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• Clinical Article •
Application of synthetic MRI combined with MUSE-DWI to differentiate glioma progressive disease from treatment-related changes
LÜ Ruirui  YANG Zhihua  DANG Pei  HUANG Xueying  MA Wenfu  JIN Yixuan  LÜ Hongjie  WANG Xiaodong 

Cite this article as: LÜ R R, YANG Z H, DANG P, et al. Application of synthetic MRI combined with MUSE-DWI to differentiate glioma progressive disease from treatment-related changes[J]. Chin J Magn Reson Imaging, 2024, 15(7): 81-86. DOI:10.12015/issn.1674-8034.2024.07.014.


[Abstract] Objective To assess the utility of synthetic MRI quantitative parameters and multiplexed sensitivity encoding diffusion weighted imaging (MUSE-DWI) in combination to differentiate glioma progressive disease (PD) from treatment-related change (TRC).Materials and Methods In this study, we collected 45 patients who exhibited new enhancing lesions after surgery followed by completion of chemoradiation therapy from September 2020 to November 2022. The scan sequences included synthetic MRI, MUSE-DWI and contrast enhanced T1-weighted imaging (CE_T1WI). The patients were classified into two groups: PD group (n=26) and TRC group (n=19). The ROI is placed on each image to measure apparent diffusion coefficient (ADC), pre-contrast T1, T2 value (T1pre, T2pre) and post-contrast T1, T2 value (T1post, T2post). Quantitative parameters (T1pre, T2pre and T1post, T2post) and ADC were evaluated using Student's t-test or Mann-Whitney U test. We generated receiver operating characteristic (ROC) curves for each parameter and their combinations. Finally, we used the area under the ROC curve (AUC) to assess the performance of each parameter and their combinations.Results (1) The T1pre value in the PD group were significantly higher than the TRC group (P<0.05). The values of T1post and ADC in the PD group were significantly lower than the TRC group (all P<0.05). There was no statistical difference in T2pre, T2post value (P>0.05). (2) ADC diagnostic performance was highest when using single parameter analysis (AUC=0.878), followed by T1post and T1pre with AUC of 0.783 and 0.745, respectively. The combinations of two parameters (T1pre+T1post) improved the diagnostic performance (AUC=0.850) compared to the single parameter. A combined multi-parameter model (T1pre+T1post+ADC) was established with the highest diagnostic efficacy (AUC=0.901).Conclusions The combinations of the two techniques to construct a multiparametric combined model of relaxation quantitative parameters (T1pre, T1post) combined with ADC values have a good diagnostic value in differentiating PD and TRC.
[Keywords] glioma；synthetic magnetic resonance imaging；magnetic resonance imaging；multiplexed sensitivity-encoding；progressive disease；treatment-related change

LÜ Ruirui1   YANG Zhihua2   DANG Pei1   HUANG Xueying1   MA Wenfu3   JIN Yixuan3   LÜ Hongjie3   WANG Xiaodong1*  

1 Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750003, China

2 Department of Radiotherapy, Cancer Hospital of Ningxia Medical University General Hospital, Yinchuan 750003, China

3 The First Clinical Medical College of Ningxia Medical University, Yinchuan 750003, China

Corresponding author: WANG X D, E-mail: xdw80@yeah.net

Conflicts of interest   None.

Received  2024-02-04
Accepted  2024-07-12
DOI: 10.12015/issn.1674-8034.2024.07.014
Cite this article as: LÜ R R, YANG Z H, DANG P, et al. Application of synthetic MRI combined with MUSE-DWI to differentiate glioma progressive disease from treatment-related changes[J]. Chin J Magn Reson Imaging, 2024, 15(7): 81-86. DOI:10.12015/issn.1674-8034.2024.07.014.

