Multiparametric study for glioma grading with FLAIR, ADC map, eADC map, T1 map, and SWI images

Magnetic Resonance Imaging - Tập 96 - Trang 93-101 - 2023
Amir Khorasani1, Mohamad Bagher Tavakoli1
1Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Tài liệu tham khảo

Miller, 2021, Brain and other central nervous system tumor statistics, 2021, CA Cancer J Clin, 71, 381, 10.3322/caac.21693 Goodenberger, 2012, 205, 613 Louis, 2016, The 2016 World Health Organization classification of tumors of the central nervous system: a summary, Acta Neuropathol, 131, 803, 10.1007/s00401-016-1545-1 Hakyemez, 2005, High-grade and low-grade gliomas: differentiation by using perfusion MR imaging, Clin Radiol, 60, 493, 10.1016/j.crad.2004.09.009 Khorasani, 2021, Preliminary study of multiple b-value diffusion-weighted images and T1 post enhancement magnetic resonance imaging images fusion with Laplacian Re-decomposition (LRD) medical fusion algorithm for glioma grading, Eur J Radiol Open, 8, 10.1016/j.ejro.2021.100378 Al-Agha, 2020, Efficiency of high and standard b value diffusion-weighted magnetic resonance imaging in grading of gliomas, J Oncol, 2020, 10.1155/2020/6942406 Kusunoki, 2020, Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models, Neuroradiology, 62, 815, 10.1007/s00234-020-02456-2 Mao, 2020, Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models, BMC Med Imaging, 20, 1, 10.1186/s12880-020-00524-w Gaudino, 2020, Role of susceptibility-weighted imaging and intratumoral susceptibility signals in grading and differentiating pediatric brain tumors at 1.5 T: a preliminary study, Neuroradiology, 62, 705, 10.1007/s00234-020-02386-z Abo-Elhoda, 2021, Role of susceptibility weighted imaging (SWI) in assessment of intra axial brain neoplasms in adults, QJM An Int J Med, 114, 10.1093/qjmed/hcab106.056 Hsu, 2016, Susceptibility-weighted imaging of glioma: update on current imaging status and future directions, J Neuroimaging, 26, 383, 10.1111/jon.12360 Law, 2003, Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging, Am J Neuroradiol, 24, 1989 Essig, 2013, Perfusion MRI: the five most frequently asked technical questions, AJR Am J Roentgenol, 200, 24, 10.2214/AJR.12.9543 Ellika, 2007, Role of perfusion CT in glioma grading and comparison with conventional MR imaging features, Am J Neuroradiol, 28, 1981, 10.3174/ajnr.A0688 Vamvakas, 2019, Imaging biomarker analysis of advanced multiparametric MRI for glioma grading, Phys Med, 60, 188, 10.1016/j.ejmp.2019.03.014 Bhattacharjee, 2020, Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma, J Magn Reson Imaging, 51, 225, 10.1002/jmri.26786 Aydin, 2017, Susceptibility imaging in glial tumor grading; using 3 tesla magnetic resonance (MR) system and 32 channel head coil, Polish J Radiol, 82, 179, 10.12659/PJR.900374 Gu, 2021, Exploring diagnostic performance of T2 mapping in diffuse glioma grading, Quant Imaging Med Surg, 11, 2943, 10.21037/qims-20-916 Kern, 2020, T2 mapping of molecular subtypes of WHO grade II/III gliomas, BMC Neurol, 20, 1, 10.1186/s12883-019-1590-1 Cao, 2019, Brain T1ρ mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study, J Neurooncol, 141, 245, 10.1007/s11060-018-03033-7 Pirkl, 2021, Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging, Neuroradiology, 1 Goceri, 2018, Fully automated and adaptive intensity normalization using statistical features for brain MR images, Celal Bayar Univ J Sci, 14, 125 Fortin, 2016, Removing inter-subject technical variability in magnetic resonance imaging studies, Neuroimage, 132, 198, 10.1016/j.neuroimage.2016.02.036 Reinhold, 2019, 890 Gondara, 2016, 241 