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European Radiology

, Volume 29, Issue 3, pp 1425–1434 | Cite as

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging

  • Shiteng Suo
  • Dandan Zhang
  • Fang Cheng
  • Mengqiu Cao
  • Jia HuaEmail author
  • Jinsong Lu
  • Jianrong XuEmail author
Magnetic Resonance
  • 181 Downloads

Abstract

Objectives

To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging.

Methods

One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated.

Results

Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative.

Conclusions

Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination.

Key Points

• ADC metrics are associated with molecular marker status in IDC.

• ADC 1500 improves differentiation of histologic phenotypes compared with ADC 800 .

• ADC metrics add value to morphologic features in IDC phenotyping.

Keywords

Diffusion magnetic resonance imaging Breast cancer Phenotype Immunohistochemistry Prognosis 

Abbreviations

ADC

Apparent diffusion coefficient

DCE

Dynamic contrast enhanced

DWI

Diffusion-weighted imaging

ER

Estrogen receptor

HER2

Human epidermal growth factor receptor 2

IDC

Invasive ductal carcinoma

IHC

Immunohistochemistry

OR

Odds ratio

PR

Progesterone receptor

ROC

Receiver-operating characteristic

ROI

Region of interest

SPAIR

Spectral adiabatic inversion recovery

THRIVE

T1-weighted high resolution isotropic volume examination

Notes

Funding

This study has received funding from the National Natural Science Foundation of China (nos. 81501458 and 81701642) and Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (nos. YG2015QN37 and YG2014ZD05).

Compliance with Ethical Standards

Guarantor

The scientific guarantor of this publication is Jianrong Xu.

Conflict of Interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and Biometry

No complex statistical methods were necessary for this paper.

Informed Consent

Written informed consent was waived by the Institutional Review Board.

Ethical Approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in:

Suo S, Cheng F, Cao M et al (2017) Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging. DOI: 10.1002/jmri.25612

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5667_MOESM1_ESM.docx (28 kb)
ESM 1 (DOCX 27 kb)

