Literature DB >> 18070824

Measurement of pharmacokinetic parameters in histologically graded invasive breast tumours using dynamic contrast-enhanced MRI.

A Radjenovic1, B J Dall, J P Ridgway, M A Smith.   

Abstract

Dynamic contrast-enhanced MRI (DCE-MRI) has demonstrated high sensitivity for detection of breast cancer. Analysis of correlation between quantitative DCE-MRI findings and prognostic factors (such as histological tumour grade) is important for defining the role of this technique in the diagnosis of breast cancer as well as the monitoring of neoadjuvant therapies. This paper presents a practical clinical application of a quantitative pharmacokinetic model to study histologically confirmed and graded invasive human breast tumours. The hypothesis is that, given a documented difference in capillary permeability between benign and malignant breast tumours, a relationship between permeability-related DCE-MRI parameters and tumour aggressiveness persists within invasive breast carcinomas. In addition, it was hypothesized that pharmacokinetic parameters may demonstrate stronger correlation with prognostic factors than the more conventional black-box techniques, so a comparison was undertaken. Significant correlations were found between pharmacokinetic and black-box parameters in 59 invasive breast carcinomas. However, statistically significant variation with tumour grade was demonstrated in only two permeability-related pharmacokinetic parameters: k(ep) (p<0.05) and K(trans) (p<0.05), using one-way analysis of variance. Parameters k(ep) and K(trans) were significantly higher in Grade 3 tumours than in low-grade tumours. None of the measured DCE-MRI parameters varied significantly between Grade 1 and Grade 2 tumours. Measurement of k(ep) and K(trans) might therefore be used to monitor the effectiveness of neoadjuvant treatment of high-grade invasive breast carcinomas, but is unlikely to demonstrate remission in low-grade tumours.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 18070824     DOI: 10.1259/bjr/98435332

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  13 in total

1.  3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis.

Authors:  Riham H El Khouli; Katarzyna J Macura; Ihab R Kamel; Michael A Jacobs; David A Bluemke
Journal:  AJR Am J Roentgenol       Date:  2011-12       Impact factor: 3.959

2.  Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm.

Authors:  J Jayender; K G Vosburgh; E Gombos; A Ashraf; D Kontos; S C Gavenonis; F A Jolesz; K Pohl
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2012-07-12

3.  Pharmacokinetic mapping for lesion classification in dynamic breast MRI.

Authors:  Matthias C Schabel; Glen R Morrell; Karen Y Oh; Cheryl A Walczak; R Brad Barlow; Leigh A Neumayer
Journal:  J Magn Reson Imaging       Date:  2010-06       Impact factor: 4.813

Review 4.  Magnetic resonance in the era of molecular imaging of cancer.

Authors:  John C Gore; H Charles Manning; C Chad Quarles; Kevin W Waddell; Thomas E Yankeelov
Journal:  Magn Reson Imaging       Date:  2011-04-27       Impact factor: 2.546

5.  Use of a reference tissue and blood vessel to measure the arterial input function in DCEMRI.

Authors:  Xiaobing Fan; Chad R Haney; Devkumar Mustafi; Cheng Yang; Marta Zamora; Erica J Markiewicz; Gregory S Karczmar
Journal:  Magn Reson Med       Date:  2010-12       Impact factor: 4.668

6.  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?

Authors:  Boram Yi; Doo Kyoung Kang; Dukyong Yoon; Yong Sik Jung; Ku Sang Kim; Hyunee Yim; Tae Hee Kim
Journal:  Eur Radiol       Date:  2014-02-21       Impact factor: 5.315

7.  Statistical Learning Algorithm for in situ and invasive breast carcinoma segmentation.

Authors:  Jagadeesan Jayender; Eva Gombos; Sona Chikarmane; Donnette Dabydeen; Ferenc A Jolesz; Kirby G Vosburgh
Journal:  Comput Med Imaging Graph       Date:  2013-05-19       Impact factor: 4.790

8.  Inhibition of SUR1 decreases the vascular permeability of cerebral metastases.

Authors:  Eric M Thompson; Gregory L Pishko; Leslie L Muldoon; Edward A Neuwelt
Journal:  Neoplasia       Date:  2013-05       Impact factor: 5.715

9.  Temporal sampling requirements for reference region modeling of DCE-MRI data in human breast cancer.

Authors:  Catherine R Planey; E Brian Welch; Lei Xu; A Bapsi Chakravarthy; J Christopher Gatenby; Darla Freehardt; Ingrid Mayer; Ingrid Meszeoly; Mark Kelley; Julie Means-Powell; John C Gore; Thomas E Yankeelov
Journal:  J Magn Reson Imaging       Date:  2009-07       Impact factor: 4.813

10.  Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations.

Authors:  Jacob U Fluckiger; Xia Li; Jennifer G Whisenant; Todd E Peterson; John C Gore; Thomas E Yankeelov
Journal:  Int J Biomed Imaging       Date:  2013-10-03
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.