet Oncol. 2007;8:292–3.

    PubMed  Google Scholar 

  18. Monninkhof EM, et al. Physical activity and breast cancer: a systematic review. Epidemiology. 2007;18:137–57.

    PubMed  Google Scholar 

  19. Soerjomataram I, et al. Risks of second primary breast and urogenital cancer following female breast cancer in the south of The Netherlands, 1972–2001. Eur J Cancer. 2005;41:2331–7.

    CAS  PubMed  Google Scholar 

  20. Hartmann LC, et al. Benign breast disease and the risk of breast cancer. N Engl J Med. 2005;353:229–37.

    CAS  PubMed  Google Scholar 

  21. Krecke KN, Gisvold JJ. Invasive lobular carcinoma of the breast: mammographic findings and extent of disease at diagnosis in 184 patients. AJR Am J Roentgenol. 1993;161:957–60.

    CAS  PubMed  Google Scholar 

  22. Wellings SR, Jensen HM. On the origin and progression of ductal carcinoma in the human breast. J Natl Cancer Inst. 1973;50:1111–8.

    CAS  PubMed  Google Scholar 

  23. Bloom HJ, Richardson WW. Histological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years. Br J Cancer. 1957;11:359–77.

    CAS  PubMed Central  PubMed  Google Scholar 

  24. Perou CM, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–52.

    CAS  PubMed  Google Scholar 

  25. Sorlie T, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98:10869–74.

    Google Scholar 

  26. Brenton JD, et al. Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J Clin Oncol. 2005;23:7350–60.

    CAS  PubMed  Google Scholar 

  27. Carey LA, et al. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA. 2006;295:2492–502.

    CAS  PubMed  Google Scholar 

  28. Fan C, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006;355:560–9.

    CAS  PubMed  Google Scholar 

  29. Nguyen PL, et al. Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy. J Clin Oncol. 2008;26:2373–8.

    PubMed  Google Scholar 

  30. Cardoso F, et al. Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol. 2008;26:729–35.

    PubMed  Google Scholar 

  31. Sparano JA, Paik S. Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol. 2008;26:721–8.

    PubMed  Google Scholar 

  32. Gotzsche PC, Nielsen M. Screening for breast cancer with mammography. Cochrane Database Syst Rev. 2011;19(1):CD001877.

    Google Scholar 

  33. Mandelson MT, et al. Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst. 2000;92:1081–7.

    CAS  PubMed  Google Scholar 

  34. Harvey JA, Bovbjerg VE. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology. 2004;230:29–41.

    PubMed  Google Scholar 

  35. Berg WA, et al. Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology. 2004;233:830–49.

    PubMed  Google Scholar 

  36. Hillman BJ, et al. Diagnostic performance of a dedicated 1.5-T breast MR imaging system. Radiology. 2012;265:51–8.

    PubMed  Google Scholar 

  37. Kuhl CK, et al. Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J Clin Oncol. 2005;23:8469–76.

    PubMed  Google Scholar 

  38. Leach MO, et al. Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). Lancet. 2005;365:1769–78.

    CAS  PubMed  Google Scholar 

  39. Kuhl CK. Current status of breast MR imaging. Part 2. Clinical applications. Radiology. 2007;244:672–91.

    PubMed  Google Scholar 

  40. Knopp MV, et al. Pathophysiologic basis of contrast enhancement in breast tumors. J Magn Reson Imaging. 1999;10:260–6.

    CAS  PubMed  Google Scholar 

  41. Kuhl CK, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211:101–10.

    CAS  PubMed  Google Scholar 

  42. Medeiros LR, et al. Accuracy of magnetic resonance in suspicious breast lesions: a systematic quantitative review and meta-analysis. Breast Cancer Res Treat. 2011;126:273–85.

    PubMed  Google Scholar 

  43. Peters NH, et al. Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology. 2008;246:116–24.

    PubMed  Google Scholar 

  44. Elsamaloty H, et al. Increasing accuracy of detection of breast cancer with 3-T MRI. AJR Am J Roentgenol. 2009;192(4):1142–8.

    Google Scholar 

  45. Chen X, et al. Meta-analysis of quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesions. BMC Cancer. 2010;10:693.

    PubMed Central  PubMed  Google Scholar 

  46. Bogner W, 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.

    PubMed  Google Scholar 

  47. Woodhams R, et al. ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci. 2005;4:35–42.

    PubMed  Google Scholar 

  48. Partridge SC, et al. Menstrual cycle variation of apparent diffusion coefficients measured in the normal breast using MRI. J Magn Reson Imaging. 2001;14:433–8.

