Literature DB >> 26854494

Dynamic contrast-enhanced MRI for oncology drug development.

Yu Sub Sung1,2, Bumwoo Park1,2, Yoonseok Choi2, Hyeong-Seok Lim2,3, Dong-Cheol Woo1,2, Kyung Won Kim1,2, Jeong Kon Kim1,2.   

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising tool for evaluating tumor vascularity, as it can provide vasculature-derived, functional, and quantitative parameters. To implement DCE-MRI parameters as biomarkers for monitoring the effect of antiangiogenic or vascular-disrupting treatment, two crucial elements of surrogate endpoint, ie, validation and qualification, should be satisfied. Although early studies have shown the accuracy and reliability of DCE-MRI parameters for evaluating treatment-driven vascular alterations, there have been an increasing number of studies demonstrating the limitations of DCE-MRI parameters as surrogate endpoints. Therefore, in order to improve the application of DCE-MRI parameters in drug development, it is necessary to establish a standardized evaluation method and to determine the correct therapeutics-oriented meaning of individual DCE-MRI parameter. In this regard, this article describes the biophysical background and data acquisition/analysis techniques of DCE-MRI while focusing on the validation and qualification issues. Specifically, the causes of disagreement and confusion encountered in the preclinical and clinical trials using DCE-MRI are presented in detail. Finally, considering these limitations, we present potential strategies to optimize implementation of DCE-MRI. J. Magn. Reson. Imaging 2016;44:251-264.
© 2016 Wiley Periodicals, Inc.

Entities:  

Keywords:  DCE-MRI; drug development; qualification; validation

Mesh:

Substances:

Year:  2016        PMID: 26854494     DOI: 10.1002/jmri.25173

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  14 in total

1.  Magnetic Resonance Imaging for Drug Development.

Authors:  Jeong Kon Kim
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

2.  A Multicenter Phase II Study of Second-Line Axitinib for Patients with Advanced Hepatocellular Carcinoma Failing First-Line Sorafenib Monotherapy.

Authors:  Zhong-Zhe Lin; Bang-Bin Chen; Yi-Ping Hung; Po-Hsiang Huang; Ying-Chun Shen; Yu-Yun Shao; Chih-Hung Hsu; Ann-Lii Cheng; Rheun-Chuan Lee; Yee Chao; Chiun Hsu
Journal:  Oncologist       Date:  2020-04-09

3.  Portable perfusion phantom for quantitative DCE-MRI of the abdomen.

Authors:  Harrison Kim; Mina Mousa; Patrick Schexnailder; Robert Hergenrother; Mark Bolding; Bernard Ntsikoussalabongui; Vinoy Thomas; Desiree E Morgan
Journal:  Med Phys       Date:  2017-08-12       Impact factor: 4.071

4.  Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI.

Authors:  Sharon Peled; Mark Vangel; Ron Kikinis; Clare M Tempany; Fiona M Fennessy; Andrey Fedorov
Journal:  Acad Radiol       Date:  2018-11-20       Impact factor: 3.173

5.  Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.

Authors:  Evan D H Gates; Jonathan S Lin; Jeffrey S Weinberg; Jackson Hamilton; Sujit S Prabhu; John D Hazle; Gregory N Fuller; Veera Baladandayuthapani; David Fuentes; Dawid Schellingerhout
Journal:  Neuro Oncol       Date:  2019-03-18       Impact factor: 12.300

6.  Fast magnetic resonance fingerprinting for dynamic contrast-enhanced studies in mice.

Authors:  Yuning Gu; Charlie Y Wang; Christian E Anderson; Yuchi Liu; He Hu; Mette L Johansen; Dan Ma; Yun Jiang; Ciro Ramos-Estebanez; Susann Brady-Kalnay; Mark A Griswold; Chris A Flask; Xin Yu
Journal:  Magn Reson Med       Date:  2018-05-09       Impact factor: 4.668

Review 7.  Medullary Thyroid Carcinoma: An Update on Imaging.

Authors:  Sergiy V Kushchayev; Yevgeniya S Kushchayeva; Sri Harsha Tella; Tetiana Glushko; Karel Pacak; Oleg M Teytelboym
Journal:  J Thyroid Res       Date:  2019-07-07

8.  Estimating Local Cellular Density in Glioma Using MR Imaging Data.

Authors:  E D H Gates; J S Weinberg; S S Prabhu; J S Lin; J Hamilton; J D Hazle; G N Fuller; V Baladandayuthapani; D T Fuentes; D Schellingerhout
Journal:  AJNR Am J Neuroradiol       Date:  2020-11-26       Impact factor: 3.825

9.  Influence of B1-Inhomogeneity on Pharmacokinetic Modeling of Dynamic Contrast-Enhanced MRI: A Simulation Study.

Authors:  Bumwoo Park; Byung Se Choi; Yu Sub Sung; Dong-Cheol Woo; Woo Hyun Shim; Kyung Won Kim; Yoon Seok Choi; Sang Joon Pae; Ji-Yeon Suh; Hyungjoon Cho; Jeong Kon Kim
Journal:  Korean J Radiol       Date:  2017-05-19       Impact factor: 3.500

Review 10.  Imaging biomarkers in oncology: Basics and application to MRI.

Authors:  Isabel Dregely; Davide Prezzi; Christian Kelly-Morland; Elisa Roccia; Radhouene Neji; Vicky Goh
Journal:  J Magn Reson Imaging       Date:  2018-07       Impact factor: 4.813

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