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.
Similar content being viewed by others
References
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
Perou CM, Sorlie T, Eisen MB et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752
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
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
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
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
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
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
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
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
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
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
Koh DM, Collins DJ (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635
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
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
Hamstra DA, Rehemtulla A, Ross BD (2007) Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol 25:4104–4109
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
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
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
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
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
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
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
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
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
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
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
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
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
Weissleder R, Pittet MJ (2008) Imaging in the era of molecular oncology. Nature 452:580–589
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
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
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
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
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
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
Iacconi C (2010) Diffusion and perfusion of the breast. Eur J Radiol 76:386–390
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
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
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
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
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
Kawashima H (2010) Imaging findings of triple-negative breast cancer. Breast Cancer 18:145
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
Uematsu T, Kasami M, Yuen S (2009) Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology 250:638–647
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-012-2403-8