    CAS  PubMed  Google Scholar 

  49. Partridge SC, 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.

    PubMed  Google Scholar 

  50. O’Flynn EA, et al. Diffusion weighted imaging of the normal breast: reproducibility of apparent diffusion coefficient measurements and variation with menstrual cycle and menopausal status. Eur Radiol. 2012;22:1512–8.

    PubMed  Google Scholar 

  51. Huang W, et al. Detection of breast malignancy: diagnostic MR protocol for improved specificity. Radiology. 2004;232:585–91.

    PubMed  Google Scholar 

  52. Meisamy S, 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.

    PubMed  Google Scholar 

  53. Mizukoshi W, et al. (1)H MR spectroscopy with external reference solution at 1.5 T for differentiating malignant and benign breast lesions: comparison using qualitative and quantitative approaches. Eur Radiol. 2013;23:75–83.

    PubMed  Google Scholar 

  54. Bartella L, et al. Enhancing nonmass lesions in the breast: evaluation with proton (1H) MR spectroscopy. Radiology. 2007;245:80–7.

    PubMed  Google Scholar 

  55. Varas X, et al. Revisiting the mammographic follow-up of BI-RADS category 3 lesions. AJR Am J Roentgenol. 2002;179:691–5.

    PubMed  Google Scholar 

  56. Berg WA, et al. Shear-wave elastography improves the specificity of breast US: the BE1 multinational study of 939 masses. Radiology. 2012;262:435–49.

    PubMed  Google Scholar 

  57. Zhao H, et al. Contrast-enhanced ultrasound is helpful in the differentiation of malignant and benign breast lesions. Eur J Radiol. 2010;73:288–93.

    PubMed  Google Scholar 

  58. Krouskop TA, et al. Elastic moduli of breast and prostate tissues under compression. Ultrason Imaging. 1998;20:260–74.

    CAS  PubMed  Google Scholar 

  59. Athanasiou A, et al. Breast lesions: quantitative elastography with supersonic shear imaging – preliminary results. Radiology. 2010;256:297–303.

    PubMed  Google Scholar 

  60. Cosgrove DO, et al. Shear wave elastography for breast masses is highly reproducible. Eur Radiol. 2012;22:1023–32.

    PubMed Central  PubMed  Google Scholar 

  61. Evans A, et al. Quantitative shear wave ultrasound elastography: initial experience in solid breast masses. Breast Cancer Res. 2010;12:R104.

    PubMed Central  PubMed  Google Scholar 

  62. Evans A, et al. Invasive breast cancer: relationship between shear-wave elastographic findings and histologic prognostic factors. Radiology. 2012;263:673–7.

    PubMed  Google Scholar 

  63. Balleyguier C, et al. New potential and applications of contrast-enhanced ultrasound of the breast: own investigations and review of the literature. Eur J Radiol. 2009;69:14–23.

    PubMed  Google Scholar 

  64. Saracco A, et al. Differentiation between benign and malignant breast tumors using kinetic features of real-time harmonic contrast-enhanced ultrasound. Acta Radiol. 2012;53:382–8.

    PubMed  Google Scholar 

  65. Sorelli PG, et al. Can contrast-enhanced sonography distinguish benign from malignant breast masses? J Clin Ultrasound. 2010;38:177–81.

    CAS  PubMed  Google Scholar 

  66. Berman CG. Recent advances in breast-specific imaging. Cancer Control. 2007;14:338–49.

    PubMed  Google Scholar 

  67. Berg WA, et al. High-resolution fluorodeoxyglucose positron emission tomography with compression (“positron emission mammography”) is highly accurate in depicting primary breast cancer. Breast J. 2006;12:309–23.

    PubMed  Google Scholar 

  68. Raylman RR, et al. The positron emission mammography/tomography breast imaging and biopsy system (PEM/PET): design, construction and phantom-based measurements. Phys Med Biol. 2008;53:637–53.

    PubMed  Google Scholar 

  69. Fleming RM. Mitochondrial uptake of sestamibi distinguishes between normal, inflammatory breast changes, pre-cancers, and infiltrating breast cancer. Integr Cancer Ther. 2002;1:229–37.

    PubMed  Google Scholar 

  70. Brem RF, et al. Approaches to improving breast cancer diagnosis using a high resolution, breast specific gamma camera. Phys Med. 2006;21 Suppl 1:17–9.

