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?

ObjectiveTo find out any correlation between dynamic contrast-enhanced (DCE) model-based parameters and model-free parameters, and evaluate correlations between perfusion parameters with histologic prognostic factors.MethodsModel-based parameters (Ktrans, Kep and Ve) of 102 invasive ductal carcinomas were obtained using DCE-MRI and post-processing software. Correlations between model-based and model-free parameters and between perfusion parameters and histologic prognostic factors were analysed.ResultsMean Kep was significantly higher in cancers showing initial rapid enhancement (P = 0.002) and a delayed washout pattern (P = 0.001). Ve was significantly lower in cancers showing a delayed washout pattern (P = 0.015). Kep significantly correlated with time to peak enhancement (TTP) (ρ = −0.33, P < 0.001) and washout slope (ρ = 0.39, P = 0.002). Ve was significantly correlated with TTP (ρ = 0.33, P = 0.002). Mean Kep was higher in tumours with high nuclear grade (P = 0.017). Mean Ve was lower in tumours with high histologic grade (P = 0.005) and in tumours with negative oestrogen receptor status (P = 0.047). TTP was shorter in tumours with negative oestrogen receptor status (P = 0.037).ConclusionsWe could acquire general information about the tumour vascular physiology, interstitial space volume and pathologic prognostic factors by analyzing time-signal intensity curve without a complicated acquisition process for the model-based parameters.Key points• Kep mainly affected the initial and delayed curve pattern in time–signal intensity curve.• There is significant correlation between model-based and model-free parameters.• We acquired information about tumour vascular physiology, interstitial space volume and prognostic factors.

[1]  Edward V R Dibella,et al.  The effect of temporal sampling on quantitative pharmacokinetic and three-time-point analysis of breast DCE-MRI. , 2012, Magnetic resonance imaging.

[2]  A. Jackson,et al.  Comparative study into the robustness of compartmental modeling and model‐free analysis in DCE‐MRI studies , 2006, Journal of magnetic resonance imaging : JMRI.

[3]  W. Han,et al.  Survival outcomes of breast cancer patients who receive neoadjuvant chemotherapy: association with dynamic contrast-enhanced MR imaging with computer-aided evaluation. , 2013, Radiology.

[4]  Vlayka Liotcheva,et al.  DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: A pilot study , 2009, International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group.

[5]  A Radjenovic,et al.  Measurement of pharmacokinetic parameters in histologically graded invasive breast tumours using dynamic contrast-enhanced MRI. , 2008, The British journal of radiology.

[6]  M. Su,et al.  Inflammatory Breast Cancer: Dynamic Contrast-enhanced MR in Patients Receiving Bevacizumab—Initial Experience , 2008 .

[7]  James H Thrall,et al.  Imaging angiogenesis: applications and potential for drug development. , 2005, Journal of the National Cancer Institute.

[8]  Karel G M Moons,et al.  Meta-analysis of MR imaging in the diagnosis of breast lesions. , 2008, Radiology.

[9]  W. Kaiser,et al.  Assessing the degree of collinearity among the lesion features of the MRI BI-RADS lexicon. , 2011, European journal of radiology.

[10]  Xia Li,et al.  DCE‐MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings , 2014, Magnetic resonance in medicine.

[11]  D. Collins,et al.  Primary human breast adenocarcinoma: imaging and histologic correlates of intrinsic susceptibility-weighted MR imaging before and during chemotherapy. , 2010, Radiology.

[12]  P. Tofts Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[13]  D. Bluemke,et al.  3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis. , 2011, AJR. American journal of roentgenology.

[14]  C. Boetes,et al.  Contrast-enhanced magnetic resonance imaging of the breast: the value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in classifying lesions , 2008, European Radiology.

[15]  Thomas E Yankeelov,et al.  Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples. , 2007, Current medical imaging reviews.

[16]  Michael V Knopp,et al.  Functional magnetic resonance imaging in oncology for diagnosis and therapy monitoring. , 2003, Molecular cancer therapeutics.

[17]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[18]  Edna Schechtman,et al.  Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions , 2005, Cancer.

[19]  D. Collins,et al.  Vascular characterisation of triple negative breast carcinomas using dynamic MRI , 2011, European Radiology.

[20]  Werner A. Kaiser,et al.  Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer , 2010, European Radiology.

[21]  Jun Li,et al.  Quantitative analysis of clinical dynamic contrast-enhanced MR imaging for evaluating treatment response in human breast cancer. , 2010, Radiology.

[22]  W. Moon,et al.  Correlation of perfusion parameters on dynamic contrast‐enhanced MRI with prognostic factors and subtypes of breast cancers , 2012, Journal of magnetic resonance imaging : JMRI.

[23]  C. Kuhl,et al.  Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? , 1999, Radiology.