"skipMainNavigation"
closeDrawerMenuopenDrawerMenu
Search
Quick Search in Journals
Quick Search anywhere
Advanced Search
Radiology
Menu
  • Information
    • Previous
    Next
    Original Research

    Quantitative Analysis of Clinical Dynamic Contrast-enhanced MR Imaging for Evaluating Treatment Response in Human Breast Cancer

    Published Online:Oct 1 2010https://doi.org/10.1148/radiol.10092169

    Abstract

    A quantitative dynamic contrast-enhanced MR imaging method has been developed to assess treatment response to neoadjuvant chemotherapy in patients with breast cancer by using standard clinical dynamic contrast-enhanced MR imaging data without measuring either arterial input function or baseline T1.

    Purpose

    To develop a method that combines a fixed-T1, fuzzy c-means (FCM) technique with a reference region (RR) model (T1-FCM method) to estimate pharmacokinetic parameters without measuring the arterial input function or baseline T1, or T1(0), and to demonstrate its feasibility in the assessment of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer by using data from dynamic contrast material–enhanced magnetic resonance (MR) imaging.

    Materials and Methods

    This study was approved by the human investigation committees of the two participating institutions. All patients gave written informed consent. A conventional dual-flip-angle gradient-echo method was used to evaluate the effects of noise and the T1 in the tissue itself on the accuracy of T1 estimation. Both conventional RR and fixed-T1 methods were used to evaluate the effects of noise and preselected T1(0) on the estimation of pharmacokinetic parameters by means of a simulation study. Thirty-three women (age range, 32–66 years; mean age, 45 years) with pathologically proved breast tumors were examined to evaluate the feasibility of using the T1-FCM method as a means of assessing treatment response to NAC. A nonparametric Mann-Whitney U test was used to assess the difference in each of the MR imaging parameters between patients with a major histologic response to treatment and those with a nonmajor histologic response.

    Results

    With use of the dual-flip-angle method, the accuracy and distribution of T1 estimation are dependent on the T1 in the tissue itself. The T1-FCM method is more accurate than other methods and is relatively insensitive to the effects of noise and incorrect T1(0) selection. Preliminary clinical data revealed a significant difference (P < .01) in the change of the volume transfer constant after two cycles of NAC between the major and nonmajor histologic response groups.

    Conclusion

    Results of the simulation study demonstrate that the T1-FCM method appears to be relatively insensitive to noisy dynamic contrast-enhanced MR imaging data. This method could prove useful in the evaluation of breast cancer therapy.