[1]
GE X, YANG Z H, SHEN Y, et al. Application of synthetic MRI in predicting isocitrate dehydrogenase 1 genotypes in gliomas[J]. Natl Med J China, 2023, 103(33): 2619-2623. DOI: 10.3760/cma.j.cn112137-20230130-00137.
[2]
WU X, LIANG X, WANG X, et al. Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging[J/OL]. Eur J Radiol, 2021, 135: 109445 [2024-01-22]. https://doi.org/10.1016/j.ejrad.2020.109445. DOI: 10.1016/j.ejrad.2020.109445.
[3]
TANENBAUM L N, TSIOURIS A J, JOHNSON A N, et al. Synthetic MRI for clinical neuroimaging: Results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial[J]. AJNR Am J Neuroradiol, 2017, 38(6): 1103-1110. DOI: 10.3174/ajnr.A5227.
[4]
KONAR A S, PAUDYAL R, SHAH A D, et al. Qualitative and quantitative performance of magnetic resonance image compilation (MAGiC) method: An exploratory analysis for head and neck imaging[J/OL]. Cancers (Basel), 2022, 14(15): 3624 [2024-01-22]. https://doi.org/10.3390/cancers14153624. DOI: 10.3390/cancers14153624.
[5]
MATSUDA M, TSUDA T, EBIHARA R, et al. Triple-negative breast cancer on contrast-enhanced MRI and synthetic MRI: A comparison with non-triple-negative breast carcinoma[J/OL]. Eur J Radiol, 2021, 142: 109838 [2024-01-22]. https://doi.org/10.1016/j.ejrad.2021.109838. DOI: 10.1016/j.ejrad.2021.109838.
[6]
SUH Y H, KANG Y, BAEK M J, et al. T2 relaxation time shortening in the cochlea of patients with sudden sensory neuronal hearing loss: a retrospective study using quantitative synthetic magnetic resonance imaging[J]. Eur Radiol, 2021, 31(9): 6438-6445. DOI: 10.1007/s00330-021-07749-5.
[7]
MCKAY J A, CHURCH A L, RUBIN N, et al. A comparison of methods for high-spatial-resolution diffusion-weighted imaging in breast MRI[J]. Radiology, 2020, 297(2): 304-312. DOI: 10.1148/radiol.2020200221.
[8]
CHANG H C, CHEN G, CHUNG H W, et al. Multi‐shot diffusion‐weighted MRI with multiplexed sensitivity encoding (MUSE) in the assessment of active inflammation in Crohn's disease[J]. J Magn Reson Imaging, 2022, 55(1): 126-137. DOI: 10.1002/jmri.27801.
[9]
JOHANSSON J, LAGERSTRAND K, IVARSSON L, et al. Brain diffusion MRI with multiplexed sensitivity encoding for reduced distortion in a pediatric patient population[J]. Magn Reson Imaging, 2022, 87: 97-103. DOI: 10.1016/j.mri.2022.01.003.
[10]
HERBST M, ZAHNEISEN B, KNOWLES B, et al. Prospective motion correction of segmented diffusion weighted EPI[J]. Magn Reson Med, 2015, 74(6): 1675-1681. DOI: 10.1002/mrm.25547.
[11]
GE X, WANG M, MA H, et al. Investigated diagnostic value of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling and diffusion-weighted imaging in the grading of glioma[J]. Magn Reson Imaging, 2022, 86: 20-27. DOI: 10.1016/j.mri.2021.11.006.
[12]
MA W F, GE X, DANG P, et al. Application of Sy-MRI combined with DWI in predicting MGMT methylation in glioma[J]. Chin J Magn Reson Imaging, 2023, 14(7): 18-24, 48. DOI: 10.12015/issn.1674-8034.2023.07.004.
[13]
GE X, SUN S Y, LIU W X, et al. Prediction of diffuse glioma grade and tumor cell proliferative activity by synthetic MRI combined with three-dimensional arterial spin labeling imaging[J]. Chin J of Radiol, 2022, 56(5): 524-529. DOI: 10.3760/cma.j.cn112149-20210617-00569.
[14]
KIKUCHI K, TOGAO O, YAMASHITA K, et al. Comparison of diagnostic performance of radiologist- and AI-based assessments of T2-FLAIR mismatch sign and quantitative assessment using synthetic MRI in the differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, IDH-mutant and 1p/19q-codeleted[J/OL]. Neuroradiology, 2024 [2024-01-22]. https://doi.org/10.1007/s00234-024-03288-0. DOI: 10.1007/s00234-024-03288-0.
[15]
ELLINGSON B M, WEN P Y, CLOUGHESY T F. Modified criteria for radiographic response assessment in glioblastoma clinical trials[J]. Neurotherapeutics, 2017, 14(2): 307-320. DOI: 10.1007/s13311-016-0507-6.
[16]
DASTMALCHIAN S, KILINC O, ONYEWADUME L, et al. Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors[J]. Eur J Nucl Med Mol Imaging, 2021, 48(3): 683-693. DOI: 10.1007/s00259-020-05037-w.
[17]
BLYSTAD I, WARNTJES J B M, SMEDBY Ö, et al. Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema[J/OL]. Sci Rep, 2020, 10(1): 17986 [2024-01-23]. https://doi.org/10.1038/s41598-020-75105-6. DOI: 10.1038/s41598-020-75105-6.
[18]
