Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 8/2017

01-08-2017 | Neuro

Dynamic contrast-enhanced MR imaging in predicting progression of enhancing lesions persisting after standard treatment in glioblastoma patients: a prospective study

Authors: Roh-Eul Yoo, Seung Hong Choi, Tae Min Kim, Chul-Kee Park, Sung-Hye Park, Jae-Kyung Won, Il Han Kim, Soon Tae Lee, Hye Jeong Choi, Sung-Hye You, Koung Mi Kang, Tae Jin Yun, Ji-Hoon Kim, Chul-Ho Sohn

Published in: European Radiology | Issue 8/2017

Login to get access

Abstract

Objectives

To prospectively explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE–MRI) in predicting the progression of enhancing lesions persisting after standard treatment in patients with surgically resected glioblastoma (GBM).

Methods

Forty-seven GBM patients, who underwent near-total tumorectomy followed by concurrent chemoradiation therapy (CCRT) with temozolomide (TMZ) between May 2014 and February 2016, were enrolled. Twenty-four patients were finally analyzed for measurable enhancing lesions persisting after standard treatment. DCE-MRI parameters were calculated at enhancing lesions. Mann–Whitney U tests and multivariable stepwise logistic regression were used to compare parameters between progression (n = 16) and non-progression (n = 8) groups.

Results

Mean Ktrans and ve were significantly lower in progression than in non-progression (P = 0.037 and P = 0.037, respectively). The 5th percentile of the cumulative Ktrans histogram was also significantly lower in the progression than in non-progression group (P = 0.017). Mean ve was the only independent predictor of progression (P = 0.007), with a sensitivity of 100%, specificity of 63%, and an overall accuracy of 88% at a cut-off value of 0.873.

Conclusions

DCE-MRI may help predict the progression of enhancing lesions persisting after the completion of standard treatment in patients with surgically resected GBM, with mean ve serving as an independent predictor of progression.

