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Diagnostic performance of initial enhancement analysis using ultra-fast dynamic contrast-enhanced MRI for breast lesions

  • Mariko Goto
  • Koji Sakai
  • Hajime Yokota
  • Maki Kiba
  • Mariko Yoshida
  • Hiroshi Imai
  • Elisabeth Weiland
  • Isao Yokota
  • Kei Yamada
Breast

Abstract

Objectives

To assess the diagnostic value and contribution to BI-RADS categorisation of initial enhancement on ultra-fast DCE-MRI for differentiating malignant and benign breast lesions.

Methods

The institutional review board approved this study, and written informed consent was obtained from each participant. Both ultra-fast DCE-MRI for initial enhancement analysis and conventional MRI were performed on 200 subjects with a total of 215 lesions (147 malignant and 68 benign). BI-RADS categorisation of enhancing lesions was performed using the conventional MRI. Two initial enhancement measures, time to enhancement (TTE) and maximum slope (MS), were derived from the ultra-fast DCE-MRI. Diagnostic performance and the additional diagnostic value of adding TTE and MS to BI-RADS were evaluated.

Results

Both TTE and MS showed significant differences between malignant and benign breast lesions in masses (TTE, p <.001; MS, p = .006) and non-mass enhancement (NME) (TTE, p <.001; MS, p <.001). For masses, the AUC of TTE+MS combined with BI-RADS (0.864) was better than BI-RADS alone (0.823, p = .065). For NME, the AUC of TTE+MS combined with BI-RADS (0.923) was significantly larger than BI-RADS alone (0.865, p = .036), and diagnostic specificity improved by 40.9% (p = .005), without a significant decrease in the sensitivity (p = .083).

Conclusion

Initial enhancement analysis using ultra-fast DCE-MRI is especially useful for increasing the diagnostic performance of NME in breast MRI.

Key Points

• Ultra-fast dynamic MRI effectively differentiates benign from malignant breast lesions.

• Ultra-fast dynamic MRI contributes to BI-RADS categorisation in non-mass enhancement.

• Management of non-mass breast lesions becomes more appropriate.

Keywords

Breast neoplasms Magnetic resonance imaging Contrast media Kinetics Classification 

Abbreviations

ACR

American College of Radiology

BI-RADS

Breast imaging reporting and data system

CI

Confidence interval

DCIS

Ductal carcinoma in situ

FOV

Field of view

GRAPPA

Generalised autocalibrating partial parallel acquisition

HER2

Human epidermal growth factor receptor type 2

HR

Hormone receptor

IDC

Invasive ductal carcinoma

ILC

Invasive lobular carcinoma

IQR

Interquartile range

MS

Maximum slope

NME

Non-mass enhancement

TTE

Time to enhancement

T2WI

T2-weighted imaging

TWIST

Time-resolved angiography with interleaved stochastic trajectories

VIBE

Volume-interpolated breath-hold examination

Notes

Funding

This study has received funding by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP16K19840.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Mariko Goto MD, PhD, Assistant Professor of the Department of Radiology, Kyoto Prefectural University of Medicine.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Two of the co-authors (Elisabeth Weiland and Hiroshi Imai) are employees of Siemens Healthcare.

Statistics and biometry

One of the authors (Isao Yokota) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

