Literature DB >> 23383694

A comparison of dynamic contrast-enhanced CT and MR imaging-derived measurements in patients with cervical cancer.

Sun Mo Kim1, Masoom A Haider, Michael Milosevic, Ivan W T Yeung.   

Abstract

This work is to compare the kinetic parameters derived from the DCE-CT and -MR data of a group of 37 patients with cervical cancer. The modified Tofts model and the reference tissue method were applied to estimate kinetic parameters. In the MR kinetic analyses using the modified Tofts model for each patient data set, both the arterial input function (AIF) measured from DCE-MR images and a population-averaged AIF from the literature were applied to the analyses, while the measured AIF was used for the CT kinetic analysis. The kinetic parameters obtained from both modalities were compared. Significant moderate correlations were found in modified Tofts parameters [volume transfer constant(K(trans) ) and rate constant (k(ep) )] between CT and MR analysis for MR with the measured AIFs (R = 0·45, P<0·01 and R = 0·40, P<0·01 in high-K(trans) region; R = 0·38, P<0·01 and R = 0·80, P<0·01 in low-K(trans) region) as well as with the population-averaged AIF (R = 0·59, P<0·01 and R = 0·62, P<0·01 in high-K(trans) region; R = 0·50, P<0·01 and R = 0·63, P<0·01 in low-K(trans) region), respectively. In addition, from the Bland-Altman plot analysis, it was found that the systematic biases (the mean difference) between the modalities were drastically reduced in magnitude by adopting the population-averaged AIF for the MR analysis instead of the measured ones (from 51·5% to 18·9% for K(trans) and from 21·7% to 4·1% for k(ep) in high-K(trans) region; from 73·0% to 29·4% for K(trans) and from 63·4% to 24·5% for k(ep) in low-K(trans) region). The preliminary results showed the feasibility in the interchangeable use of the two imaging modalities in assessing cervical cancers.
© 2012 The Authors Clinical Physiology and Functional Imaging © 2012 Scandinavian Society of Clinical Physiology and Nuclear Medicine.

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Year:  2012        PMID: 23383694     DOI: 10.1111/cpf.12010

Source DB:  PubMed          Journal:  Clin Physiol Funct Imaging        ISSN: 1475-0961            Impact factor:   2.273


  6 in total

1.  Comparison of arterial input functions measured from ultra-fast dynamic contrast enhanced MRI and dynamic contrast enhanced computed tomography in prostate cancer patients.

Authors:  Shiyang Wang; Zhengfeng Lu; Xiaobing Fan; Milica Medved; Xia Jiang; Steffen Sammet; Ambereen Yousuf; Federico Pineda; Aytekin Oto; Gregory S Karczmar
Journal:  Phys Med Biol       Date:  2018-01-30       Impact factor: 3.609

Review 2.  A physiological perspective on the use of imaging to assess the in vivo delivery of therapeutics.

Authors:  Shengping Qin; Brett Z Fite; M Karen J Gagnon; Jai W Seo; Fitz-Roy Curry; Frits Thorsen; Katherine W Ferrara
Journal:  Ann Biomed Eng       Date:  2013-09-10       Impact factor: 3.934

3.  Techniques and applications of dynamic contrast enhanced magnetic resonance imaging in cancer.

Authors:  Stephanie L Barnes; Jennifer G Whisenant; Thomas E Yankeelov
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2014

Review 4.  Hypoxia in cervical cancer: from biology to imaging.

Authors:  Heidi Lyng; Eirik Malinen
Journal:  Clin Transl Imaging       Date:  2017-07-10

5.  Evaluating differential nanoparticle accumulation and retention kinetics in a mouse model of traumatic brain injury via Ktrans mapping with MRI.

Authors:  Hunter A Miller; Alexander W Magsam; Aria W Tarudji; Svetlana Romanova; Laura Weber; Connor C Gee; Gary L Madsen; Tatiana K Bronich; Forrest M Kievit
Journal:  Sci Rep       Date:  2019-11-06       Impact factor: 4.379

6.  Comparison of Voxel-Wise Tumor Perfusion Changes Measured With Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients With Metastatic Brain Cancer Treated with Radiosurgery.

Authors:  Catherine Coolens; Brandon Driscoll; Warren Foltz; Carly Pellow; Cynthia Menard; Caroline Chung
Journal:  Tomography       Date:  2016-12
  6 in total

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