Advertisement

European Radiology

, Volume 28, Issue 11, pp 4860–4870 | Cite as

Imaging biomarkers from multiparametric magnetic resonance imaging are associated with survival outcomes in patients with brain metastases from breast cancer

  • Bang-Bin Chen
  • Yen-Shen Lu
  • Chih-Wei Yu
  • Ching-Hung Lin
  • Tom Wei-Wu Chen
  • Shwu-Yuan Wei
  • Ann-Lii Cheng
  • Tiffany Ting-Fang Shih
Oncology

Abstract

Objectives

The aim of this study is to investigate the correlation of survival outcomes with imaging biomarkers from multiparametric magnetic resonance imaging (MRI) in patients with brain metastases from breast cancer (BMBC).

Methods

This study was approved by the institutional review board. Twenty-two patients with BMBC who underwent treatment involving bevacizumab on day 1, etoposide on days 2-4, and cisplatin on day 2 in 21-day cycles were prospectively enrolled for a phase II study. Three brain MRIs were performed: before the treatment, on day 1, and on day 21. Eight imaging biomarkers were derived from dynamic contrast-enhanced MRI (Peak, IAUC60, Ktrans, kep, ve), diffusion-weighted imaging [apparent diffusion coefficient (ADC)], and MR spectroscopy (choline/N-acetylaspartate and choline/creatine ratios). The relative changes (Δ) in these biomarkers were correlated with the central nervous system (CNS)-specific progression-free survival (PFS) and overall survival (OS) using the Kaplan-Meier and Cox proportional hazard models.

Results

There were no significant differences in the survival outcomes as per the changes in the biomarkers on day 1. On day 21, those with a low ΔKtrans (p = 0.024) or ΔADC (p = 0.053) reduction had shorter CNS-specific PFS; further, those with a low ΔPeak (p = 0.012) or ΔIAUC60 (p = 0.04) reduction had shorter OS compared with those with high reductions. In multivariate analyses, ΔKtrans and ΔPeak were independent prognostic factors for CNS-specific PFS and OS, respectively, after controlling for age, size, hormone receptors, and performance status.

Conclusions

Multiparametric MRI may help predict the survival outcomes in patients with BMBC.

Key Points

• Decreased angiogenesis after chemotherapy on day 21 indicated good survival outcome.

• ΔK trans was an independent prognostic factors for CNS-specific PFS.

• ΔPeak was an independent prognostic factors for OS.

• Multiparametric MRI helps clinicians to assess patients with BMBC.

• High-risk patients may benefit from more intensive follow-up or treatment strategies.

Keywords

Breast cancer Neovascularization, pathologic Survival analysis Diffusion magnetic resonance imaging Magnetic resonance spectroscopy 

Abbreviations

1H-MRS

Proton magnetic resonance spectroscopy

ADC

Apparent diffusion coefficient

Cho

Choline

CNS

Central nerve system

Cre

Creatine

DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging

DWI

Diffusion-weighted imaging

ECOG

Eastern Cooperative Oncology Group

ER

Estrogen receptor

HER2

Human epidermal growth factor receptor 2

IAUC60

Initial area under curve for the first 60 s

kep

Reflux constant

Ktrans

Volume transfer constant

NAA

N-acetylaspartate

OS

Overall survival

Peak

(maximal signal – baseline signal)/baseline signal

PFS

Progression-free survival

PR

Progesterone receptor

ROI

Region of interest

ve

Extravascular extracellular volume fraction

Notes

Funding

This study has received funding by the National Taiwan University (NTU-ICRP-103R7557 to Y.S.L.), Ministry of Science and Technology, Taiwan (MOST 103-2314-B-002-170-MY3 to Y.S.L.)

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Tiffany Ting-Fang Shih.

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

Fu-Chang Hu, Sc.D., kindly provided statistical advice for this manuscript.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

All study subjects or cohorts have been previously reported.

This study is based on our previous works (Clin Cancer Res. 2015 Apr 15;21(8):1851-8; BMC Cancer. 2016 Jul 13;16:466. NCT01281696).