    PubMed  Google Scholar 

  71. Rhodes DJ, et al. Molecular breast imaging: a new technique using technetium Tc 99m scintimammography to detect small tumors of the breast. Mayo Clin Proc. 2005;80:24–30.

    PubMed  Google Scholar 

  72. Esserman L, et al. Utility of magnetic resonance imaging in the management of breast cancer: evidence for improved preoperative staging. J Clin Oncol. 1999;17:110–9.

    CAS  PubMed  Google Scholar 

  73. Sardanelli F, et al. Sensitivity of MRI versus mammography for detecting foci of multifocal, multicentric breast cancer in Fatty and dense breasts using the whole-breast pathologic examination as a gold standard. AJR Am J Roentgenol. 2004;183:1149–57.

    PubMed  Google Scholar 

  74. Siegmann KC, et al. Risk-benefit analysis of preoperative breast MRI in patients with primary breast cancer. Clin Radiol. 2009;64:403–13.

    CAS  PubMed  Google Scholar 

  75. Fisher B, et al. Eight-year results of a randomized clinical trial comparing total mastectomy and lumpectomy with or without irradiation in the treatment of breast cancer. N Engl J Med. 1989;320:822–8.

    CAS  PubMed  Google Scholar 

  76. Fisher B, et al. Twenty-five-year follow-up of a randomized trial comparing radical mastectomy, total mastectomy, and total mastectomy followed by irradiation. N Engl J Med. 2002;347:567–75.

    PubMed  Google Scholar 

  77. Costantini M, et al. Diffusion-weighted imaging in breast cancer: relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol. 2010;65:1005–12.

    CAS  PubMed  Google Scholar 

  78. Choi SY, et al. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol. 2012;85:e474–9.

    CAS  PubMed  Google Scholar 

  79. Li SP, et al. Vascular characterisation of triple negative breast carcinomas using dynamic MRI. Eur Radiol. 2011;21:1364–73.

    PubMed  Google Scholar 

  80. Youk JH, et al. Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes. Eur Radiol. 2012;22:1724–34.

    PubMed  Google Scholar 

  81. Martincich L, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol. 2012;22:1519–28.

    PubMed  Google Scholar 

  82. Sah RG, et al. Association of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 status with total choline concentration and tumor volume in breast cancer patients: an MRI and in vivo proton MRS study. Magn Reson Med. 2012;68:1039–47.

    CAS  PubMed  Google Scholar 

  83. Jeh SK, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging. 2011;33:102–9.

    PubMed  Google Scholar 

  84. Kim SH, et al. Diffusion-weighted imaging of breast cancer: correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging. 2009;30:615–20.

    PubMed  Google Scholar 

  85. Razek AA, et al. Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors. NMR Biomed. 2010;23:619–23.

    PubMed  Google Scholar 

  86. Kaufmann M, et al. Recommendations from an international consensus conference on the current status and future of neoadjuvant systemic therapy in primary breast cancer. Ann Surg Oncol. 2012;19:1508–16.

    PubMed  Google Scholar 

  87. Cameron DA, et al. Primary systemic therapy for operable breast cancer – 10-year survival data after chemotherapy and hormone therapy. Br J Cancer. 1997;76:1099–105.

    CAS  PubMed Central  PubMed  Google Scholar 

  88. Fisher B, et al. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J Clin Oncol. 1998;16:2672–85.

    CAS  PubMed  Google Scholar 

  89. Powles TJ, et al. Randomized trial of chemoendocrine therapy started before or after surgery for treatment of primary breast cancer. J Clin Oncol. 1995;13:547–52.

    CAS  PubMed  Google Scholar 

  90. Swain SM, et al. Neoadjuvant chemotherapy in the combined modality approach of locally advanced nonmetastatic breast cancer. Cancer Res. 1987;47:3889–94.

    CAS  PubMed  Google Scholar 

  91. Fisher B, Mamounas EP. Preoperative chemotherapy: a model for studying the biology and therapy of primary breast cancer. J Clin Oncol. 1995;13:537–40.

    CAS  PubMed  Google Scholar 

  92. Scholl SM, et al. Breast tumour response to primary chemotherapy predicts local and distant control as well as survival. Eur J Cancer. 1995;31A:1969–75.

    CAS  PubMed  Google Scholar 

  93. Junkermann H, von Fournier FD. Imaging methods for evaluating the response of breast carcinoma to preoperative chemotherapy. Radiologe. 1997;37:726–32.

