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Clinical Article
Application of MUSE-DWI combined with amide proton transfer quantitative imaging in evaluating the consistency of meningiomas
LÜ Hongjie  SUN Meng  LÜ Ruirui  DANG Pei  DONG Lei  HOU Mingli  MA Xinyu  DING Xuefu  WANG Xiaodong 

DOI:10.12015/issn.1674-8034.2025.12.010.


[Abstract] Objective To investigate the value of multiplexed sensitivity encoding diffusion weighted imaging (MUSE-DWI) combined with amide proton transfer (APT) imaging in preoperatively assessing meningioma consistency.Materials and Methods A retrospective analysis was performed on 71 patients with meningioma who underwent tumor resection at the General Hospital of Ningxia Medical University between January 2024 and August 2025. All patients had complete pathological results and comprehensive intraoperative surgical records. Preoperatively, each patient underwent conventional MRI, MUSE-DWI, amide proton transfer (APT) imaging, and contrast enhanced T1-weighted imaging (CE-T1WI). During surgery, tumor consistency was assessed according to the Zada classification scale and categorized into a soft group or a non-soft group. The apparent diffusion coefficient (ADC) and APT values were measured within the enhancing region. Independent samples t-test or Mann-Whitney U test was used to compare ADC and APT values between groups. Parameters showing significant differences were included in a multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of individual parameters and their combination for predicting meningioma consistency. The areas under the ROC curves (AUCs) were compared using DeLong's test.Results The soft meningioma group showed significantly higher ADC and APT values than the non-soft group (P = 0.011 and P < 0.001, respectively). Among all single parameters, APT value demonstrated the highest diagnostic efficacy for differentiating soft from non-soft meningiomas (AUC = 0.915), which was higher than that of the ADC value (AUC = 0.675). The multiparameter combined prediction model (ADC + APT) achieved an AUC of 0.947, which is higher than that of any single parameter. The DeLong test demonstrated that the multiparameter combined model achieved significantly superior diagnostic performance compared to the ADC value (P < 0.05), while no statistically significant difference was observed in the AUC between the combined model and APT (P = 0.061).Conclusions Both MUSE-DWI and APT techniques are useful for the noninvasive preoperative prediction of meningioma consistency. Their combination provides the highest diagnostic performance.
[Keywords] meningioma;consistency;magnetic resonance imaging;multiplexed sensitivity encoding;amide proton transfer;prediction

LÜ Hongjie1   SUN Meng2   LÜ Ruirui3   DANG Pei3   DONG Lei3   HOU Mingli3   MA Xinyu1   DING Xuefu1   WANG Xiaodong3, 4*  

1 Clinical Medical College, Ningxia Medical University, Yinchuan 750004, China

2 Department of Radiology, Baoji Hospital of Traditional Chinese Medicine, Baoji 721000, China

3 Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750004, China

4 Key Laboratory of Craniofacial Diseases, Ningxia Medical University, Yinchuan 750004, China

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

Conflicts of interest   None.

Received  2025-09-24
Accepted  2025-12-03
DOI: 10.12015/issn.1674-8034.2025.12.010
DOI:10.12015/issn.1674-8034.2025.12.010.