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Ferlay J, Soerjomataram I, Ervik M et al (2014) GLOBOCAN 2012 v1.1, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. Lyon, France: International Agency for Research on Cancer. Available from: http://globocan.iarc.fr, accessed on 24/11/2017.
  2. 2.
    Arvold ND, Oh KS, Niemierko A et al (2012) Brain metastases after breast-conserving therapy and systemic therapy: incidence and characteristics by biologic subtype. Breast Cancer Res Treat 136:153–160CrossRefGoogle Scholar
  3. 3.
    Matsuo S, Watanabe J, Mitsuya K, Hayashi N, Nakasu Y, Hayashi M (2017) Brain metastasis in patients with metastatic breast cancer in the real world: a single-institution, retrospective review of 12-year follow-up. Breast Cancer Res Treat 162:169–179CrossRefGoogle Scholar
  4. 4.
    Brufsky AM, Mayer M, Rugo HS et al (2011) Central nervous system metastases in patients with HER2-positive metastatic breast cancer: incidence, treatment, and survival in patients from registHER. Clin Cancer Res 17:4834–4843CrossRefGoogle Scholar
  5. 5.
    O'Connor JP, Jackson A, Parker GJ, Roberts C, Jayson GC (2012) Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat Rev Clin Oncol 9:167–177CrossRefGoogle Scholar
  6. 6.
    Lu YS, Chen TW, Lin CH et al (2015) Bevacizumab preconditioning followed by Etoposide and Cisplatin is highly effective in treating brain metastases of breast cancer progressing from whole-brain radiotherapy. Clin Cancer Res 21:1851–1858CrossRefGoogle Scholar
  7. 7.
    Chen BB, Lu YS, Lin CH et al (2016) A pilot study to determine the timing and effect of bevacizumab on vascular normalization of metastatic brain tumors in breast cancer. BMC Cancer 16:466CrossRefGoogle Scholar
  8. 8.
    Al-Okaili RN, Krejza J, Woo JH et al (2007) Intraaxial brain masses: MR imaging-based diagnostic strategy--initial experience. Radiology 243:539–550CrossRefGoogle Scholar
  9. 9.
    Usinskiene J, Ulyte A, Bjornerud A et al (2016) Optimal differentiation of high- and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics. Neuroradiology 58:339–350CrossRefGoogle Scholar
  10. 10.
    Messiou C, Orton M, Ang JE et al (2012) Advanced solid tumors treated with cediranib: comparison of dynamic contrast-enhanced MR imaging and CT as markers of vascular activity. Radiology 265:426–436CrossRefGoogle Scholar
  11. 11.
    Ellingson BM, Kim E, Woodworth DC et al (2015) Diffusion MRI quality control and functional diffusion map results in ACRIN 6677/RTOG 0625: a multicenter, randomized, phase II trial of bevacizumab and chemotherapy in recurrent glioblastoma. Int J Oncol 46:1883–1892CrossRefGoogle Scholar
  12. 12.
    Boonzaier NR, Larkin TJ, Matys T, van der Hoorn A, Yan JL, Price SJ (2017) Multiparametric MR imaging of diffusion and perfusion in contrast-enhancing and nonenhancing components in patients with glioblastoma. Radiology 284:180–190CrossRefGoogle Scholar
  13. 13.
    Oz G, Alger JR, Barker PB et al (2014) Clinical proton MR spectroscopy in central nervous system disorders. Radiology 270:658–679CrossRefGoogle Scholar
  14. 14.
    Padhani AR, Miles KA (2010) Multiparametric imaging of tumor response to therapy. Radiology 256:348–364CrossRefGoogle Scholar
  15. 15.
    Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232CrossRefGoogle Scholar
  16. 16.
    Vuori K, Kankaanranta L, Hakkinen AM et al (2004) Low-grade gliomas and focal cortical developmental malformations: differentiation with proton MR spectroscopy. Radiology 230:703–708CrossRefGoogle Scholar