    © RSNA, 2010

    References

    • 1 Fisher B, Bryant J, Wolmark N, et al.. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J Clin Oncol 1998;16(8):2672–2685. Crossref, Medline, Google Scholar
    • 2 Machiavelli MR, Romero AO, Pérez JE, et al.. Prognostic significance of pathological response of primary tumor and metastatic axillary lymph nodes after neoadjuvant chemotherapy for locally advanced breast carcinoma. Cancer J Sci Am 1998;4(2):125–131. Medline, Google Scholar
    • 3 Padhani AR. Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging 2002;16(4):407–422. Crossref, Medline, Google Scholar
    • 4 Padhani AR, Hayes C, Assersohn L, et al.. Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results. Radiology 2006;239(2):361–374. Link, Google Scholar
    • 5 Tofts PS, Brix G, Buckley DL, et al.. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999;10(3):223–232. Crossref, Medline, Google Scholar
    • 6 Yankeelov TE, Gore JC. Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples. Curr Med Imaging Rev 2009;3(2):91–107. Crossref, Medline, Google Scholar
    • 7 Yankeelov TE, Luci JJ, Lepage M, et al.. Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magn Reson Imaging 2005;23(4):519–529. Crossref, Medline, Google Scholar
    • 8 Kovar DA, Lewis M, Karczmar GS. A new method for imaging perfusion and contrast extraction fraction: input functions derived from reference tissues. J Magn Reson Imaging 1998;8(5):1126–1134. Crossref, Medline, Google Scholar
    • 9 Yang C, Karczmar GS, Medved M, Stadler WM. Estimating the arterial input function using two reference tissues in dynamic contrast-enhanced MRI studies: fundamental concepts and simulations. Magn Reson Med 2004;52(5):1110–1117. Crossref, Medline, Google Scholar
    • 10 Walker-Samuel S, Leach MO, Collins DJ. Reference tissue quantification of DCE-MRI data without a contrast agent calibration. Phys Med Biol 2007;52(3):589–601. Crossref, Medline, Google Scholar
    • 11 Yang C, Karczmar GS, Medved M, Stadler WM. Multiple reference tissue method for contrast agent arterial input function estimation. Magn Reson Med 2007;58(6):1266–1275. Crossref, Medline, Google Scholar
    • 12 Haacke EM, Filleti CL, Gattu R, et al.. New algorithm for quantifying vascular changes in dynamic contrast-enhanced MRI independent of absolute T1 values. Magn Reson Med 2007;58(3):463–472. Crossref, Medline, Google Scholar
    • 13 Guo JY, Reddick WE, Rosen MA, Song HK. Dynamic contrast-enhanced magnetic resonance imaging parameters independent of baseline T10 values. Magn Reson Imaging 2009;27(9):1208–1215. Crossref, Medline, Google Scholar
    • 14 Huang W, Wang Y, Panicek DM, Schwartz LH, Koutcher JA. Feasibility of using limited-population-based average R10 for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced magnetic resonance imaging data. Magn Reson Imaging 2009;27(6):852–858. Crossref, Medline, Google Scholar
    • 15 Chen WJ, Giger ML, Bick U, Newstead GM. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys 2006;33(8):2878–2887. Crossref, Medline, Google Scholar
    • 16 Chen WJ, Giger ML, Bick U. A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol 2006;13(1):63–72. Crossref, Medline, Google Scholar
    • 17 Haase A. Snapshot FLASH MRI: applications to T1, T2, and chemical-shift imaging. Magn Reson Med 1990;13(1):77–89. Crossref, Medline, Google Scholar
    • 18 Bottomley PA, Hardy CJ, Argersinger RE, Allen-Moore G. A review of 1H nuclear magnetic resonance relaxation in pathology: are T1 and T2 diagnostic? Med Phys 1987;14(1):1–37. Crossref, Medline, Google Scholar
    • 19 Benjaminsen IC, Graff BA, Brurberg KG, Rofstad EK. Assessment of tumor blood perfusion by high-resolution dynamic contrast-enhanced MRI: a preclinical study of human melanoma xenografts. Magn Reson Med 2004;52(2):269–276. Crossref, Medline, Google Scholar
    • 20 Walker-Samuel S, Parker CC, Leach MO, Collins DJ. Reproducibility of reference tissue quantification of dynamic contrast-enhanced data: comparison with a fixed vascular input function. Phys Med Biol 2007;52(1):75–89. Crossref, Medline, Google Scholar
    • 21 Deoni SCL, Rutt BK, Peters TM. Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn Reson Med 2003;49(3):515–526. Crossref, Medline, Google Scholar
    • 22 Schabel MC, Morrell GR. Uncertainty in T(1) mapping using the variable flip angle method with two flip angles. Phys Med Biol 2009;54(1):N1–N8. Crossref, Medline, Google Scholar
    • 23 Ogston KN, Miller ID, Payne S, et al.. A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival. Breast 2003;12(5):320–327. Crossref, Medline, Google Scholar
    • 24 van der Hage JA, van de Velde CJ, Julien JP, Tubiana-Hulin M, Vandervelden C, Duchateau L. Preoperative chemotherapy in primary operable breast cancer: results from the European Organization for Research and Treatment of Cancer Trial 10902. J Clin Oncol 2001;19(22):4224–4237. Crossref, Medline, Google Scholar
    • 25 Martincich L, Montemurro F, De Rosa G, et al.. Monitoring response to primary chemotherapy in breast cancer using dynamic contrast-enhanced magnetic resonance imaging. Breast Cancer Res Treat 2004;83(1):67–76. Crossref, Medline, Google Scholar
    • 26 Roberts C, Issa B, Stone A, Jackson A, Waterton JC, Parker GJ. Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies. J Magn Reson Imaging 2006;23(4):554–563. Crossref, Medline, Google Scholar
    • 27 Rakow-Penner R, Daniel B, Yu H, Sawyer-Glover A, Glover GH. Relaxation times of breast tissue at 1.5T and 3T measured using IDEAL. J Magn Reson Imaging 2006;23(1):87–91. Crossref, Medline, Google Scholar
    • 28 Merchant TE, Obertop H, de Graaf PW. Advantages of magnetic resonance imaging in breast surgery treatment planning. Breast Cancer Res Treat 1993;25(3):257–264. Crossref, Medline, Google Scholar

    Article History

    Received November 18, 2009; revision requested January 12, 2010; revision received April 1; accepted April 19; final version accepted May 19.
    Published online: Oct 2010
    Published in print: Oct 2010