Abdominal Radiology

, Volume 42, Issue 5, pp 1342–1349 | Cite as

Correlation between quantitative and semiquantitative parameters in DCE-MRI with a blood pool agent in rectal cancer: can semiquantitative parameters be used as a surrogate for quantitative parameters?

  • Rebecca A. P. Dijkhoff
  • Monique MaasEmail author
  • Milou H. Martens
  • Nikolaos Papanikolaou
  • Doenja M. J. Lambregts
  • Geerard L. Beets
  • Regina G. H. Beets-Tan



The aim of this study was to assess correlation between quantitative and semiquantitative parameters in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in rectal cancer patients, both in a primary staging and restaging setting.

Materials and methods

Nineteen patients were included with DCE-MRI before and/or after neoadjuvant therapy. DCE-MRI was performed with gadofosveset trisodium (Ablavar®, Lantheus Medical Imaging, North Billerica, Massachusetts, USA). Regions of interest were placed in the tumor and quantitative parameters were extracted with Olea Sphere 2.2 software permeability module using the extended Tofts model. Semiquantitative parameters were calculated on a pixel-by-pixel basis. Spearman rank correlation tests were used for assessment of correlation between parameters. A p value ≤0.05 was considered statistically significant.


Strong positive correlations were found between mean peak enhancement and mean K trans: 0.79 (all patients, p<0.0001), 0.83 (primary staging, p = 0.003), and 0.81 (restaging, p = 0.054). Mean wash-in correlated significantly with mean V p and K ep (0.79 and 0.58, respectively, p<0.0001 and p = 0.009) in all patients. Mean wash-in showed a significant correlation with mean K ep (0.67, p = 0.033) in the primary staging group. On the restaging MRI, mean wash-in only strongly correlated with mean V p (0.81, p = 0.054).


This study shows a strong correlation between quantitative and semiquantitative parameters in DCE-MRI for rectal cancer. Peak enhancement correlates strongly with K trans and wash-in showed strong correlation with V p and K ep. These parameters have been reported to predict tumor aggressiveness and response in rectal cancer. Therefore, semiquantitative analyses might be a surrogate for quantitative analyses.


Rectal cancer Dynamic contrast-enhanced MRI Correlation Semiquantitative Quantitative 