[1]
OSTROM Q T, GITTLEMAN H, FULOP J, et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012[J/OL]. Neuro Oncol, 2015, 17(Suppl 4): iv1-iv62 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/26511214/. DOI: 10.1093/neuonc/nov189.
[2]
BIERNAT D, BIRKELAND BUGGE R A, RAMM-PETTERSEN J, et al. Predicting intraoperative meningioma consistency using features from standard MRI sequences: a preoperative evaluation[J/OL]. Acta Neurochir (Wien), 2025, 167(1): 173 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/40542946/. DOI: 10.1007/s00701-025-06582-9.
[3]
MURPHY M C, HUSTON J, GLASER K J, et al. Preoperative assessment of meningioma stiffness using magnetic resonance elastography[J]. J Neurosurg, 2013, 118(3): 643-648. DOI: 10.3171/2012.9.JNS12519.
[4]
YOGI A, KOGA T, AZAMA K, et al. Usefulness of the apparent diffusion coefficient (ADC) for predicting the consistency of intracranial meningiomas[J]. Clin Imaging, 2014, 38(6): 802-807. DOI: 10.1016/j.clinimag.2014.06.016.
[5]
RABIEE S, KANKAM S B, SHAFIZADEH M, et al. Supratentorial meningioma consistency prediction utilizing tumor to cerebellar peduncle intensity on T1 and T2-weighted and fluid attenuated inversion recovery magnetic resonance imaging sequences[J/OL]. World Neurosurg, 2023, 170: e180-e187 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/36328167/. DOI: 10.1016/j.wneu.2022.10.097.
[6]
CEPEDA S, ARRESE I, GARCÍA-GARCÍA S, et al. Meningioma consistency can be defined by combining the radiomic features of magnetic resonance imaging and ultrasound elastography. a pilot study using machine learning classifiers[J/OL]. World Neurosurg, 2021, 146: e1147-e1159 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/33259973/. DOI: 10.1016/j.wneu.2020.11.113.
[7]
HAN T, LIU X W, SUN J C, et al. T2-weighted imaging and apparent diffusion coefficient histogram parameters predict meningioma consistency[J]. Acad Radiol, 2024, 31(6): 2511-2520. DOI: 10.1016/j.acra.2023.12.014.
[8]
JIANG Y, LI L, SONG C J, et al. Application of FOCUS-MUSE-DWI and conventional DWI in the diagnosis of rectal cancer[J]. CHIN J CT MRI, 2025, 23(9): 185-187, 212. DOI: 10.3969/J.ISSN.1672-5131.2025.09.051.
[9]
LÜ R R, WANG L, GE X, et al. A comparison of multiplexed sensitivity encoding DWI and conventional DWI in the evaluation of glioma[J]. Chin Comput Med Imag, 2025, 31(2): 162-166. DOI: 10.19627/j.cnki.cn31-1700/th.2025.02.010.
[10]
CHANG H C, CHEN G T, 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.
[11]
ZHA F X, FENG C, XU J, et al. Evaluation of multiplexed sensitivity encoding diffusion-weighted imaging in detecting uterine lesions: Image quality optimization[J/OL]. Magn Reson Imag, 2024, 110: 17-22 [2025-09-23]. https://www.sciencedirect.com/science/article/pii/S0730725X24000523?via%3Dihub. DOI: 10.1016/j.mri.2024.03.003.
[12]
LIU X R, TIAN Z R, HE H, et al. Research on the application value of magnetic resonance MUSE sequence in breast lesions[J]. J CHINA CLIN MED IMAG, 2025, 36(7): 469-473. DOI: 10.12117/JCCMI.2025.07.004.
[13]
GU H L, TANG W W, TIAN Z F, et al. Quantitative parameters of synthetic MRI and multiplexed sensitivity encoding diffusion weighted imaging for predicting pathological characteristics of endometrial cancer[J]. CHIN J INTERV IMAG THER, 2025, 22(3): 183-187. DOI: 10.13929/J.ISSN.1672-8475.2025.03.007.
[14]
HU Y F, HUANG Z J, LIU K, et al. Optimization of parameters for multiple sensitivity encoding diffusion-weighted imaging in nasopharyngeal cancer[J]. CHIN J CT MRI, 2025, 23(6): 39-42. DOI: 10.3969/J.ISSN.1672-5131.2025.06.012.
[15]
YU H, LOU H L, ZOU T Y, et al. Applying protein-based amide proton transfer MR imaging to distinguish solitary brain metastases from glioblastoma[J]. Eur Radiol, 2017, 27(11): 4516-4524. DOI: 10.1007/s00330-017-4867-z.
[16]
TOGAO O, KESSINGER C W, HUANG G, et al. Characterization of lung cancer by amide proton transfer (APT) imaging: an in-vivo study in an orthotopic mouse model[J/OL]. PLoS One, 2013, 8(10): e77019 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/24143199/. DOI: 10.1371/journal.pone.0077019.
[17]
ZADA G, YASHAR P, ROBISON A, et al. A proposed grading system for standardizing tumor consistency of intracranial meningiomas[J/OL]. Neurosurg Focus, 2013, 35(6): E1 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/24289117/. DOI: 10.3171/2013.8.focus13274.
[18]
PRICE M, BALLARD C, BENEDETTI J, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2017-2021[J/OL]. Neuro Oncol, 2024, 26(Supplement_6): vi1-vi85 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/39371035/. DOI: 10.1093/neuonc/noae145.