References

  1. 1.
    American College of Radiology Breast Imaging Reporting and Data System (BI-RADS), vol 2013, 5th edn. American College of Radiology, Reston, VAGoogle Scholar
  2. 2.
    Helbich TH, Roberts TP, Gossmann A et al (2000) Quantitative gadopentetate-enhanced MRI of breast tumors: testing of different analytic methods. Magn Reson Med 44:915–924CrossRefPubMedGoogle Scholar
  3. 3.
    Gibbs P, Liney GP, Lowry M, Kneeshaw PJ, Turnbull LW (2004) Differentiation of benign and malignant sub-1 cm breast lesions using dynamic contrast enhanced MRI. Breast 13:115–121CrossRefPubMedGoogle Scholar
  4. 4.
    Boetes C, Barentsz JO, Mus RD et al (1994) MR characterization of suspicious breast lesions with a gadolinium-enhanced TurboFLASH subtraction technique. Radiology 193:777–781CrossRefPubMedGoogle Scholar
  5. 5.
    Sardanelli F, Rescinito G, Giordano GD, Calabrese M, Parodi RC (2000) MR dynamic enhancement of breast lesions: high temporal resolution during the first-minute versus eight-minute study. J Comput Assist Tomogr 24:724–731CrossRefPubMedGoogle Scholar
  6. 6.
    Goto M, Ito H, Akazawa K et al (2007) 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 25:104–112Google Scholar
  7. 7.
    Kuhl CK, Schild HH, Morakkabati N (2005) Dynamic bilateral contrast-enhanced MR imaging of the breast: trade-off between spatial and temporal resolution. Radiology 236:789–800CrossRefPubMedGoogle Scholar
  8. 8.
    Rosen EL, Baker JA, Soo MS (2002) Malignant lesions initially subjected to short-term mammographic follow-up. Radiology 223:221–228CrossRefPubMedGoogle Scholar
  9. 9.
    Ikeda DM, Baker DR, Daniel BL (2000) Magnetic resonance imaging of breast cancer: clinical indications and breast MRI reporting system. J Magn Reson Imaging 12:975–983CrossRefPubMedGoogle Scholar
  10. 10.
    Malich A, Boehm T, Facius M et al (2001) Differentiation of mammographically suspicious lesions: evaluation of breast ultrasound, MRI mammography and electrical impedance scanning as adjunctive technologies in breast cancer detection. Clin Radiol 56:278–283CrossRefPubMedGoogle Scholar
  11. 11.
    Malich A, Fischer DR, Wurdinger S et al (2005) Potential MRI interpretation model: differentiation of benign from malignant breast masses. AJR Am J Roentgenol 185:964–970CrossRefPubMedGoogle Scholar
  12. 12.
    Peters NH, Borel Rinkes IH, Zuithoff NP, Mali WP, Moons KG, Peeters PH (2008) Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology 246:116–124CrossRefPubMedGoogle Scholar
  13. 13.
    Dijkstra H, Dorrius MD, Wielema M, Pijnappel RM, Oudkerk M, Sijens PE (2016) Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions. J Magn Reson Imaging 44:1642–1649CrossRefPubMedGoogle Scholar
  14. 14.
    Kul S, Cansu A, Alhan E, Dinc H, Gunes G, Reis A (2011) Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors. AJR Am J Roentgenol 196:210–217CrossRefPubMedGoogle Scholar
  15. 15.
    Le Y, Kipfer H, Majidi S et al (2013) Application of Time-Resolved Angiography With Stochastic Trajectories (TWIST) -Dixon in Dynamic Contrast-Enhanced (DCE) Breast MRI. J Magn Reson Imaging 38:1033–1042CrossRefPubMedGoogle Scholar
  16. 16.
    Mann RM, Mus RD, van Zelst J, Geppert C, Karssemeijer N, Platel B (2014) A novel approach to contrast-enhanced breast magnetic resonance imaging for screening: high-resolution ultrafast dynamic imaging. Invest Radiol 49:579–585CrossRefPubMedGoogle Scholar
  17. 17.
    Mus RD, Borelli C, Bult P et al (2017) Time to enhancement derived from ultrafast breast MRI as a novel parameter to discriminate benign from malignant breast lesions. Eur J Radiol 89:90–96CrossRefPubMedGoogle Scholar
  18. 18.
    Tozaki M, Fukuma E (2009) 1H MR spectroscopy and diffusion-weighted imaging of the breast: are they useful tools for characterizing breast lesions before biopsy? AJR Am J Roentgenol 193:840–849CrossRefPubMedGoogle Scholar
  19. 19.
    Maltez de Almeida JR, Gomes AB, Barros TP, Fahel PE, de Seixas Rocha M (2015) Subcategorization of Suspicious Breast Lesions (BI-RADS Category 4) According to MRI Criteria: Role of Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging. AJR Am J Roentgenol 205:222–231CrossRefPubMedGoogle Scholar