    CAS  PubMed  Google Scholar 

  94. Mazouni C, et al. Residual ductal carcinoma in situ in patients with complete eradication of invasive breast cancer after neoadjuvant chemotherapy does not adversely affect patient outcome. J Clin Oncol. 2007;25:2650–5.

    CAS  PubMed  Google Scholar 

  95. Grimsby GM, et al. Is there concordance of invasive breast cancer pathologic tumor size with magnetic resonance imaging? Am J Surg. 2009;198:500–4.

    PubMed  Google Scholar 

  96. Partridge SC, et al. MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival. AJR Am J Roentgenol. 2005;184:1774–81.

    PubMed  Google Scholar 

  97. Johansen R, et al. Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE-MRI. J Magn Reson Imaging. 2009;29:1300–7.

    PubMed  Google Scholar 

  98. Hylton NM, 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.

    PubMed  Google Scholar 

  99. Prevos R, et al. Pre-treatment differences and early response monitoring of neoadjuvant chemotherapy in breast cancer patients using magnetic resonance imaging: a systematic review. Eur Radiol. 2012;22:2607–16.

    CAS  PubMed  Google Scholar 

  100. Marinovich ML, et al. Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI. Breast. 2012;21:669–77.

    CAS  PubMed  Google Scholar 

  101. Ah-See ML, et al. Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer. Clin Cancer Res. 2008;14:6580–9.

    CAS  PubMed  Google Scholar 

  102. Pickles MD, et al. Role of dynamic contrast enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy. Breast Cancer Res Treat. 2005;91:1–10.

    CAS  PubMed  Google Scholar 

  103. O’Connor JP, et al. DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Br J Cancer. 2007;96:189–95.

    PubMed Central  PubMed  Google Scholar 

  104. Li SP, et al. Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy. Radiology. 2011;260:68–78.

    PubMed  Google Scholar 

  105. Nilsen L, et al. Diffusion-weighted magnetic resonance imaging for pretreatment prediction and monitoring of treatment response of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Acta Oncol. 2010;49:354–60.

    PubMed  Google Scholar 

  106. Sharma U, et al. 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.

    PubMed  Google Scholar 

  107. Pickles MD, et al. Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging. 2006;24:843–7.

    PubMed  Google Scholar 

  108. Iacconi 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.

    PubMed  Google Scholar 

  109. Li XR, et al. DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Med Oncol. 2012;29:425–31.

    CAS  PubMed  Google Scholar 

  110. Park SH, et al. Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology. 2010;257:56–63.

    PubMed  Google Scholar 

  111. Partridge SC, et al. Differential diagnosis of mammographically and clinically occult breast lesions on diffusion-weighted MRI. J Magn Reson Imaging. 2010;31:562–70.

    PubMed  Google Scholar 

  112. Fangberget A, et al. Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imaging. Eur Radiol. 2011;21:1188–99.

    CAS  PubMed Central  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  114. Li SP, 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.

    PubMed  Google Scholar 

  115. Jiang L, 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(5):1083–92.

    Google Scholar 

  116. Jagannathan NR, 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.

    CAS  PubMed Central  PubMed  Google Scholar 

  117. Baek HM, et al. Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy. Radiology. 2009;251:653–62.

    PubMed  Google Scholar 

  118. Danishad KK, 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.

    CAS  PubMed  Google Scholar 

  119. Tozaki M, et al. Preliminary study of early response to neoadjuvant chemotherapy after the first cycle in breast cancer: comparison of 1H magnetic resonance spectroscopy with diffusion magnetic resonance imaging. Jpn J Radiol. 2010;28:101–9.

    CAS  PubMed  Google Scholar 

  120. Tozaki M, et al. Predicting pathological response to neoadjuvant chemotherapy in breast cancer with quantitative 1H MR spectroscopy using the external standard method. J Magn Reson Imaging. 2010;31:895–902.

    PubMed  Google Scholar 

  121. Cao MD, et al. Predicting long-term survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling. NMR Biomed. 2012;25:369–78.

    CAS  PubMed  Google Scholar 

  122. Cao MD, et al. Prognostic value of metabolic response in breast cancer patients receiving neoadjuvant chemotherapy. BMC Cancer. 2012;12:39.

    CAS  PubMed Central  PubMed  Google Scholar 

  123. Hayashi M, et al. Evaluation of tumor stiffness by elastography is predictive for pathologic complete response to neoadjuvant chemotherapy in patients with breast cancer. Ann Surg Oncol. 2012;19:3042–9.

