European Radiology

, Volume 23, Issue 10, pp 2916–2925 | Cite as

Dynamic contrast-enhanced MRI in endometrial carcinoma identifies patients at increased risk of recurrence

  • Ingfrid S. Haldorsen
  • Renate Grüner
  • Jenny A. Husby
  • Inger J. Magnussen
  • Henrica M. J. Werner
  • Øyvind O. Salvesen
  • Line Bjørge
  • Ingunn Stefansson
  • Lars A. Akslen
  • Jone Trovik
  • Torfinn Taxt
  • Helga B. Salvesen



To study the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessment of tumour microvasculature in endometrial carcinoma patients, and to explore correlations with histological subtype, clinical course and microstructural characteristics based on apparent diffusion coefficient (ADC) values.


Diffusion-weighted imaging (DWI) and three-dimensional DCE-MRI (1.5 T) with high temporal resolution (2.49 s) were acquired preoperatively in 55 patients. Quantitative modelling allowed the calculation of four independent parameters describing microvasculature: blood flow (Fb), extraction fraction (E), capillary transit time (Tc) and transfer constant from the extravascular extracellular space [EES] to blood (Kep); and four derived parameters: blood volume (Vb), volume of EES (Ve), capillary permeability surface area product (PS) and transfer from blood to EES (Ktrans).


Endometrial carcinoma tissue exhibited reduced Fb, E, Vb, Ve, PS and Ktrans compared with normal myometrium. Non-endometrioid carcinomas (n = 12) had lower Fb, and E than endometrioid carcinomas (n = 43; P < 0.05). Tumour Ve positively correlated with tumour ADC value (r = 0.29, P = 0.03). Reduced survival was observed in patients with low tumour Fb and high tumour Tc (P < 0.05).


We demonstrate the feasibility of DCE-MRI in reflecting histological subtype and clinical course in primary endometrial carcinomas. DCE-MRI may potentially provide future biomarkers for preoperative risk stratification in endometrial carcinomas.

Key Points

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers new information about endometrial carcinoma.

Pelvic DCE-MRI with subsequent quantitative modelling seems feasible in endometrial carcinoma patients.

Low tumour perfusion is a feature of a more aggressive tumour subtype.

DCE-MRI provides potential biomarkers for preoperative risk stratification in endometrial carcinoma patients.


Endometrial carcinoma Magnetic resonance imaging Dynamic contrast-enhanced MRI Diffusion MRI Prognosis 



extraction fraction


extravascular extracellular space


blood flow


transfer constant from EES to blood


transfer from blood to EES


capillary permeability surface area product


intravascular/capillary transit time


blood volume


fractional volume of EES



This work was supported by The Western Norway Regional Health Authority, Research Funds at the Department of Radiology, Haukeland University Hospital, MedViz, Norwegian Research Council, The University of Bergen, The Meltzer Foundation, and The Norwegian Cancer Society (The Harald Andersen’s legacy).