Liberman, 2014, T1 mapping using variable flip angle SPGR data with flip angle correction, J Magn Reson Imaging, 40, 171, 10.1002/jmri.24373 Blüml, 1993, Spin-lattice relaxation time measurement by means of a TurboFLASH technique, Magn Reson Med, 30, 289, 10.1002/mrm.1910300304 Khorasani, 2021, Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images, Polish J Med Phys Eng, 27, 261, 10.2478/pjmpe-2021-0031 Lin, 2013, Glioma-related edema: new insight into molecular mechanisms and their clinical implications, Chin J Cancer, 32, 49, 10.5732/cjc.012.10242 Schoenegger, 2009, Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma?, Eur J Neurol, 16, 874, 10.1111/j.1468-1331.2009.02613.x Soliman, 2021, Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: analysis of whole tumoral and peri-tumoral tissue, Diagn Interv Imaging, 102, 287, 10.1016/j.diii.2020.12.001 Lee, 2014, Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation, NMR Biomed, 27, 1046, 10.1002/nbm.3153 Hu, 2017, Comparison between ultra-high and conventional mono b-value DWI for preoperative glioma grading, Oncotarget, 8, 37884, 10.18632/oncotarget.14180 Gihr, 2021, Diffusion weighted imaging in high-grade gliomas: a histogram-based analysis of apparent diffusion coefficient profile, PLoS One, 16, 10.1371/journal.pone.0249878 Razek, 2020, Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas, Polish J Radiol, 85 Provenzale, 1999, Use of MR exponential diffusion-weighted images to eradicate T2“ shine-through” effect, AJR Am J Roentgenol, 172, 537, 10.2214/ajr.172.2.9930819 Sprinkart, 2018, 758 Howe, 2003, Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy, Magn Reson Med An Off J Int Soc Magn Reson Med, 49, 223, 10.1002/mrm.10367 Mou, 2010, AQP-4 in peritumoral edematous tissue is correlated with the degree of glioma and with expression of VEGF and HIF-alpha, J Neurooncol, 100, 375, 10.1007/s11060-010-0205-x Zoccarato, 2021, Edema, thrombosis, and hemorrhages: an update review on the medical management of Gliomas, Front Oncol, 11 Biterge-Sut, 2020, A comprehensive analysis of the angiogenesis-related genes in glioblastoma multiforme vs. brain lower grade glioma, Arq Neuropsiquiatr, 78, 34, 10.1590/0004-282x20190131 Park, 2009, Semiquantitative assessment of intratumoral susceptibility signals using non-contrast-enhanced high-field high-resolution susceptibility-weighted imaging in patients with gliomas: comparison with MR perfusion imaging, Am J Neuroradiol, 30, 1402, 10.3174/ajnr.A1593 Minati, 2007, Physical foundations, models, and methods of diffusion magnetic resonance imaging of the brain: a review, Concepts Magn Reson Part A An Educ J, 30, 278, 10.1002/cmr.a.20094 Aherne, 2020, Cardiac T1 mapping: techniques and applications, J Magn Reson Imaging, 51, 1336, 10.1002/jmri.26866 Duyn, 2013, MR susceptibility imaging, J Magn Reson, 229, 198, 10.1016/j.jmr.2012.11.013 Park, 2016, Exponential apparent diffusion coefficient in evaluating prostate cancer at 3 T: preliminary experience, Br J Radiol, 89, 20150470, 10.1259/bjr.20150470 Kim, 2007, A prospective study on the added value of pulsed arterial spin-labeling and apparent diffusion coefficients in the grading of gliomas, Am J Neuroradiol, 28, 1693, 10.3174/ajnr.A0674 Park, 2010, Combination of high-resolution susceptibility-weighted imaging and the apparent diffusion coefficient: added value to brain tumour imaging and clinical feasibility of non-contrast MRI at 3 T, Br J Radiol, 83, 466, 10.1259/bjr/34304111 Khorasani, 2022, Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-net, Australas Phys Eng Sci Med, 45, 925, 10.1007/s13246-022-01164-w