Share：
Share this content in WeChat
X
[Chinese][PDF]100579
• Review •
Progress of functional MR imaging on the monitoring of therapeutic effects of high-grade gliomas
LIU Ye-qiu  YU Tao 

DOI:10.12015/issn.1674-8034.2018.02.014.


[Abstract] The current management of high-grade gliomas is complicated and changeable. The standard treatment for HGGs is resection and followed by radiotherapy and oral chemotherapy at present. However, the continuous cranial magnetic resonance imaging (MRI) that differentiates treatment response from treatment effect can be challenging and affects clinical decision-making in later stages. A variety of advanced MRI techniques such as diffusion weighted imaging, perfusion weighted imaging, MR spectroscopy, and multiparametric imaging incorporating novel physiologic and biochemical parameters might provide new methods to help characterize tumor progression, pseudoprogression, and pseudo response. In this review, the latest progresses and challenges regarding MR functional imaging in the area of monitoring the efficacy of HGGs are summarized.
[Keywords] Magnetic resonance imaging；Glioma；Diffusion weighted imaging；Diffusion tensor imaging；Perfusion weighted imaging

LIU Ye-qiu Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, China

YU Tao* Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, China

*Corresponding to: Yu T, E-mail: yutao@cancerhosp-ln-cmu.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Clinical Capability Construction Project of Liaoning Provincial Hospitals No.LNCCC-B06-2014
Received  2017-11-02
Accepted  2018-01-05
DOI: 10.12015/issn.1674-8034.2018.02.014
DOI:10.12015/issn.1674-8034.2018.02.014.

[1]
Oberoi RK, Parrish KE, Sio TT, et al. Strategies to improve delivery of anticancer drugs across the blood-brain barrier to treat glioblastoma. Neuro Oncol, 2016, 18(1): 27-36.
[2]
Tao J, Ying M, Ma W, et al. CGCG clinical practice guidelines for the management of adult diffuse gliomas. Cancer Lett, 2016, 375(2): 263-273.
[3]
Desouza RM, Shaweis H, Han C, et al. Has the survival of patients with glioblastoma changed over the years?. Br J Cancer, 2015, 114(2): 146-150.
[4]
Stupp R, Brada M, Mj VDB, et al. High-grade glioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2014, 25(Suppl 3): 93-101.
[5]
Knudsenbaas KM, Moen G, Fluge Ø, et al. Pseudoprogression in high-grade glioma. Acta Neurol Scand, 2013, 127(196): 31-37.
[6]
Young RJ, Gupta A, Shah AD, et al. Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma. Neurology, 2011, 76(22): 1918-1924.
[7]
Brandsma D, Stalpers L, Taal W, et al. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol, 2008, 9(5): 453-461.
[8]
Van West SE, De Bruin HG, Van DLB, et al. Incidence of pseudoprogression in low-grade gliomas treated with radiotherapy. Neuro Oncol, 2017, 19(5): 719-725.
[9]
Narayana A, Kelly P, Golfinos J, et al. Antiangiogenic therapy using bevacizumab in recurrent high-grade glioma: impact on local control and patient survival. J Neurosurg, 2009, 110(1): 173-180.
[10]
Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol, 2010, 28(11): 1963-1972.
[11]
Chinot OL, Macdonald DR, Abrey LE, et al. Response assessment criteria for glioblastoma: practical adaptation and implementation in clinical trials of antiangiogenic therapy. Curr Neurol Neurosci Rep, 2013, 13(5): 347.
[12]
Ellingson BM, Bendszus M, Boxerman J, et al. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol, 2015, 17(9): 1188-1198.
[13]
Hein PA, Eskey CJ, Dunn JF, et al. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol, 2004, 25(2): 201-209.
[14]
Asao C, Korogi Y, Kitajima M, et al. Diffusion-weighted imaging of radiation-induced brain Injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol, 2005, 26(6): 1455-1460.
[15]
Song YS, Hong CS, Chul-Kee P, et al. True progression versus pseudoprogression in the treatment of glioblastomas: a comparison study of normalized cerebral blood volume and apparent diffusion coefficient by histogram analysis. Korean J Radiol, 2013, 14(4): 662-672.
[16]
Chu HH, Choi SH, Ryoo I, et al. Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging. Radiology, 2013, 269(3): 831-840.
[17]
Jr RFB, Butowski NA, Phillips JJ, et al. The development of reduced diffusion following bevacizumab therapy identifies regions of recurrent disease in patients with high-grade glioma. Acad Radiol, 2016, 23(9): 1073-1082.
[18]
Hope TR, Vardal J, Bjørnerud A, et al. Serial diffusion tensor imaging for early detection of radiation-induced injuries to normal-appearing white matter in high-grade glioma patients. J Magn Reson Imaging, 2015, 41(2): 414-423.
[19]
Wu EX, Cheung MM. MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed, 2010, 23(7): 836-848.
[20]
Jiang R, Jiang J, Zhao L, et al. Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget, 2015, 6(39): 42380-42393.
[21]
Lacerda S, Meng L. Magnetic resonance perfusion and permeability imaging in brain tumors. Neuroimaging Clin N Am, 2009, 19(4): 527-557.
[22]
Patel P, Baradaran H, Delgado D, et al. MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol, 2017, 19(1): 118-127.
[23]
Kelm ZS, Korfiatis PD, Lingineni RK, et al. Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression. J Med Imaging (Bellingham), 2015, 2(2): 26001.
[24]
Boxerman JL, Ellingson BM, Jeyapalan S, et al. Longitudinal DSC-MRI for distinguishing tumor recurrence from pseudoprogression in patients with a high-grade glioma. Am J Clin Oncol, 2017, 40(3): 228-234.
[25]
Wang S, Martinez-Lage M, Sakai Y, et al. Differentiating tumor progression from pseudoprogression in patients with glioblastomas using diffusion tensor imaging and dynamic susceptibility contrast MRI. AJNR Am J Neuroradiol, 2016, 37(1): 28-36.
[26]
Choi SH, Jung SC, Kim KW, et al. Perfusion MRI as the predictive/prognostic and pharmacodynamic biomarkers in recurrent malignant glioma treated with bevacizumab: a systematic review and a time-to-event meta-analysis. J Neurooncol, 2016, 128(2): 185-194.
[27]
Yun TJ, Park CK, Kim TM, et al. Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR imaging. Radiology, 2015, 274(3): 830-840.
[28]
Thomas AA, Arevalo-Perez J, Kaley T, et al. Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma. J Neurooncol, 2015, 125(1): 183-190.
[29]
Choi YJ, Kim HS, Jahng GH, et al. Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging. Acta Radiol Open, 2013, 54(4): 448-454.
[30]
Wang Q, Zhang H, Zhang J, et al. The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: A systematic review and meta-analysis. Eur Radiol, 2015, 26(8): 2670-2684.
[31]
Tomas K, Martin B, Petr P, et al. Advanced MRI increases the diagnostic accuracy of recurrent glioblastoma: Single institution thresholds and validation of MR spectroscopy and diffusion weighted MR imaging. Neuroimage Clin, 2016, 11: 316-321.
[32]
Zhang H, Ma L, Wang Q, et al. Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis. Eur J Radiol, 2014, 83(12): 2181-2189.
[33]
Matsusue E, Fink JR, Rockhill JK, et al. Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology, 2010, 52(4): 297-306.
[34]
Akbari H, Macyszyn L, Da X, et al. Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery, 2016, 78(4): 572-580.

PREV Giant cell reparative granuloma of temporal region: a case report
NEXT Magnetic resonance diffusion imaging: Clinical research progress of IVIM and DKI in breast lesions
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn