Share：
Share this content in WeChat
X
[Chinese] [PDF] 64 2
• Clinical Article •
Prediction of MGMT methylation status in glioblastoma using DTI and ASL histogram features and its association with prognosis
YU Fengwei  JIANG Hailong  CHEN Pinzhen  YANG Jing  LIU Ruishan  YANG Jianping  CHEN Jiafei  CHEN Wei 

DOI:10.12015/issn.1674-8034.2026.07.006.


[Abstract] Objective To explore the value of histogram parameters derived from diffusion tensor imaging (DTI) and arterial spin labeling (ASL) in the non-invasive preoperative prediction of the promoter methylation status of O6-methylguanine-DNA methyltransferase (MGMT), and to further analyze their correlation with patients' survival and prognosis.Materials and Methods We retrospectively analyzed DTI and ASL data from 349 patients with IDH-wildtype glioblastoma. Histogram parameters—including fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF)—were extracted. Binary logistic regression models were built using DTI parameters, ASL parameters, and their combination. Patients were divided into two groups based on MGMT promoter methylation status (methylated vs. unmethylated), and comparisons were made between groups. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer-Lemeshow goodness-of-fit test. For survival analysis, we performed Kaplan-Meier analysis and used both univariate and multivariate Cox regression to examine associations between imaging features, molecular markers, and overall survival (OS). Bootstrap mediation analysis was conducted to test whether specific imaging parameters mediate the relationship between MGMT methylation status and survival.Results The DTI model (AUC = 0.842) significantly outperformed the ASL model (AUC = 0.619) in predicting MGMT methylation status. The combined DTI-ASL model (AUC = 0.859) was not significantly different from the DTI model alone (Z = 0.53, P = 0.593). Kaplan-Meier analysis confirmed that MGMT promoter methylation was a significant protective factor for prognosis (Log-rank P = 0.041, HR = 0.737, 95% CI: 0.550 to 0.989). However, Cox regression showed that although imaging parameters were significantly associated with MGMT methylation status, they were not independent predictors of OS. Mediation analysis revealed that the 10th percentile of FA mediated the relationship between MGMT methylation status and OS. Skewness of MD showed a statistical association with both factors but its mediation effect was not significant.Conclusions DTI histogram parameters serve as effective noninvasive imaging markers for predicting MGMT promoter methylation before surgery. The 10th percentile of FA is a key imaging factor linking unmethylated MGMT status to poor prognosis. MD skewness is statistically associated with this link, suggesting that disruption of tumor microstructure and increased tissue heterogeneity may mediate the effect of MGMT methylation status on patient outcomes. These findings offer a new imaging-based perspective for understanding the aggressive biological behavior of glioblastoma.
[Keywords] glioblastoma；magnetic resonance imaging；diffusion tensor imaging；arterial spin labeling；O6-methylguanine-DNA methyltransferase；prognosis

YU Fengwei1, 2   JIANG Hailong2, 3   CHEN Pinzhen1, 2   YANG Jing1, 2   LIU Ruishan1, 2   YANG Jianping1, 2   CHEN Jiafei1, 2   CHEN Wei1, 2*  

1 7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China

2 Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China

3 Department of Pharmacy, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China

Corresponding author: CHEN W, E-mail: landcw@tmmu.edu.cn

Conflicts of interest   None.

Received  2026-02-28
Accepted  2026-06-09
DOI: 10.12015/issn.1674-8034.2026.07.006
DOI:10.12015/issn.1674-8034.2026.07.006.