  17. 17.
    Jung SC, Yeom JA, Kim JH et al (2014) Glioma: Application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading. AJNR Am J Neuroradiol 35:1103–1110CrossRefGoogle Scholar
  18. 18.
    Jung BC, Arevalo-Perez J, Lyo JK et al (2016) Comparison of glioblastomas and brain metastases using dynamic contrast-enhanced perfusion MRI. J Neuroimaging 26:240–246CrossRefGoogle Scholar
  19. 19.
    Jalali S, Chung C, Foltz W et al (2014) MRI biomarkers identify the differential response of glioblastoma multiforme to anti-angiogenic therapy. Neuro Oncol 16:868–879CrossRefGoogle Scholar
  20. 20.
    Gossmann A, Helbich TH, Kuriyama N et al (2002) Dynamic contrast-enhanced magnetic resonance imaging as a surrogate marker of tumor response to anti-angiogenic therapy in a xenograft model of glioblastoma multiforme. J Magn Reson Imaging 15:233–240CrossRefGoogle Scholar
  21. 21.
    Hawighorst H, Knopp MV, Debus J et al (1998) Pharmacokinetic MRI for assessment of malignant glioma response to stereotactic radiotherapy: initial results. J Magn Reson Imaging 8:783–788CrossRefGoogle Scholar
  22. 22.
    Chung WJ, Kim HS, Kim N, Choi CG, Kim SJ (2013) Recurrent glioblastoma: optimum area under the curve method derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging. Radiology 269:561–568CrossRefGoogle Scholar
  23. 23.
    Chen BB, Hsu CY, Yu CW et al (2011) Dynamic contrast-enhanced MR imaging measurement of vertebral bone marrow perfusion may be indicator of outcome of acute myeloid leukemia patients in remission. Radiology 258:821–831CrossRefGoogle Scholar
  24. 24.
    Chen BB, Hsu CY, Yu CW et al (2017) Early perfusion changes within 1 week of systemic treatment measured by dynamic contrast-enhanced MRI may predict survival in patients with advanced hepatocellular carcinoma. Eur Radiol 27:3069–3079CrossRefGoogle Scholar
  25. 25.
    Chen BB, Hsu CY, Yu CW et al (2016) Dynamic contrast-enhanced MR imaging of advanced hepatocellular carcinoma: comparison with the liver parenchyma and correlation with the survival of patients receiving systemic therapy. Radiology 281:454–464CrossRefGoogle Scholar
  26. 26.
    Piludu F, Marzi S, Pace A et al (2015) Early biomarkers from dynamic contrast-enhanced magnetic resonance imaging to predict the response to antiangiogenic therapy in high-grade gliomas. Neuroradiology 57:1269–1280CrossRefGoogle Scholar
  27. 27.
    O'Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186CrossRefGoogle Scholar
  28. 28.
    Panebianco V, Iacovelli R, Barchetti F et al (2013) Dynamic contrast-enhanced magnetic resonance imaging in the early evaluation of anti-angiogenic therapy in metastatic renal cell carcinoma. Anticancer Res 33:5663–5666PubMedGoogle Scholar
  29. 29.
    Thoeny HC, Ross BD (2010) Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging 32:2–16CrossRefGoogle Scholar
  30. 30.
    Chang K, Zhang B, Guo X et al (2016) Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro Oncol 18:1680–1687CrossRefGoogle Scholar
  31. 31.
    Pope WB, Kim HJ, Huo J et al (2009) Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 252:182–189CrossRefGoogle Scholar
  32. 32.
    Nguyen HS, Milbach N, Hurrell SL et al (2016) Progressing bevacizumab-induced diffusion restriction is associated with coagulative necrosis surrounded by viable tumor and decreased overall survival in patients with recurrent glioblastoma. AJNR Am J Neuroradiol 37:2201–2208CrossRefGoogle Scholar
  33. 33.
    Zakaria R, Das K, Radon M et al (2014) Diffusion-weighted MRI characteristics of the cerebral metastasis to brain boundary predicts patient outcomes. BMC Med Imaging 14:26CrossRefGoogle Scholar
  34. 34.
    Berghoff AS, Spanberger T, Ilhan-Mutlu A et al (2013) Preoperative diffusion-weighted imaging of single brain metastases correlates with patient survival times. PLoS One 8:e55464CrossRefGoogle Scholar
  35. 35.
    Lee CC, Wintermark M, Xu Z, Yen CP, Schlesinger D, Sheehan JP (2014) Application of diffusion-weighted magnetic resonance imaging to predict the intracranial metastatic tumor response to Gamma Knife radiosurgery. J Neurooncol 118:351–361CrossRefGoogle Scholar
  36. 36.
    Mardor Y, Roth Y, Lidar Z et al (2001) Monitoring response to convection-enhanced taxol delivery in brain tumor patients using diffusion-weighted magnetic resonance imaging. Cancer Res 61:4971–4973PubMedGoogle Scholar
  37. 37.
    Provenzale JM, Mukundan S, Barboriak DP (2006) Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology 239:632–649CrossRefGoogle Scholar
  38. 38.
    Garcia-Figueiras R, Baleato-Gonzalez S, Padhani AR et al (2016) Proton magnetic resonance spectroscopy in oncology: the fingerprints of cancer? Diagn Interv Radiol 22:75–89CrossRefGoogle Scholar
  39. 39.
    Kugel H, Heindel W, Ernestus RI, Bunke J, du Mesnil R, Friedmann G (1992) Human brain tumors: spectral patterns detected with localized H-1 MR spectroscopy. Radiology 183:701–709CrossRefGoogle Scholar
  40. 40.
    Kim H, Catana C, Ratai EM et al (2011) Serial magnetic resonance spectroscopy reveals a direct metabolic effect of cediranib in glioblastoma. Cancer Res 71:3745–3752CrossRefGoogle Scholar
  41. 41.
    Li Y, Lupo JM, Parvataneni R et al (2013) Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. Neuro Oncol 15:607–617CrossRefGoogle Scholar
  42. 42.
    Clarke JL, Molinaro AM, Phillips JJ et al (2014) A single-institution phase II trial of radiation, temozolomide, erlotinib, and bevacizumab for initial treatment of glioblastoma. Neuro Oncol 16:984–990CrossRefGoogle Scholar
  43. 43.
    Pirzkall A, McGue C, Saraswathy S et al (2009) Tumor regrowth between surgery and initiation of adjuvant therapy in patients with newly diagnosed glioblastoma. Neuro Oncol 11:842–852CrossRefGoogle Scholar
  44. 44.
    Park I, Tamai G, Lee MC et al (2007) Patterns of recurrence analysis in newly diagnosed glioblastoma multiforme after three-dimensional conformal radiation therapy with respect to pre-radiation therapy magnetic resonance spectroscopic findings. Int J Radiat Oncol Biol Phys 69:381–389CrossRefGoogle Scholar
  45. 45.
    Nelson SJ, Li Y, Lupo JM, Olson M et al (2016) Serial analysis of 3D H-1 MRSI for patients with newly diagnosed GBM treated with combination therapy that includes bevacizumab. J Neurooncol 130:171–179CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Bang-Bin Chen
    • 1
    • 2
  • Yen-Shen Lu
    • 3
    • 4
  • Chih-Wei Yu
    • 1
    • 2
  • Ching-Hung Lin
    • 3
    • 4
  • Tom Wei-Wu Chen
    • 3
    • 4
  • Shwu-Yuan Wei
    • 1
  • Ann-Lii Cheng
    • 3
    • 4
  • Tiffany Ting-Fang Shih
    • 1
    • 2
  1. 1.Department of RadiologyCollege of Medicine, National Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Medical ImagingNational Taiwan University HospitalTaipeiTaiwan
  3. 3.Department of OncologyNational Taiwan University HospitalTaipeiTaiwan
  4. 4.Department of Internal MedicineCollege of Medicine, National Taiwan UniversityTaipeiTaiwan

Personalised recommendations