References

  1. 1.
    Lam SW, Jimenez CR, Boven E (2014) Breast cancer classification by proteomic technologies: current state of knowledge. Cancer Treat Rev 40:129–138CrossRefGoogle Scholar
  2. 2.
    Goldhirsch A, Winer EP, Coates AS et al (2013) Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 24:2206–2223CrossRefGoogle Scholar
  3. 3.
    Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:105–117Google Scholar
  4. 4.
    O'Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21:249–257CrossRefGoogle Scholar
  5. 5.
    Catalano OA, Horn GL, Signore A et al (2017) PET/MR in invasive ductal breast cancer: correlation between imaging markers and histological phenotype. Br J Cancer 116:893–902CrossRefGoogle Scholar
  6. 6.
    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 33:102–109CrossRefGoogle Scholar
  7. 7.
    Kim EJ, Kim SH, Park GE et al (2015) Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma. J Magn Reson Imaging 42:1666–1678CrossRefGoogle Scholar
  8. 8.
    Martincich L, Deantoni V, Bertotto I et al (2012) Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol 22:1519–1528CrossRefGoogle Scholar
  9. 9.
    Guvenc I, Akay S, Ince S et al (2016) Apparent diffusion coefficient value in invasive ductal carcinoma at 3.0 Tesla: is it correlated with prognostic factors? Br J Radiol 89:20150614CrossRefGoogle Scholar
  10. 10.
    Karan B, Pourbagher A, Torun N (2016) Diffusion-weighted imaging and (18) F-fluorodeoxyglucose positron emission tomography/computed tomography in breast cancer: Correlation of the apparent diffusion coefficient and maximum standardized uptake values with prognostic factors. J Magn Reson Imaging 43:1434–1444CrossRefGoogle Scholar
  11. 11.
    Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213CrossRefGoogle Scholar
  12. 12.
    Suo S, Zhang K, Cao M et al (2016) Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging 43:894–902CrossRefGoogle Scholar
  13. 13.
    Shin HJ, Kim SH, Lee HJ et al (2016) Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness in patients with estrogen-receptor-positive breast cancer. NMR Biomed 29:1070–1078CrossRefGoogle Scholar
  14. 14.
    American College of Radiology (2013) Breast Imaging Reporting and Data System (BI-RADS), 5th edn. American College of Radiology, Reston, VAGoogle Scholar
  15. 15.
    Uematsu T, Kasami M, Yuen S (2009) Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology 250:638–647CrossRefGoogle Scholar
  16. 16.
    Arponen O, Masarwah A, Sutela A et al (2016) Incidentally detected enhancing lesions found in breast MRI: analysis of apparent diffusion coefficient and T2 signal intensity significantly improves specificity. Eur Radiol 26:4361–4370CrossRefGoogle Scholar
  17. 17.
    Fujimoto K, Tonan T, Azuma S et al (2011) Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. Radiology 258:739–748CrossRefGoogle Scholar
  18. 18.
    Kim JH, Ko ES, Lim Y et al (2017) Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. Radiology 282:665–675CrossRefGoogle Scholar
  19. 19.
    Bustreo S, Osella-Abate S, Cassoni P et al (2016) Optimal Ki67 cut-off for luminal breast cancer prognostic evaluation: a large case series study with a long-term follow-up. Breast Cancer Res Treat 157:363–371CrossRefGoogle Scholar
  20. 20.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefGoogle Scholar
  21. 21.
    Iima M, Kataoka M, Kanao S et al (2018) Intravoxel incoherent motion and quantitative non-Gaussian diffusion MR imaging: evaluation of the diagnostic and prognostic value of several markers of malignant and benign breast lesions. Radiology 287:432–441CrossRefGoogle Scholar
  22. 22.
    Ludovini V, Sidoni A, Pistola L et al (2003) Evaluation of the prognostic role of vascular endothelial growth factor and microvessel density in stages I and II breast cancer patients. Breast Cancer Res Treat 81:159–168CrossRefGoogle Scholar
  23. 23.
    Jarque F, Lluch A, Vera FJ et al (1990) Intratumoral variation of estrogen and progesterone receptors in breast cancer: relationship with histopathological characteristics of the tumor. Oncology 47:9–13CrossRefGoogle Scholar
  24. 24.
    Järvinen TA, Pelto-Huikko M, Holli K, Isola J (2000) Estrogen receptor beta is coexpressed with ERalpha and PR and associated with nodal status, grade, and proliferation rate in breast cancer. Am J Pathol 156:29–35CrossRefGoogle Scholar
  25. 25.
    Vazquez-Martin A, Colomer R, Menendez JA (2007) Protein array technology to detect HER2 (erbB-2)-induced 'cytokine signature' in breast cancer. Eur J Cancer 43:1117–1124CrossRefGoogle Scholar
  26. 26.
    Kontzoglou K, Palla V, Karaolanis G et al (2013) Correlation between Ki67 and breast cancer prognosis. Oncology 84:219–225CrossRefGoogle Scholar
  27. 27.
    Suo S, Cheng F, Cao M et al (2017) Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging 46:740–750CrossRefGoogle Scholar
  28. 28.
    Lee HS, Kim SH, Kang BJ, Baek JE, Song BJ (2016) Perfusion parameters in dynamic contrast-enhanced MRI and apparent diffusion coefficient value in diffusion-weighted MRI: association with prognostic factors in breast cancer. Acad Radiol 23:446–456CrossRefGoogle Scholar
  29. 29.
    Surov A, Meyer HJ, Wienke A (2017) Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean. Oncotarget 8:75434–75444Google Scholar
  30. 30.
    Parker JS, Mullins M, Cheang MC et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167CrossRefGoogle Scholar
  31. 31.
    Youk JH, Son EJ, Chung J, Kim JA, Kim EK (2012) Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes. Eur Radiol 22:1724–1734CrossRefGoogle Scholar
  32. 32.
    Takanaga M, Hayashi N, Miyati T et al (2012) Influence of b value on the measurement of contrast and apparent diffusion coefficient in 3.0 Tesla breast magnetic resonance imaging. Nihon Hoshasen Gijutsu Gakkai Zasshi 68:201–208CrossRefGoogle Scholar
  33. 33.
    Tamura T, Murakami S, Naito K, Yamada T, Fujimoto T, Kikkawa T (2014) Investigation of the optimal b-value to detect breast tumors with diffusion weighted imaging by 1.5-T MRI. Cancer Imaging 14:11Google Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Renji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Breast Surgery, Renji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiChina

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