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Correlations between diffusion-weighted imaging and breast cancer biomarkers

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Abstract

Objective

We evaluated whether the apparent diffusion coefficient (ADC) provided by diffusion-weighted imaging (DWI) varies according to biological features in breast cancer.

Methods

DWI was performed in 190 patients undergoing dynamic contrast-enhanced magnetic resonance imaging (MRI) for local staging. For each of the 192 index cancers we studied the correlation between ADC and classical histopathological and immunohistochemical breast tumour features (size, histological type, grade, oestrogen receptor [ER] and Ki-67 expression, HER2 status). ADC was compared with immunohistochemical surrogates of the intrinsic subtypes (Luminal A; Luminal B; HER2-enriched; triple-negative). Correlations were analysed using the Mann–Whitney U and Kruskal–Wallis H tests.

Results

A weak, statistically significant correlation was observed between ADC values and the percentage of ER-positive cells (-0.168, P = 0.020). Median ADC values were significantly higher in ER-negative than in ER-positive tumours (1.110 vs 1.050 × 10-3 mm2/s, P = 0.015). HER2-enriched tumours had the highest median ADC value (1.190 × 10-3 mm2/s, range 0.950–2.090). Multiple comparisons showed that this value was significantly higher than that of Luminal A (1.025 × 10-3 mm2/s [0.700–1.340], P = 0.004) and Luminal B/HER2-negative (1.060 × 10-3 mm2/s [0.470–2.420], P = 0.008) tumours. A trend towards statistical significance (P = 0.018) was seen with Luminal B/HER2-positive tumours.

Conclusions

ADC values vary significantly according to biological tumour features, suggesting that cancer heterogeneity influences imaging parameters.

Key Points

DWI may identify biological heterogeneity of breast neoplasms.

ADC values vary significantly according to biological features of breast cancer.

Compared with other types, HER2-enriched tumours show highest median ADC value.

Knowledge of biological heterogeneity of breast neoplasm may improve imaging interpretation.

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References

  1. Goldhirsch A, Ingle JN, Gelber RD, Coates AS, Thurlimann B, Senn HJ (2009) Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol 20:1319–1329

    Article  PubMed  CAS  Google Scholar 

  2. Perou CM, Sorlie T, Eisen MB et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752

    Article  PubMed  CAS  Google Scholar 

  3. Sorlie T, Perou CM, Tibshirani R et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98:10869–10874

    Article  PubMed  CAS  Google Scholar 

  4. Parker JS, Mullins M, Cheang MC et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167

    Article  PubMed  Google Scholar 

  5. Cheang MC, Chia SK, Voduc D et al (2009) Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 101:736–750

    Article  PubMed  CAS  Google Scholar 

  6. Hugh J, Hanson J, Cheang MC et al (2009) Breast cancer subtypes and response to docetaxel in node-positive breast cancer: use of an immunohistochemical definition in the BCIRG 001 trial. J Clin Oncol 27:1168–1176

    Article  PubMed  CAS  Google Scholar 

  7. Penault-Llorca F, Andre F, Sagan C et al (2009) Ki67 expression and docetaxel efficacy in patients with estrogen receptor-positive breast cancer. J Clin Oncol 27:2809–2815

    Article  PubMed  CAS  Google Scholar 

  8. Montemurro F, Martincich L, Sarotto I et al (2007) Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer. Eur Radiol 17:1490–1497

    Article  PubMed  Google Scholar 

  9. Tuncbilek N, Karakas HM, Okten OO (2005) Dynamic magnetic resonance imaging in determining histopathological prognostic factors of invasive breast cancers. Eur J Radiol 53:199–205

    Article  PubMed  Google Scholar 

  10. Chang YW, Kwon KH, Choi DL et al (2009) Magnetic resonance imaging of breast cancer and correlation with prognostic factors. Acta Radiol 50:990–998

    Article  PubMed  Google Scholar 

  11. Loo CE, Straver ME, Rodenhuis S et al (2011) Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype. J Clin Oncol 29:660–666