[19]
XU C, SHAO C, WANG J, et al. Association between anthropometric factors and meningioma risk: a systematic review and meta-analysis[J/OL]. PLoS One, 2025, 20(5): e0323461 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/40359201/. DOI: 10.1371/journal.pone.0323461.
[20]
OGASAWARA C, PHILBRICK B D, ADAMSON D C. Meningioma: a review of epidemiology, pathology, diagnosis, treatment, and future directions[J/OL]. Biomedicines, 2021, 9(3): 319 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/33801089/. DOI: 10.3390/biomedicines9030319.
[21]
LIN H J, YUE Y B, XIE L, et al. Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study[J/OL]. BMC Med Imag, 2025, 25(1): 216 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/40596910/. DOI: 10.1186/s12880-025-01787-x.
[22]
ZHENG L M, JIANG P R, LIN D J, et al. Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas[J/OL]. Cancer Imaging, 2023, 23(1): 117 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/38053183/. DOI: 10.1186/s40644-023-00633-z.
[23]
XU S, WANG Y F, HONG X X, et al. Analysis of the diagnostic value of magnetic resonance diffusion-weighted imaging for meningiomas of different grades[J]. Prog Biomed Eng, 2024, 45(4): 293-298. DOI: 10.3969/j.issn.1674-1242.2024.04.002.
[24]
ERSAY H, HATIPOGLU H G, GURESCI S. ADC values compared to tumor grade and Ki-67 proliferation index detected by a digital image analysis program in meningiomas[J]. Acta Radiol, 2025, 66(12): 1263-1270. DOI: 10.1177/02841851251365512.
[25]
LIMPASTAN K, UNSRISONG K, VANIYAPONG T, et al. Benefits of combined MRI sequences in meningioma consistency prediction: a prospective study of 287 consecutive patients[J]. Asian J Neurosurg, 2022, 17(4): 614-620. DOI: 10.1055/s-0042-1758849.
[26]
MASUDA H, NEMOTO M, HARADA N, et al. Comparison of quantitative measurements of central nervous system tumour consistency and the associated preoperative imaging findings[J]. Br J Neurosurg, 2019, 33(5): 522-527. DOI: 10.1080/02688697.2019.1617405.
[27]
CAI L, WANG Y F, JU J Y, et al. Comparative analysis of DWI sequences: muse vs SS-EPI in evaluating Crohn's disease activity[J]. Chin Comput Med Imag, 2025, 31(2): 232-236. DOI: 10.19627/j.cnki.cn31-1700/th.2025.02.017.
[28]
LI G, LUO N, OUYANG J Y. Comparison of the value of multiplexed sensitivity encoding diffusion-weighted imaging with conventional diffusion-weighted imaging in brain examination[J]. Image Technol, 2022, 34(4): 25-28, 34. DOI: 10.3969/j.issn.1001-0270.2022.04.05.
[29]
LIU Q, ZHOU Z P. Principle and clinical application of high resolution magnetic resonance diffusion imaging based on composite sensitivity coding[J]. Chin J Magn Reson Imaging, 2022, 13(1): 167-170. DOI: 10.12015/issn.1674-8034.2022.01.040.
[30]
MIYOSHI K, WADA T, UWANO I, et al. Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging[J]. J Neurosurg, 2020, 135(3): 969-976. DOI: 10.3171/2020.6.JNS20740.
[31]
HIGANO S, YUN X, KUMABE T, et al. Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis[J]. Radiology, 2006, 241(3): 839-846. DOI: 10.1148/radiol.2413051276.
[32]
ZHOU J Y, PAYEN J F, WILSON D A, et al. Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI[J]. Nat Med, 2003, 9(8): 1085-1090. DOI: 10.1038/nm907.
[33]
GE Y, DU J, CHENG H, et al. Assessment of renal allograft function using amide proton transfer imaging[J/OL]. Front Med (Lausanne), 2025, 12: 1612028 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/40950973/. DOI: 10.3389/fmed.2025.1612028.
[34]
SHI Y, HUO Y L, PAN C, et al. Use of magnetic resonance elastography to gauge meningioma intratumoral consistency and histotype[J/OL]. Neuroimage Clin, 2022, 36: 103173 [2025-09-23]. https://pubmed.ncbi.nlm.nih.gov/36081257/. DOI: 10.1016/j.nicl.2022.103173.
[35]
YU H, WANG X L, SUN Z G, et al. Feasibility study of magnetic resonance amide proton transfer imaging in predicting the hardness of meningioma[J]. Chin J Neurosurg, 2022, 38(8): 831-836. DOI: 10.3760/cma.j.cn112050-20201120-00584.
[36]
TAKAMURA T, MOTOSUGI U, OGIWARA M, et al. Relationship between shear stiffness measured by MR elastography and perfusion metrics measured by perfusion CT of meningiomas[J]. AJNR Am J Neuroradiol, 2021, 42(7): 1216-1222. DOI: 10.3174/ajnr.A7117.
[37]
LI J L, XU Y, XIANG Y S, et al. Comparative analysis of amide proton transfer and diffusionweighted imaging for assessing ki-67, p53 and PD-L1 expression in bladder cancer[J]. Acad Radiol, 2025, 32(2): 834-843. DOI: 10.1016/j.acra.2024.09.043.

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