  1. 1.
    Amant F, Moerman P, Neven P, Timmerman D, Van LE, Vergote I (2005) Endometrial cancer. Lancet 366:491–505PubMedCrossRefGoogle Scholar
  2. 2.
    Pecorelli S (2009) Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet 105:103–104PubMedCrossRefGoogle Scholar
  3. 3.
    Salvesen HB, Haldorsen IS, Trovik J (2012) Markers for individualised therapy in endometrial carcinoma. Lancet Oncol 13:e353–e361PubMedCrossRefGoogle Scholar
  4. 4.
    Kinkel K, Kaji Y, Yu KK et al (1999) Radiologic staging in patients with endometrial cancer: a meta-analysis. Radiology 212:711–718PubMedGoogle Scholar
  5. 5.
    Haldorsen IS, Salvesen HB (2012) Staging of endometrial carcinomas with MRI using traditional and novel MRI techniques. Clin Radiol 67:2–12PubMedCrossRefGoogle Scholar
  6. 6.
    Haldorsen IS, Husby JA, Werner HM et al (2012) Standard 1.5-T MRI of endometrial carcinomas: modest agreement between radiologists. Eur Radiol 22:1601–1611PubMedCrossRefGoogle Scholar
  7. 7.
    Shen SH, Chiou YY, Wang JH et al (2008) Diffusion-weighted single-shot echo-planar imaging with parallel technique in assessment of endometrial cancer. AJR Am J Roentgenol 190:481–488PubMedCrossRefGoogle Scholar
  8. 8.
    Beddy P, Moyle P, Kataoka M et al (2012) Evaluation of depth of myometrial invasion and overall staging in endometrial cancer: comparison of diffusion-weighted and dynamic contrast-enhanced MR imaging. Radiology 262:530–537PubMedCrossRefGoogle Scholar
  9. 9.
    Tamai K, Koyama T, Saga T et al (2007) Diffusion-weighted MR imaging of uterine endometrial cancer. J Magn Reson Imaging 26:682–687PubMedCrossRefGoogle Scholar
  10. 10.
    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 10:223–232PubMedCrossRefGoogle Scholar
  11. 11.
    Harry VN (2010) Novel imaging techniques as response biomarkers in cervical cancer. Gynecol Oncol 116:253–261PubMedCrossRefGoogle Scholar
  12. 12.
    Andersen EK, Hole KH, Lund KV et al (2012) Dynamic contrast-enhanced MRI of cervical cancers: temporal percentile screening of contrast enhancement identifies parameters for prediction of chemoradioresistance. Int J Radiat Oncol Biol Phys 82:e485–e492PubMedCrossRefGoogle Scholar
  13. 13.
    Zahra MA, Tan LT, Priest AN et al (2009) Semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging measurements predict radiation response in cervix cancer. Int J Radiat Oncol Biol Phys 74:766–773PubMedCrossRefGoogle Scholar
  14. 14.
    Coenegrachts K, Bols A, Haspeslagh M, Rigauts H (2012) Prediction and monitoring of treatment effect using T1-weighted dynamic contrast-enhanced magnetic resonance imaging in colorectal liver metastases: potential of whole tumour ROI and selective ROI analysis. Eur J Radiol 81:3870–3876PubMedCrossRefGoogle Scholar
  15. 15.
    Kinkel K, Forstner R, Danza FM et al (2009) Staging of endometrial cancer with MRI: guidelines of the European Society of Urogenital Imaging. Eur Radiol 19:1565–1574PubMedCrossRefGoogle Scholar
  16. 16.
    Taxt T, Jirik R, Rygh CB et al (2012) Single-channel blind estimation of arterial input function and tissue impulse response in DCE-MRI. IEEE Trans Biomed Eng 59:1012–1021PubMedCrossRefGoogle Scholar
  17. 17.
    Gruner R, Taxt T (2006) Iterative blind deconvolution in magnetic resonance brain perfusion imaging. Magn Reson Med 55:805–815PubMedCrossRefGoogle Scholar
  18. 18.
    St Lawrence KS, Lee TY (1998) An adiabatic approximation to the tissue homogeneity model for water exchange in the brain: I. Theoretical derivation. J Cereb Blood Flow Metab 18:1365–1377PubMedCrossRefGoogle Scholar
  19. 19.
    Silverberg SG, Kurman RJ, Nogales F (2003) Tumors of the uterine corpus. In: Tavassoli FA, Devilee P (eds) Tumours of the breast and female genital organs. World health organization classification of tumours. Pathology & genetics. IACR Press Inc., Lyon, pp 217–258Google Scholar
  20. 20.
    Carmeliet P, Jain RK (2011) Molecular mechanisms and clinical applications of angiogenesis. Nature 473:298–307PubMedCrossRefGoogle Scholar
  21. 21.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674PubMedCrossRefGoogle Scholar
  22. 22.
    Lin G, Ng KK, Chang CJ et al (2009) Myometrial invasion in endometrial cancer: diagnostic accuracy of diffusion-weighted 3.0-T MR imaging—initial experience. Radiology 250:784–792PubMedCrossRefGoogle Scholar
  23. 23.
    Rechichi G, Galimberti S, Signorelli M et al (2011) Endometrial cancer: correlation of apparent diffusion coefficient with tumor grade, depth of myometrial invasion, and presence of lymph node metastases. AJR Am J Roentgenol 197:256–262PubMedCrossRefGoogle Scholar
  24. 24.
    Perren TJ, Swart AM, Pfisterer J et al (2011) A phase 3 trial of bevacizumab in ovarian cancer. N Engl J Med 365:2484–2496PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2013

Authors and Affiliations

  • Ingfrid S. Haldorsen
    • 1
    • 2
  • Renate Grüner
    • 1
    • 3
  • Jenny A. Husby
    • 1
    • 2
  • Inger J. Magnussen
    • 1
  • Henrica M. J. Werner
    • 4
    • 5
  • Øyvind O. Salvesen
    • 6
  • Line Bjørge
    • 4
    • 5
  • Ingunn Stefansson
    • 7
    • 8
  • Lars A. Akslen
    • 7
    • 8
  • Jone Trovik
    • 4
    • 5
  • Torfinn Taxt
    • 1
    • 9
  • Helga B. Salvesen
    • 4
    • 5
  1. 1.Department of RadiologyHaukeland University HospitalBergenNorway
  2. 2.Section for Radiology, Centre for Cancer Biomarkers, Department of Clinical MedicineUniversity of BergenBergenNorway
  3. 3.Department of Physics and TechnologyUniversity of BergenBergenNorway
  4. 4.Department of Obstetrics and GynecologyHaukeland University HospitalBergenNorway
  5. 5.Centre for Cancer Biomarkers, Department of Clinical ScienceUniversity of BergenBergenNorway
  6. 6.Unit for applied Clinical Research, Department of Cancer Research and Molecular MedicineNorwegian University of Science and TechnologyTrondheimNorway
  7. 7.Centre for Cancer Biomarkers, The Gade Institute, Department of Clinical MedicineUniversity of BergenBergenNorway
  8. 8.Department of PathologyHaukeland University HospitalBergenNorway
  9. 9.Department of BiomedicineUniversity of BergenBergenNorway

Personalised recommendations