    Article  PubMed  Google Scholar 

  12. Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R (2006) Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics 26(Suppl 1):S205–S223

    Article  PubMed  Google Scholar 

  13. Koh DM, Collins DJ (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635

    Article  PubMed  Google Scholar 

  14. Schnapauff D, Zeile M, Niederhagen MB et al (2009) Diffusion-weighted echo-planar magnetic resonance imaging for the assessment of tumor cellularity in patients with soft-tissue sarcomas. J Magn Reson Imaging 29:1355–1359

    Article  PubMed  Google Scholar 

  15. Pickles MD, Gibbs P, Lowry M, Turnbull LW (2006) Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging 24:843–847

    Article  PubMed  Google Scholar 

  16. Hamstra DA, Rehemtulla A, Ross BD (2007) Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol 25:4104–4109

    Article  PubMed  Google Scholar 

  17. Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD (2009) Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. AJR Am J Roentgenol 193:1716–1722

    Article  PubMed  Google Scholar 

  18. Tsushima Y, Takahashi-Taketomi A, Endo K (2009) Magnetic resonance (MR) differential diagnosis of breast tumors using apparent diffusion coefficient (ADC) on 1.5-T. J Magn Reson Imaging 30:249–255

    Article  PubMed  Google Scholar 

  19. Jeh SK, Kim SH, Kim HS et al (2011) Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging 33:102–109

    Article  PubMed  Google Scholar 

  20. Kim SH, Cha ES, Kim HS et al (2009) Diffusion-weighted imaging of breast cancer: correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging 30:615–620

    Article  PubMed  Google Scholar 

  21. The American College of Radiology (2011) ACR practice guidelines for the performance of contrast enhanced magnetic resonance imaging (MRI) of the breast. http://www.acr.org/SecondaryMainMenuCategories/quality_safety/guidelines/breast/MRI-Guided-Breast.aspx

  22. Sardanelli F, Boetes C, Borisch B et al (2010) Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer 46:1296–1316

    Article  PubMed  Google Scholar 

  23. Rausch DR, Hendrick RE (2006) How to optimize clinical breast MR imaging practices and techniques on Your 1.5-T system. Radiographics 26:1469–1484

    Article  PubMed  Google Scholar 

  24. Padhani AR, Liu G, Koh DM et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11:102–125

    PubMed  CAS  Google Scholar 

  25. Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19:403–410

    Article  PubMed  CAS  Google Scholar 

  26. Wolff AC, Hammond ME, Schwartz JN et al (2007) American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 25:118–145

    Article  PubMed  CAS  Google Scholar 

  27. Park S, Koo JS, Kim MS et al (2012) Characteristics and outcomes according to molecular subtypes of breast cancer as classified by a panel of four biomarkers using immunohistochemistry. Breast 21:50–57

    Article  PubMed  Google Scholar 

  28. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ (2011) Strategies for subtypes – dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22:1736–1747

    Article  PubMed  CAS  Google Scholar 

  29. Moffat BA, Chenevert TL, Lawrence TS et al (2005) Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A 102:5524–5529

    Article  PubMed  CAS  Google Scholar 

  30. Weissleder R, Pittet MJ (2008) Imaging in the era of molecular oncology. Nature 452:580–589

    Article  PubMed  CAS  Google Scholar 

  31. Gore JC, Manning HC, Quarles CC, Waddell KW, Yankeelov TE (2011) Magnetic resonance in the era of molecular imaging of cancer. Magn Reson Imaging 29:587–600

    Article  PubMed  CAS  Google Scholar 

  32. Razek AA, Gaballa G, Denewer A, Nada N (2010) Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors. NMR Biomed 23:619–623

    Article  PubMed  Google Scholar 

  33. Costantini M, Belli P, Rinaldi P et al (2010) Diffusion-weighted imaging in breast cancer: relationship between apparent diffusion coefficient and tumour aggressiveness. Clin Radiol 65:1005–1012