Compliance with ethical standards


No funding was received for this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Tong T, Sun Y, Gollub MJ, et al. (2015) Dynamic contrast-enhanced MRI: use in predicting pathological complete response to neoadjuvant chemoradiation in locally advanced rectal cancer. J Magn Reson Imaging 42(3):673–680. doi: 10.1002/jmri.24835 CrossRefPubMedGoogle Scholar
  2. 2.
    Padhani AR, Khan AA (2010) Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for monitoring anticancer therapy. Target Oncol 5(1):39–52. doi: 10.1007/s11523-010-0135-8 CrossRefPubMedGoogle Scholar
  3. 3.
    Tofts PS (1997) Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 7(1):91–101CrossRefGoogle Scholar
  4. 4.
    Tofts PS, Kermode AG (1991) Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 17(2):357–367CrossRefGoogle Scholar
  5. 5.
    Lollert A, Junginger T, Schimanski CC, et al. (2014) Rectal cancer: dynamic contrast-enhanced MRI correlates with lymph node status and epidermal growth factor receptor expression. J Magn Reson Imaging 39(6):1436–1442. doi: 10.1002/jmri.24301 CrossRefPubMedGoogle Scholar
  6. 6.
    Hong HS, Kim SH, Park HJ, et al. (2013) Correlations of dynamic contrast-enhanced magnetic resonance imaging with morphologic, angiogenic, and molecular prognostic factors in rectal cancer. Yonsei Med J 54(1):123–130. doi: 10.3349/ymj.2013.54.1.123 CrossRefPubMedGoogle Scholar
  7. 7.
    Zhang XM, Yu D, Zhang HL, et al. (2008) 3D dynamic contrast-enhanced MRI of rectal carcinoma at 3T: correlation with microvascular density and vascular endothelial growth factor markers of tumor angiogenesis. J Magn Reson Imaging 27(6):1309–1316. doi: 10.1002/jmri.21378 CrossRefPubMedGoogle Scholar
  8. 8.
    Woolf DK, Padhani AR, Taylor NJ, et al. (2014) Assessing response in breast cancer with dynamic contrast-enhanced magnetic resonance imaging: are signal intensity-time curves adequate? Breast Cancer Res Treat 147(2):335–343. doi: 10.1007/s10549-014-3072-x CrossRefPubMedGoogle Scholar
  9. 9.
    Padhani AR, Leach MO (2005) Antivascular cancer treatments: functional assessments by dynamic contrast-enhanced magnetic resonance imaging. Abdom Imaging 30(3):324–341. doi: 10.1007/s00261-004-0265-5 CrossRefPubMedGoogle Scholar
  10. 10.
    Kuhl CK, Mielcareck P, Klaschik S, et al. (1999) Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 211(1):101–110. doi: 10.1148/radiology.211.1.r99ap38101 CrossRefPubMedGoogle Scholar
  11. 11.
    Orel SG (1999) Differentiating benign from malignant enhancing lesions identified at MR imaging of the breast: are time-signal intensity curves an accurate predictor? Radiology 211(1):5–7. doi: 10.1148/radiology.211.1.r99ap395 CrossRefPubMedGoogle Scholar
  12. 12.
    Renz DM, Diekmann F, Schmitzberger FF, et al. (2013) Pharmacokinetic approach for dynamic breast MRI to indicate signal intensity time curves of benign and malignant lesions by using the tumor flow residence time. Investig Radiol 48(2):69–78. doi: 10.1097/RLI.0b013e31827d29cf CrossRefGoogle Scholar
  13. 13.
    Hauth EA, Jaeger H, Maderwald S, et al. (2006) Evaluation of quantitative parametric analysis for characterization of breast lesions in contrast-enhanced MR mammography. Eur Radiol 16(12):2834–2841. doi: 10.1007/s00330-006-0348-5 CrossRefPubMedGoogle Scholar
  14. 14.
    Rosenkrantz AB, Sabach A, Babb JS, et al. (2013) Prostate cancer: comparison of dynamic contrast-enhanced MRI techniques for localization of peripheral zone tumor. AJR Am J Roentgenol 201(3):W471–W478. doi: 10.2214/AJR.12.9737 CrossRefPubMedGoogle Scholar
  15. 15.
    Huang B, Wong CS, Whitcher B, et al. (2013) Dynamic contrast-enhanced magnetic resonance imaging for characterising nasopharyngeal carcinoma: comparison of semiquantitative and quantitative parameters and correlation with tumour stage. Eur Radiol 23(6):1495–1502. doi: 10.1007/s00330-012-2740-7 CrossRefPubMedGoogle Scholar
  16. 16.
    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(3):766–773. doi: 10.1016/j.ijrobp.2008.08.023 CrossRefPubMedGoogle Scholar
  17. 17.
    Lambregts DM, Beets GL, Maas M, et al. (2011) Accuracy of gadofosveset-enhanced MRI for nodal staging and restaging in rectal cancer. Ann Surg 253(3):539–545. doi: 10.1097/SLA.0b013e31820b01f1 CrossRefPubMedGoogle Scholar
  18. 18.
    Sourbron SP, Buckley DL (2013) Classic models for dynamic contrast-enhanced MRI. NMR Biomed 26(8):1004–1027. doi: 10.1002/nbm.2940 CrossRefPubMedGoogle Scholar
  19. 19.
    Kim SH, Lee JM, Gupta SN, Han JK, Choi BI (2014) Dynamic contrast-enhanced MRI to evaluate the therapeutic response to neoadjuvant chemoradiation therapy in locally advanced rectal cancer. J Magn Reson Imaging 40(3):730–737. doi: 10.1002/jmri.24387 CrossRefPubMedGoogle Scholar
  20. 20.
    Intven M, Reerink O, Philippens ME (2015) Dynamic contrast enhanced MR imaging for rectal cancer response assessment after neo-adjuvant chemoradiation. J Magn Reson Imaging 41(6):1646–1653. doi: 10.1002/jmri.24718 CrossRefPubMedGoogle Scholar
  21. 21.
    George ML, Dzik-Jurasz AS, Padhani AR, et al. (2001) Non-invasive methods of assessing angiogenesis and their value in predicting response to treatment in colorectal cancer. Br J Surg 88(12):1628–1636CrossRefGoogle Scholar
  22. 22.
    Yeo DM, Oh SN, Jung CK, et al. (2015) Correlation of dynamic contrast-enhanced MRI perfusion parameters with angiogenesis and biologic aggressiveness of rectal cancer: preliminary results. J Magn Reson Imaging 41(2):474–480. doi: 10.1002/jmri.24541 CrossRefPubMedGoogle Scholar
  23. 23.
    Chwang WB, Jain R, Bagher-Ebadian H, et al. (2014) Measurement of rat brain tumor kinetics using an intravascular MR contrast agent and DCE-MRI nested model selection. J Magn Reson Imaging 40(5):1223–1229. doi: 10.1002/jmri.24469 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Martens MH, Subhani S, Heijnen LA, et al. (2015) Can perfusion MRI predict response to preoperative treatment in rectal cancer? Radiother Oncol 114(2):218–223. doi: 10.1016/j.radonc.2014.11.044 CrossRefPubMedGoogle Scholar
  25. 25.
    Petrillo A, Fusco R, Petrillo M, et al. (2015) Standardized Index of Shape (SIS): a quantitative DCE-MRI parameter to discriminate responders by non-responders after neoadjuvant therapy in LARC. Eur Radiol 25(7):1935–1945. doi: 10.1007/s00330-014-3581-3 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Rebecca A. P. Dijkhoff
    • 1
  • Monique Maas
    • 2
    Email author
  • Milou H. Martens
    • 3
  • Nikolaos Papanikolaou
    • 4
  • Doenja M. J. Lambregts
    • 2
  • Geerard L. Beets
    • 5
  • Regina G. H. Beets-Tan
    • 2
  1. 1.Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
  2. 2.Department of RadiologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
  3. 3.Department of SurgeryZuyderland Medical CentreSittardThe Netherlands
  4. 4.Division for Medical Imaging and Technology, Institute for Clinical Science, Intervention and Technology (CLINTEC)Karolinska InstitutetStockholmSweden
  5. 5.Department of SurgeryThe Netherlands Cancer InstituteAmsterdamThe Netherlands

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