Correlation of perfusion parameters on dynamic contrast‐enhanced MRI with prognostic factors and subtypes of breast cancers

To investigate whether a correlation exists between perfusion parameters obtained from dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) and prognostic factors or immunohistochemical subtypes of breast cancers.

[1]  J. Gore,et al.  A quantitative comparison of the influence of individual versus population‐derived vascular input functions on dynamic contrast enhanced‐MRI in small animals , 2012, Magnetic resonance in medicine.

[2]  Thomas E Yankeelov,et al.  A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer , 2011, Physics in medicine and biology.

[3]  Andreas Makris,et al.  Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy. , 2011, Radiology.

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

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

[6]  L. Turnbull,et al.  Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy. , 2009, European journal of radiology.

[7]  Brandon Whitcher,et al.  Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps , 2009, Magnetic resonance in medicine.

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

[9]  Andreas Makris,et al.  Early Changes in Functional Dynamic Magnetic Resonance Imaging Predict for Pathologic Response to Neoadjuvant Chemotherapy in Primary Breast Cancer , 2008, Clinical Cancer Research.

[10]  Jianhua Yao,et al.  Automatic Determination of Arterial Input Function for Dynamic Contrast Enhanced MRI in Tumor Assessment , 2008, MICCAI.

[11]  Teresa McShane,et al.  Scan‐rescan variability in perfusion assessment of tumors in MRI using both model and data‐derived arterial input functions , 2008, Journal of magnetic resonance imaging : JMRI.

[12]  Woo Kyung Moon,et al.  Index terms: Breast neoplasms Breast, MR , 2006 .

[13]  S. Steinberg,et al.  Inflammatory breast cancer: dynamic contrast-enhanced MR in patients receiving bevacizumab--initial experience. , 2007, Radiology.

[14]  A. Jackson,et al.  Experimentally‐derived functional form for a population‐averaged high‐temporal‐resolution arterial input function for dynamic contrast‐enhanced MRI , 2006, Magnetic resonance in medicine.

[15]  R. Hendrick,et al.  How to optimize clinical breast MR imaging practices and techniques on Your 1.5-T system. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[16]  H. Lehr,et al.  Dynamic MR imaging of breast lesions: correlation with microvessel distribution pattern and histologic characteristics of prognosis. , 2006, Radiology.

[17]  Megumi Jinguji,et al.  Rim enhancement of breast cancers on contrast-enhanced MR imaging: Relationship with prognostic factors , 2006, Breast cancer.

[18]  H. Schlemmer,et al.  Can pre-operative contrast-enhanced dynamic MR imaging for prostate cancer predict microvessel density in prostatectomy specimens? , 2004, European Radiology.

[19]  Orhan Nalcioglu,et al.  Correlation of dynamic contrast enhancement MRI parameters with microvessel density and VEGF for assessment of angiogenesis in breast cancer , 2003, Journal of magnetic resonance imaging : JMRI.

[20]  R. Zhou,et al.  MRI estimation of the arterial input function in mice. , 2003, Academic radiology.

[21]  Tibor Tot,et al.  Invasive breast cancer: correlation of dynamic MR features with prognostic factors , 2003, European Radiology.

[22]  A. Padhani,et al.  Reproducibility of quantitative dynamic MRI of normal human tissues , 2002, NMR in biomedicine.

[23]  M D Schnall,et al.  MR imaging of the breast for the detection, diagnosis, and staging of breast cancer. , 2001, Radiology.

[24]  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.

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

[26]  A Horsman,et al.  Dynamic MR imaging of invasive breast cancer: correlation with tumour grade and other histological factors. , 1997, The British journal of radiology.

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

[28]  A. Schauer,et al.  Prognostic value of contrast-enhanced MR mammography in patients with breast cancer , 1997, European Radiology.

[29]  E. Rostrup,et al.  Measurement of the arterial concentration of Gd‐DTPA using MRI: A step toward quantitative perfusion imaging , 1996, Magnetic resonance in medicine.

[30]  D M Shames,et al.  Mammary carcinoma model: correlation of macromolecular contrast-enhanced MR imaging characterizations of tumor microvasculature and histologic capillary density. , 1996, Radiology.

[31]  S. Edge,et al.  Suspect breast lesions: findings at dynamic gadolinium-enhanced MR imaging correlated with mammographic and pathologic features. , 1995, Radiology.

[32]  H. Dvorak,et al.  Vascular permeability factor/vascular endothelial growth factor, microvascular hyperpermeability, and angiogenesis. , 1995, The American journal of pathology.