    PubMed  Google Scholar 

  124. Evans A, et al. Can shear-wave elastography predict response to neoadjuvant chemotherapy in women with invasive breast cancer? Br J Cancer 2013;109(11):298–801.

    Google Scholar 

  125. Butcher DT, et al. A tense situation: forcing tumour progression. Nat Rev Cancer. 2009;9:108–22.

    CAS  PubMed Central  PubMed  Google Scholar 

  126. Schrader J, et al. Matrix stiffness modulates proliferation, chemotherapeutic response, and dormancy in hepatocellular carcinoma cells. Hepatology. 2011;53:1192–205.

    CAS  PubMed Central  PubMed  Google Scholar 

  127. Smith IC, et al. Positron emission tomography using [(18)F]-fluorodeoxy-D-glucose to predict the pathologic response of breast cancer to primary chemotherapy. J Clin Oncol. 2000;18:1676–88.

    CAS  PubMed  Google Scholar 

  128. Jacobs MA, et al. Monitoring of neoadjuvant chemotherapy using multiparametric, (2)(3)Na sodium MR, and multimodality (PET/CT/MRI) imaging in locally advanced breast cancer. Breast Cancer Res Treat. 2011;128:119–26.

    CAS  PubMed Central  PubMed  Google Scholar 

  129. Wu LM, et al. Can diffusion-weighted MR imaging and contrast-enhanced MR imaging precisely evaluate and predict pathological response to neoadjuvant chemotherapy in patients with breast cancer? Breast Cancer Res Treat. 2012;135:17–28.

    CAS  PubMed  Google Scholar 

  130. Agliozzo S, et al. Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features. Med Phys. 2012;39:1704–15.

    CAS  PubMed  Google Scholar 

  131. Hayashi Y, et al. Analysis of complete response by MRI following neoadjuvant chemotherapy predicts pathological tumor responses differently for molecular subtypes of breast cancer. Oncol Lett. 2013;5:83–9.

    PubMed Central  PubMed  Google Scholar 

  132. Kuzucan A, et al. Diagnostic performance of magnetic resonance imaging for assessing tumor response in patients with HER2-negative breast cancer receiving neoadjuvant chemotherapy is associated with molecular biomarker profile. Clin Breast Cancer. 2012;12:110–8.

    PubMed Central  PubMed  Google Scholar 

  133. Belli P, et al. Magnetic resonance imaging in breast cancer recurrence. Breast Cancer Res Treat. 2002;73:223–35.

    CAS  PubMed  Google Scholar 

  134. Seely JM, et al. Breast MRI in the evaluation of locally recurrent or new breast cancer in the postoperative patient: correlation of morphology and enhancement features with the BI-RADS category. Acta Radiol. 2007;48:838–45.

    CAS  PubMed  Google Scholar 

  135. Veronesi U, et al. Twenty-year follow-up of a randomized study comparing breast-conserving surgery with radical mastectomy for early breast cancer. N Engl J Med. 2002;347:1227–32.

    PubMed  Google Scholar 

  136. Robertson C, et al. The clinical effectiveness and cost-effectiveness of different surveillance mammography regimens after the treatment for primary breast cancer: systematic reviews registry database analyses and economic evaluation. Health Technol Assess. 2011;15:v-322.

    Google Scholar 

  137. Preda L, et al. Magnetic resonance mammography in the evaluation of recurrence at the prior lumpectomy site after conservative surgery and radiotherapy. Breast Cancer Res. 2006;8:R53.

    PubMed Central  PubMed  Google Scholar 

  138. Sardanelli F, et al. Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer. 2010;46:1296–316.

    PubMed  Google Scholar 

  139. Viehweg P, et al. Retrospective analysis for evaluation of the value of contrast-enhanced MRI in patients treated with breast conservative therapy. MAGMA. 1998;7:141–52.

    CAS  PubMed  Google Scholar 

  140. Rinaldi P, et al. DWI in breast MRI: role of ADC value to determine diagnosis between recurrent tumor and surgical scar in operated patients. Eur J Radiol. 2010;75:e114–23.

    PubMed  Google Scholar 

  141. Isasi CR, et al. A meta-analysis of FDG-PET for the evaluation of breast cancer recurrence and metastases. Breast Cancer Res Treat. 2005;90:105–12.

    CAS  PubMed  Google Scholar 

  142. Pan L, et al. FDG-PET and other imaging modalities for the evaluation of breast cancer recurrence and metastases: a meta-analysis. J Cancer Res Clin Oncol. 2010;136:1007–22.