  20. 20.
    Zou GY (2012) Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Stat Med 31:3972–3981CrossRefPubMedGoogle Scholar
  21. 21.
    Matsuoka T, Imai A, Fujimoto H et al (2017) Reduced Pineal Volume in Alzheimer Disease: A Retrospective Cross-sectional MR Imaging Study. Radiology 286:239–248CrossRefPubMedGoogle Scholar
  22. 22.
    Faul F, Erdfelder E, Lang AG, Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39:175–191CrossRefPubMedGoogle Scholar
  23. 23.
    Koo HR, Cho N, Song IC et al (2012) Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers. J Magn Rson Imaging 36:145–151CrossRefGoogle Scholar
  24. 24.
    Li SP, Padharni AR, Taylor NM et al (2011) Vascular characterization of triple negative breast carcinomas using dynamic MRI. Eur Radiol 21:1364–1373CrossRefPubMedGoogle Scholar
  25. 25.
    Imamura T, Isomoto I, Sueyoshi E et al (2010) Diagnostic performance of ADC for Non-mass-like breast lesions on MR imaging. Magn Reson Med Sci 9:217–225CrossRefPubMedGoogle Scholar
  26. 26.
    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 75:126–132CrossRefGoogle Scholar
  27. 27.
    Tozaki M, Igarashi T, Fukuda K (2006) Positive and negative predictive values of BI-RADS-MRI descriptors for focal breast masses. Magn Reson Med Sci 5:7–15CrossRefPubMedGoogle Scholar
  28. 28.
    Schnall MD, Blume J, Bluemke DA et al (2006) Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 238:42–53CrossRefPubMedGoogle Scholar
  29. 29.
    Kim SH, Lee HS, Kang BJ et al (2016) Dynamic Contrast-Enhanced MRI Perfusion Parameters as Imaging Biomarkers of Angiogenesis. PLoS One.  https://doi.org/10.1371/journal.pone.0168632
  30. 30.
    Monzawa S, Yokokawa M, Sakuma T et al (2009) Mucinous carcinoma of the breast: MRI features of pure and mixed forms with histopathologic correlation. AJR Am J Roentgenol 192:125–131CrossRefGoogle Scholar
  31. 31.
    Orel SG, Mednonca MH, Reynolds C, Schnall MD, Solin LJ, Sullivan DC (1997) MR imaging of ductal carcinoma in situ. Radiology 202:413–420CrossRefPubMedGoogle Scholar
  32. 32.
    Szabó BK, Aspelin P, Kristoffersen Wiberg M, Tot T, Boné B (2003) Invasive breast cancer: correlation of dynamic MR features with prognostic factors. Eur Radiol 13:2425–2435CrossRefPubMedGoogle Scholar
  33. 33.
    Baltzer PA, Vag T, Dietzel M et al (2010) Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer. Eur Radiol 20:1563–1571CrossRefPubMedGoogle Scholar
  34. 34.
    El Khouli RH, Macura KJ, Kamel IR, Jacobs MA, Bluemke DA (2011) 3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis. AJR Am J Roentgenol 197:1498–1505CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Yi B, Kang DK, Yoon D et al (2014) Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients? Eur Radiol 24:1089–1096CrossRefPubMedGoogle Scholar
  36. 36.
    Milosevic ZC, Nadrljanski MM, Milovanovic ZM, Gusic NZ, Vucicevic SS, Radulovic OS (2017) Breast Dynamic Contrast Enhanced MRI: Fibrocystic Changes Presenting as a Non-mass Enhancement Mimicking Malignancy. Radiol Oncol 51:130–136CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Yuen S, Uematsu T, Masako K, Uchida Y, Nishimura T (2008) Segmental enhancement on breast MR images: differential diagnosis and diagnostic strategy. Eur Radiol 18:2067–2075CrossRefPubMedGoogle Scholar
  38. 38.
    Uematsu T, Kasami M (2012) High-spatial-resolution 3-T breast MRI of nonmasslike enhancement lesions: an analysis of their features as significant predictors of malignancy. AJR Am J Roentgenol 198:1223–1230CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Graduate School of Medical ScienceKyoto Prefectural University of MedicineKyotoJapan
  2. 2.Department of RadiologyChiba University HospitalChibaJapan
  3. 3.Department of RadiologyJapanese Red Cross Kyoto Daini HospitalKyotoJapan
  4. 4.Siemens Healthcare K.K.TokyoJapan
  5. 5.Siemens Healthcare GmbHErlangenGermany
  6. 6.Department of Biostatistics, Graduate School of Medical ScienceKyoto Prefectural University of MedicineKyotoJapan

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