Literature DB >> 20950229

Magnetic resonance imaging of tumor necrosis.

Tormod A M Egeland1, Jon-Vidar Gaustad, Kanthi Galappathi, Einar K Rofstad.   

Abstract

BACKGROUND: The prognostic and predictive value of magnetic resonance (MR) investigations in clinical oncology may be improved by implementing strategies for discriminating between viable and necrotic tissue in tumors. The purpose of this preclinical study was to investigate whether the extent of necrosis in tumors can be assessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and/or T(2)-weighted MR imaging.
MATERIAL AND METHODS: Three amelanotic human melanoma xenograft lines differing substantially in tumor necrotic fraction, necrotic pattern, extracellular volume fraction, and blood perfusion were used as experimental models of human cancer. MRI was performed at 1.5 T and a spatial resolution of 0.23 × 0.47 × 2.0 mm(3). Gadolinium diethylene-triamine penta-acetic acid (Gd-DTPA) was used as contrast agent. Plots of Gd-DTPA concentration versus time were generated for each voxel, and three parameters were calculated for each curve: the extracellular volume fraction (ν(e)), the final slope (a), and the Gd-DTPA concentration at one minute after the contrast administration (C(1min)). Parametric images of ν(e), a, C(1min), and the signal intensity in T(2)-weighted images (SI(T2W)) were compared with the histology of the imaged tissue.
RESULTS: The ν(e), a, and C(1min) frequency distributions were significantly different for necrotic and viable tissue in all three tumor lines. By using adequate values of ν(e), a, and C(1min) to discriminate between necrotic and viable tissue, significant correlations were found between the fraction of necrotic tissue assessed by MRI and the fraction of necrotic tissue assessed by image analysis of histological preparations. On the other hand, the SI(T2W) frequency distributions did not differ significantly between necrotic and viable tissue in two of the three tumor lines.
CONCLUSION: Necrotic regions in tumor tissue can be identified in parametric images derived from DCE-MRI series, whereas T(2)-weighted images are unsuitable for detection of tumor necrosis.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20950229     DOI: 10.3109/0284186X.2010.526633

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  13 in total

Review 1.  Tumour-targeting bacteria engineered to fight cancer.

Authors:  Shibin Zhou; Claudia Gravekamp; David Bermudes; Ke Liu
Journal:  Nat Rev Cancer       Date:  2018-12       Impact factor: 60.716

2.  Retinoblastoma: value of dynamic contrast-enhanced MR imaging and correlation with tumor angiogenesis.

Authors:  F Rodjan; P de Graaf; P van der Valk; A C Moll; J P A Kuijer; D L Knol; J A Castelijns; P J W Pouwels
Journal:  AJNR Am J Neuroradiol       Date:  2012-05-24       Impact factor: 3.825

3.  Antitumor efficacy of 34.5ENVE: a transcriptionally retargeted and "Vstat120"-expressing oncolytic virus.

Authors:  Ji Young Yoo; Amy Haseley; Anna Bratasz; E Antonio Chiocca; Jianying Zhang; Kimerly Powell; Balveen Kaur
Journal:  Mol Ther       Date:  2011-10-25       Impact factor: 11.454

4.  Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity.

Authors:  SoHyun Han; Radka Stoyanova; Hansol Lee; Sean D Carlin; Jason A Koutcher; HyungJoon Cho; Ellen Ackerstaff
Journal:  Magn Reson Med       Date:  2017-07-20       Impact factor: 4.668

5.  A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results.

Authors:  Jacob U Fluckiger; Mary E Loveless; Stephanie L Barnes; Martin Lepage; Thomas E Yankeelov
Journal:  Phys Med Biol       Date:  2013-03-04       Impact factor: 3.609

6.  Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results.

Authors:  S H Han; E Ackerstaff; R Stoyanova; S Carlin; W Huang; J A Koutcher; J K Kim; G Cho; G Jang; H Cho
Journal:  NMR Biomed       Date:  2013-02-25       Impact factor: 4.044

7.  Use of an optimised enzyme/prodrug combination for Clostridia directed enzyme prodrug therapy induces a significant growth delay in necrotic tumours.

Authors:  Alexandra M Mowday; Ludwig J Dubois; Aleksandra M Kubiak; Jasmine V E Chan-Hyams; Christopher P Guise; Amir Ashoorzadeh; Philippe Lambin; David F Ackerley; Jeff B Smaill; Nigel P Minton; Jan Theys; Adam V Patterson
Journal:  Cancer Gene Ther       Date:  2021-02-08       Impact factor: 5.987

8.  DW-MRI in assessment of the hypoxic fraction, interstitial fluid pressure, and metastatic propensity of melanoma xenografts.

Authors:  Tord Hompland; Christine Ellingsen; Kanthi Galappathi; Einar K Rofstad
Journal:  BMC Cancer       Date:  2014-02-15       Impact factor: 4.430

Review 9.  Advancing Clostridia to Clinical Trial: Past Lessons and Recent Progress.

Authors:  Alexandra M Mowday; Christopher P Guise; David F Ackerley; Nigel P Minton; Philippe Lambin; Ludwig J Dubois; Jan Theys; Jeff B Smaill; Adam V Patterson
Journal:  Cancers (Basel)       Date:  2016-06-28       Impact factor: 6.639

10.  DCE-MRI of patient-derived xenograft models of uterine cervix carcinoma: associations with parameters of the tumor microenvironment.

Authors:  Anette Hauge; Catherine S Wegner; Jon-Vidar Gaustad; Trude G Simonsen; Lise Mari K Andersen; Einar K Rofstad
Journal:  J Transl Med       Date:  2017-11-03       Impact factor: 5.531

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.