    Article  PubMed  CAS  Google Scholar 

  34. Heo SH, Jeong YY, Shin SS et al (2010) Apparent diffusion coefficient value of diffusion-weighted imaging for hepatocellular carcinoma: correlation with the histologic differentiation and the expression of vascular endothelial growth factor. Korean J Radiol 11:295–303

    Article  PubMed  Google Scholar 

  35. Muraoka N, Uematsu H, Kimura H et al (2008) Apparent diffusion coefficient in pancreatic cancer: characterization and histopathological correlations. J Magn Reson Imaging 27:1302–1308

    Article  PubMed  Google Scholar 

  36. Rosenkrantz AB, Niver BE, Fitzgerald EF, Babb JS, Chandarana H, Melamed J (2010) Utility of the apparent diffusion coefficient for distinguishing clear cell renal cell carcinoma of low and high nuclear grade. AJR Am J Roentgenol 195:W344–W351

    Article  PubMed  Google Scholar 

  37. Iacconi C (2010) Diffusion and perfusion of the breast. Eur J Radiol 76:386–390

    Article  PubMed  Google Scholar 

  38. Marini C, Iacconi C, Giannelli M, Cilotti A, Moretti M, Bartolozzi C (2007) Quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesion. Eur Radiol 17:2646–2655

    Article  PubMed  CAS  Google Scholar 

  39. Yabuuchi H, Matsuo Y, Okafuji T et al (2008) Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. J Magn Reson Imaging 28:1157–1165

    Article  PubMed  Google Scholar 

  40. Matsuoka A, Minato M, Harada M et al (2008) Comparison of 3.0- and 1.5-Tesla diffusion-weighted imaging in the visibility of breast cancer. Radiat Med 26:15–20

    Article  PubMed  Google Scholar 

  41. Woodhams R, Kakita S, Hata H et al (2009) Diffusion-weighted imaging of mucinous carcinoma of the breast: evaluation of apparent diffusion coefficient and signal intensity in correlation with histologic findings. AJR Am J Roentgenol 193:260–266

    Article  PubMed  Google Scholar 

  42. Seo BK, Pisano ED, Kuzimak CM et al (2006) Correlation of HER-2/neu overexpression with mammography and age distribution in primary breast carcinomas. Acad Radiol 13:1211–1218

    Article  PubMed  Google Scholar 

  43. Kawashima H (2010) Imaging findings of triple-negative breast cancer. Breast Cancer 18:145

    Article  Google Scholar 

  44. Dogan BE, Gonzalez-Angulo AM, Gilcrease M, Dryden MJ, Yang WT (2010) Multimodality imaging of triple receptor-negative tumors with mammography, ultrasound, and MRI. AJR Am J Roentgenol 194:1160–1166

    Article  PubMed  Google Scholar 

  45. Uematsu T, Kasami M, Yuen S (2009) Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology 250:638–647

    Article  PubMed  Google Scholar 

  46. Yabuuchi H, Matsuo Y, Kamitani T et al (2010) Non-mass-like enhancement on contrast-enhanced breast MR imaging: lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. Eur J Radiol 75:e126–e132

    Article  PubMed  Google Scholar 

  47. Baltzer PA, Benndorf M, Dietzel M, Gajda M, Camara O, Kaiser WA (2010) Sensitivity and specificity of unenhanced MR mammography (DWI combined with T2-weighted TSE imaging, ueMRM) for the differentiation of mass lesions. Eur Radiol 20:1101–1110

    Article  PubMed  Google Scholar 

  48. Woodhams R, Kakita S, Hata H et al (2010) Identification of residual breast carcinoma following neoadjuvant chemotherapy: diffusion-weighted imaging – comparison with contrast-enhanced MR imaging and pathologic findings. Radiology 254:357–366

    Article  PubMed  Google Scholar 

  49. Park SH, Moon WK, Cho N et al (2011) Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology 257:56–63

    Article  Google Scholar 

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Correspondence to Laura Martincich.

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Martincich, L., Deantoni, V., Bertotto, I. et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol 22, 1519–1528 (2012). https://doi.org/10.1007/s00330-012-2403-8

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  • DOI: https://doi.org/10.1007/s00330-012-2403-8

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