Key points

• Enhancing lesions may persist after standard treatment in GBM patients.
• DCE-MRI may help predict the progression of the enhancing lesions.
• Mean K trans and v e were lower in progression than in non-progression group.
• DCE-MRI may help identify patients requiring close follow-up after standard treatment.
• DCE-MRI may help plan treatment strategies for GBM patients.
Literature
1.
Haroon HA, Buckley DL, Patankar TA et al (2004) A comparison of Ktrans measurements obtained with conventional and first pass pharmacokinetic models in human gliomas. J Magn Reson Imaging 19:527–536CrossRefPubMed
2.
Harrer JU, Parker GJ, Haroon HA et al (2004) Comparative study of methods for determining vascular permeability and blood volume in human gliomas. J Magn Reson Imaging 20:748–757CrossRefPubMed
3.
Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232CrossRefPubMed
4.
Jia Z, Geng D, Xie T, Zhang J, Liu Y (2012) Quantitative analysis of neovascular permeability in glioma by dynamic contrast-enhanced MR imaging. J Clin Neurosci 19:820–823CrossRefPubMed
5.
Jung SC, Yeom JA, Kim JH et al (2014) Glioma: Application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading. AJNR Am J Neuroradiol 35:1103–1110CrossRefPubMed
6.
Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996CrossRefPubMed
7.
Yun TJ, Park CK, Kim TM et al (2015) Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR imaging. Radiology 274:830–840CrossRefPubMed
8.
Kim JH, Choi SH, Ryoo I et al (2014) Prognosis prediction of measurable enhancing lesion after completion of standard concomitant chemoradiotherapy and adjuvant temozolomide in glioblastoma patients: application of dynamic susceptibility contrast perfusion and diffusion-weighted imaging. PLoS One 9, e113587CrossRefPubMedPubMedCentral
9.
Wen PY, Macdonald DR, Reardon DA et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972CrossRefPubMed
10.
Tofts PS, Kermode AG (1991) Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 17:357–367CrossRefPubMed
11.
Haacke EM, Filleti CL, Gattu R et al (2007) New algorithm for quantifying vascular changes in dynamic contrast-enhanced MRI independent of absolute T1 values. Magn Reson Med 58:463–472CrossRefPubMed
12.
Pluim JP, Maintz JB, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004CrossRefPubMed
13.
Sundar H, Shen D, Biros G, Xu C, Davatzikos C (2007) Robust computation of mutual information using spatially adaptive meshes. Med Image Comput Comput Assist Interv 10:950–958PubMed
14.
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefPubMed
15.
Hauck WW, Miike R (1991) A proposal for examining and reporting stepwise regressions. Stat Med 10:711–715CrossRefPubMed
16.
Abbott NJ, Patabendige AA, Dolman DE, Yusof SR, Begley DJ (2010) Structure and function of the blood-brain barrier. Neurobiol Dis 37:13–25CrossRefPubMed
17.
de Vries NA, Beijnen JH, Boogerd W, van Tellingen O (2006) Blood-brain barrier and chemotherapeutic treatment of brain tumors. Expert Rev Neurother 6:1199–1209CrossRefPubMed
18.
Dewhirst MW (1998) Concepts of oxygen transport at the microcirculatory level. Semin Radiat Oncol 8:143–150CrossRefPubMed
19.
Gulledge CJ, Dewhirst MW (1996) Tumor oxygenation: a matter of supply and demand. Anticancer Res 16:741–749PubMed
20.
Perini R, Choe R, Yodh AG, Sehgal C, Divgi CR, Rosen MA (2008) Non-invasive assessment of tumor neovasculature: techniques and clinical applications. Cancer Metastasis Rev 27:615–630CrossRefPubMed
21.
Pettersen EO, Ebbesen P, Gieling RG et al (2015) Targeting tumour hypoxia to prevent cancer metastasis. From biology, biosensing and technology to drug development: the METOXIA consortium. J Enzyme Inhib Med Chem 30:689–721CrossRefPubMed
22.
Barsoum IB, Smallwood CA, Siemens DR, Graham CH (2014) A mechanism of hypoxia-mediated escape from adaptive immunity in cancer cells. Cancer Res 74:665–674CrossRefPubMed
23.
Calcinotto A, Filipazzi P, Grioni M et al (2012) Modulation of microenvironment acidity reverses anergy in human and murine tumor-infiltrating T lymphocytes. Cancer Res 72:2746–2756CrossRefPubMed
24.
Casazza A, Di Conza G, Wenes M, Finisguerra V, Deschoemaeker S, Mazzone M (2014) Tumor stroma: a complexity dictated by the hypoxic tumor microenvironment. Oncogene 33:1743–1754CrossRefPubMed
25.
26.
27.
28.
Hanahan D, Coussens LM (2012) Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21:309–322CrossRefPubMed
29.
Keith B, Johnson RS, Simon MC (2012) HIF1alpha and HIF2alpha: sibling rivalry in hypoxic tumour growth and progression. Nat Rev Cancer 12:9–22
30.
31.
32.
Palazon A, Aragones J, Morales-Kastresana A, de Landazuri MO, Melero I (2012) Molecular pathways: hypoxia response in immune cells fighting or promoting cancer. Clin Cancer Res 18:1207–1213CrossRefPubMed
33.
Semenza GL (2014) Oxygen sensing, hypoxia-inducible factors, and disease pathophysiology. Annu Rev Pathol 9:47–71CrossRefPubMed
34.
35.
Kikuchi M, Yamane T, Shinohara S et al (2011) 18F-fluoromisonidazole positron emission tomography before treatment is a predictor of radiotherapy outcome and survival prognosis in patients with head and neck squamous cell carcinoma. Ann Nucl Med 25:625–633CrossRefPubMed
36.
Kobayashi H, Hirata K, Yamaguchi S, Terasaka S, Shiga T, Houkin K (2013) Usefulness of FMISO-PET for glioma analysis. Neurol Med Chir (Tokyo) 53:773–778CrossRef
37.
Krohn KA, Link JM, Mason RP (2008) Molecular imaging of hypoxia. J Nucl Med 49(Suppl 2):129S–148SCrossRefPubMed
38.
Rischin D, Hicks RJ, Fisher R et al (2006) Prognostic significance of [18F]-misonidazole positron emission tomography-detected tumor hypoxia in patients with advanced head and neck cancer randomly assigned to chemoradiation with or without tirapazamine: a substudy of Trans-Tasman Radiation Oncology Group Study 98.02. J Clin Oncol 24:2098–2104CrossRefPubMed
39.
Cooper RA, Carrington BM, Loncaster JA et al (2000) Tumour oxygenation levels correlate with dynamic contrast-enhanced magnetic resonance imaging parameters in carcinoma of the cervix. Radiother Oncol 57:53–59CrossRefPubMed
40.
Egeland TA, Gulliksrud K, Gaustad JV, Mathiesen B, Rofstad EK (2012) Dynamic contrast-enhanced-MRI of tumor hypoxia. Magn Reson Med 67:519–530CrossRefPubMed
41.
Jensen RL, Mumert ML, Gillespie DL, Kinney AY, Schabel MC, Salzman KL (2014) Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome. Neuro Oncol 16:280–291CrossRefPubMed
42.
Rajendran JG, Krohn KA (2015) F-18 fluoromisonidazole for imaging tumor hypoxia: imaging the microenvironment for personalized cancer therapy. Semin Nucl Med 45:151–162CrossRefPubMedPubMedCentral
43.
Cao VT, Jung TY, Jung S et al (2009) He correlation and prognostic significance of MGMT promoter methylation and MGMT protein in glioblastomas. Neurosurgery 65:866–875CrossRefPubMed
Metadata
Title
Dynamic contrast-enhanced MR imaging in predicting progression of enhancing lesions persisting after standard treatment in glioblastoma patients: a prospective study
Authors
Roh-Eul Yoo
Seung Hong Choi
Tae Min Kim
Chul-Kee Park
Sung-Hye Park
Jae-Kyung Won
Il Han Kim
Soon Tae Lee
Hye Jeong Choi
Sung-Hye You
Koung Mi Kang
Tae Jin Yun
Ji-Hoon Kim
Chul-Ho Sohn
Publication date
01-08-2017
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2017
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-016-4692-9

Other articles of this Issue 8/2017

European Radiology 8/2017 Go to the issue

Musculoskeletal

Extensor tendinopathy of the elbow assessed with sonoelastography: histologic correlation

Magnetic Resonance

Clinical impact of preoperative brain MR angiography and MR imaging in candidates for liver transplantation: a propensity score-matching study in a single institution

Ultrasound

Liver stiffness measurements with supersonic shear wave elastography in the diagnosis of biliary atresia: a comparative study with grey-scale US

Chest

Yield of chest X-ray tuberculosis screening of immigrants during the European refugee crisis of 2015: a single-centre experience

Emergency Radiology

Emergency assessment of patients with acute abdominal pain using low-dose CT with iterative reconstruction: a comparative study

Contrast Media

Gadolinium deposition in the brain: association with various GBCAs using a generalized additive model