    CAS  PubMed Central  PubMed  Google Scholar 

  143. Yilmaz MH, et al. The role of US and MR imaging in detecting local chest wall tumor recurrence after mastectomy. Diagn Interv Radiol. 2007;13:13–8.

    PubMed  Google Scholar 

  144. Fisher B, et al. Relation of number of positive axillary nodes to the prognosis of patients with primary breast cancer. An NSABP update. Cancer. 1983;52:1551–7.

    CAS  PubMed  Google Scholar 

  145. Pritchard KI, et al. Prospective study of 2-[(1)(8)F]fluorodeoxyglucose positron emission tomography in the assessment of regional nodal spread of disease in patients with breast cancer: an Ontario clinical oncology group study. J Clin Oncol. 2012;30:1274–9.

    CAS  PubMed  Google Scholar 

  146. Veronesi U, et al. Sentinel-node biopsy to avoid axillary dissection in breast cancer with clinically negative lymph-nodes. Lancet. 1997;349:1864–7.

    CAS  PubMed  Google Scholar 

  147. Mainiero MB, et al. Axillary ultrasound and fine-needle aspiration in the preoperative evaluation of the breast cancer patient: an algorithm based on tumor size and lymph node appearance. AJR Am J Roentgenol. 2010;195:1261–7.

    PubMed  Google Scholar 

  148. Mills P, et al. Axillary ultrasound assessment in primary breast cancer: an audit of 653 cases. Breast J. 2010;16:460–3.

    PubMed  Google Scholar 

  149. Sever AR, et al. Preoperative needle biopsy of sentinel lymph nodes using intradermal microbubbles and contrast-enhanced ultrasound in patients with breast cancer. AJR Am J Roentgenol. 2012;199:465–70.

    PubMed  Google Scholar 

  150. Ruehm SG, et al. Interstitial MR lymphography with gadoterate meglumine: initial experience in humans. Radiology. 2001;220:816–21.

    CAS  PubMed  Google Scholar 

  151. Shiozawa M, et al. Magnetic resonance lymphography of sentinel lymph nodes in patients with breast cancer using superparamagnetic iron oxide: a feasibility study. Breast Cancer. 2012;28 [E pub ahed of print].

    Google Scholar 

  152. Thoeny HC, et al. Combined ultrasmall superparamagnetic particles of iron oxide-enhanced and diffusion-weighted magnetic resonance imaging reliably detect pelvic lymph node metastases in normal-sized nodes of bladder and prostate cancer patients. Eur Urol. 2009;55:761–9.

    PubMed  Google Scholar 

  153. Bonini RH, et al. Magnetization transfer ratio as a predictor of malignancy in breast lesions: preliminary results. Magn Reson Med. 2008;59:1030–4.

    PubMed  Google Scholar 

  154. Dula AN, et al. Amide proton transfer imaging of the breast at 3 T: establishing reproducibility and possible feasibility assessing chemotherapy response. Magn Reson Med. 2012;70(1):216–24.

    Google Scholar 

  155. Gallagher FA, et al. Magnetic resonance imaging of pH in vivo using hyperpolarized 13C-labelled bicarbonate. Nature. 2008;453:940–3.

    CAS  PubMed  Google Scholar 

  156. Heijblom M, et al. Visualizing breast cancer using the Twente photoacoustic mammoscope: what do we learn from twelve new patient measurements? Opt Express. 2012;20:11582–97.

    CAS  PubMed  Google Scholar 

  157. Siegmann KC, et al. Diagnostic value of MR elastography in addition to contrast-enhanced MR imaging of the breast-initial clinical results. Eur Radiol. 2010;20:318–25.

    PubMed  Google Scholar 

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Acknowledgements

I would like to acknowledge the support and advice of radiology colleagues, radiographers and physicists at the Institute of Cancer Research and Royal Marsden Hospital during the preparation of the manuscript. I would also like to thank colleagues who have provided images for use in this chapter. Finally I would like to acknowledge the support received from the CRUK and EPSRC Cancer Imaging Centre in association with the MRC and Department of Health (England) grant C1060/A10334 and NHS funding to the NIHR Biomedical Research Centre.

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Correspondence to Elizabeth A. M. O’Flynn .

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O’Flynn, E.A.M. (2014). Breast Cancer. In: Luna, A., Vilanova, J., Hygino Da Cruz Jr., L., Rossi, S. (eds) Functional Imaging in Oncology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40582-2_10

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