MULLER A, JURCOANE A, KEBIR S, et al. Quantitative T1-mapping detects cloudy-enhancing tumor compartments predicting outcome of patients with glioblastoma[J]. Cancer Med, 2017, 6(1): 89-99. DOI: 10.1002/cam4.966.
[19]
HATTINGEN E, MULLER A, JURCOANE A, et al. Value of quantitative magnetic resonance imaging T1-relaxometry in predicting contrast-enhancement in glioblastoma patients[J]. Oncotarget, 2017, 8(32): 53542-53551. DOI: 10.18632/oncotarget.18612.
[20]
XIE J P, ZhANG W D, ZHU J Y, et al. The clinical value of T1 and T2 values in predicting brain glioma grading and cell proliferation activity[J]. Chin J Magn Reson Imaging, 2021, 12(1): 15-20. DOI: 10.12015/issn.1674-8034.2021.01.004.
[21]
LESCHER S, JURCOANE A, VEIT A, et al. Quantitative T1 and T2 mapping in recurrent glioblastomas under bevacizumab: earlier detection of tumor progression compared to conventional MRI[J]. Neuroradiology, 2015, 57(1): 11-20. DOI: 10.1007/s00234-014-1445-9.
[22]
WANG B, ZHANG Y, ZHAO B, et al. Postcontrast T1 mapping for differential diagnosis of recurrence and radionecrosis after gamma knife radiosurgery for brain metastasis[J]. AJNR Am J Neuroradiol, 2018, 39(6): 1025-1031. DOI: 10.3174/ajnr.A5643.
[23]
KANG K M, CHOI S H, HWANG M, et al. Application of synthetic MRI for direct measurement of magnetic resonance relaxation time and tumor volume at multiple time points after contrast administration: Preliminary results in patients with brain metastasis[J/OL]. Korean J Radiol, 2018, 19(4): 783 [2024-01-23]. https://doi.org/10.3348/kjr.2018.19.4.783. DOI: 10.3348/kjr.2018.19.4.783.
[24]
ZACH L, GUEZ D, LAST D, et al. Delayed contrast extravasation MRI for depicting tumor and non-tumoral tissues in primary and metastatic brain tumors[J/OL]. PLoS One, 2012, 7(12): e52008 [2024-01-23]. https://doi.org/10.1371/journal.pone.0052008. DOI: 10.1371/journal.pone.0052008.
[25]
AKBARI H, RATHORE S, BAKAS S, et al. Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma[J]. Cancer, 2020, 126(11): 2625-2636. DOI: 10.1002/cncr.32790.
[26]
CHEN X, ZHANG Y, CAO Y, et al. A feasible study on using multiplexed sensitivity-encoding to reduce geometric distortion in diffusion-weighted echo planar imaging[J]. Magn Reson Imaging, 2018, 54: 153-159. DOI: 10.1016/j.mri.2018.08.022.
[27]
CHEN N, GUIDON A, CHANG H, et al. A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE)[J]. NeuroImage, 2013, 72: 41-47. DOI: 10.1016/j.neuroimage.2013.01.038.
[28]
LI J, BAI Y, WU L, et al. Synthetic relaxometry combined with MUSE DWI and 3D-pCASL improves detection of hippocampal sclerosis[J/OL]. Eur J Radiol, 2022, 157: 110571 [2024-01-23]. https://doi.org/10.1016/j.ejrad.2022.110571. DOI: 10.1016/j.ejrad.2022.110571.
[29]
ZHANG H, MA L, SHU C, et al. Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necrosis[J]. J Neurol Sci, 2015, 351(1-2): 65-71. DOI: 10.1016/j.jns.2015.02.038.
[30]
WERNER J, STOFFELS G, LICHTENSTEIN T, et al. Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI[J]. Eur J Nucl Med Mol Imaging, 2019, 46(9): 1889-1901. DOI: 10.1007/s00259-019-04384-7.
[31]
CARRETE L R, YOUNG J S, CHA S. Advanced imaging techniques for newly diagnosed and recurrent gliomas[J/OL]. Front Neurosci, 2022, 16: 787755 [2024-01-23]. https://doi.org/10.3389/fnins.2022.787755. DOI: 10.3389/fnins.2022.787755.
[32]
KIM Y, KIM M, GHO S, et al. Comparison of multiplexed sensitivity encoding and single-shot echo-planar imaging for diffusion-weighted imaging of the liver[J/OL]. Eur J Radiol, 2020, 132: 109292 [2024-01-23]. https://doi.org/10.1016/j.ejrad.2020.109292. DOI: 10.1016/j.ejrad.2020.109292.
[33]
GUO Z, SU Z, WEI Y, et al. Pyroptosis in glioma: Current anagement and future application[J]. Immunol Rev, 2024, 321(1): 152-168. DOI: 10.1111/imr.13294.
[34]
RUDIE J D, RAUSCHECKER A M, BRYAN R N, et al. Emerging applications of artificial intelligence in neuro-oncology[J]. Radiology, 2019, 290(3): 607-618. DOI: 10.1148/radiol.2018181928.
[35]
REN L F, ZHANG H, WANG X C, et al. Preliminary study of magnetic resonance dynamic contrast enhancement combined with diffusion weighted imaging to identify high grade glioma recurrence and treatment-related changes[J]. Chin J Magn Reson Imaging, 2019, 10(9): 655-660. DOI: 10.12015/issn.1674-8034.2019.09.004.

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