[1]
HORBINSKI C, BERGER T, PACKER R J, WEN P Y. Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours[J]. Nat Rev Neurol, 2022, 18(9): 515-529. DOI: 10.1038/s41582-022-00679-w.
[2]
QSTROM Q T, PRICE M, NEFF C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019[J]. Neuro Oncol, 2022, 24: v1-v95. DOI: 10.1093/neuonc/noac202.
[3]
YAO Z W, FENG X Y. Advances and prospects in imaging studies of adult diffuse gliomas[J]. China J Radiol, 2024, 58(11): 1100-1105. DOI: 10.3760/cma.j.cn112149-20240716-00415.
[4]
TANG J, KARBHARI N, CAMPIAN J L. Therapeutic Targets in Glioblastoma: Molecular Pathways, Emerging Strategies, and Future Directions[J/OL]. Cells, 2025, 14(7): 494 [2026-02-28]. https://doi.org/10.3390/cells14070494. DOI: 10.3390/cells14070494.
[5]
Neuropathology Group, Pathology Branch of Chinese Medical Association. Chinese expert consensus for molecular pathological diagnosis of gliomas (2025 version)[J]. China J Pathology, 2025, 54(6): 580-592. DOI: 10.3760/cma.j.cn112151-20250212-00090.
[6]
HORBINSKI C, NABORS L B, PORTNOW J, et al. NCCN Guidelines® Insights: Central Nervous System Cancers, Version 2.2022[J]. J Natl Compr Canc Netw, 2023, 21(1): 12-20. DOI: 10.6004/jnccn.2023.0002.
[7]
HEGI M E, DISERENS A C, GORLIA T, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma[J]. N Engl J Med, 2005, 352(10): 997-1003. DOI: 10.1056/NEJMoa043331.
[8]
STUPP R, MASON W P, VAN DEN BENT M J, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma[J]. N Engl J Med, 2005, 352(10): 987-996. DOI: 10.1056/NEJMoa043330.
[9]
HAO Z, WANG J, LV Y, et al. Identification of MGMT promoter methylation as a specific lipid metabolism biomarker, reveals the feasibility of atorvastatin application in glioblastoma[J/OL]. Metabolism, 2024, 153: 155794 [2026-02-28]. https://doi.org/10.1016/j.metabol.2024.155794. DOI: 10.1016/j.metabol.2024.155794.
[10]
WANG N, XIE S Y, LIU H M, et al. Arterial Spin Labeling for Glioma Grade Discrimination: Correlations with IDH1 Genotype and 1p/19q Status[J]. Transl Oncol, 2019, 12(5): 749-756. DOI: 10.1016/j.tranon.2019.02.013.
[11]
HUNG N DUY, MINH DUC N, VAN ANH N T, et al. Diagnostic performance of diffusion tensor imaging for preo- perative glioma grading[J]. Clin Ter, 2021, 172(4): 315-321. DOI: 10.7417/ct.2021.2335.
[12]
HU J, BAO H, LIU X, et al. Glioma-white matter tract interactions: A diffusion magnetic resonance imaging-based 3-tier classification and its clinical relevance[J]. Neuro Oncol, 2025, 27(7): 1888-1898. DOI: 10.1093/neuonc/noaf036.
[13]
CALABRESE E, VILLANUEVA-MEYER J E, RUDIE J D, et al. The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset[J/OL]. Radiol Artif Intell, 2022, 4(6): e220058[2026-02-28]. https://pubmed.ncbi.nlm.nih.gov/36523646/. DOI: 10.1148/ryai.220058.
[14]
BAID U, GHODASARA S, MOHAN S, et al. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification[J/OL]. arXiv, 2021-09-12 [2026-02-28]. https://arxiv.org/abs/2107.02314. DOI: 10.48550/arXiv.2107.02314.
[15]
MENZE B H, JAKAB A, BAUER S, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)[J]. IEEE Trans Med Imaging, 2015, 34(10): 1993-2024. DOI: 10.1109/tmi.2014.2377694.
[16]
VAN GARDEREN K A, VAN DER VOORT S R, WIJNENGA M M J, et al. Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection[J]. IEEE Trans Med Imaging, 2024, 43(1): 253-263. DOI: 10.1109/tmi.2023.3298637.
[17]
WANG W, LI T, CHENG Y, et al. Identification of hypoxic macrophages in glioblastoma with therapeutic potential for vasculature normalization[J/OL]. Cancer Cell, 2024, 42(5): 815-32.e12 [2026-02-28]. https://pubmed.ncbi.nlm.nih.gov/38640932/. DOI: 10.1016/j.ccell.2024.03.013.
[18]
FATHI KAZEROONI A, AKBARI H, HU X, et al. The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers[J/OL]. Commun Med (Lond), 2025, 5(1): 55 [2026-02-28]. https://doi.org/10.1038/s43856-025-00767-0. DOI: 10.1038/s43856-025-00767-0.
[19]
ZHANG X Y, LIU G L, ZHANG J, et al. Prediction of the Expression of MGMT Promoter Methylation in IDH Wild-Type Glioblastoma via Multiparametric MRI Radiomics[J]. Chinese Journal of Medical Imaging, 2023, 31(5): 433-441. DOI: 10.3969/j.issn.1005-5185.2023.05.001.
[20]
LESKE H, CAMENISCH GROSS U, HOFER S, et al. MGMT methylation pattern of long-term and short-term survivors of glioblastoma reveals CpGs of the enhancer region to be of high prognostic value[J/OL]. Acta Neuropathol Commun, 2023, 11(1): 139 [2026-02-28]. https://doi.org/10.1186/s40478-023-01622-w. DOI: 10.1186/s40478-023-01622-w.
[21]
LIU Y, ZHU Z, ZHOU J, et al. Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI[J/OL]. Front Neurol, 2025, 16: 1493666 [2026-02-28]. https://doi.org/10.3389/fneur.2025.1493666. DOI: 10.3389/fneur.2025.1493666.
[22]
SONG S, SHAN Y, WANG L, et al. MGMT promoter methylation status shows no effect on [(18)F]FET uptake and CBF in gliomas: a stereotactic image-based histological validation study[J]. Eur Radiol, 2022, 32(8): 5577-5587. DOI: 10.1007/s00330-022-08606-9.
[23]
SEOW P, HERNOWO A T, NARAYANAN V, et al. Neural Fiber Integrity in High- Versus Low-Grade Glioma using Probabilistic Fiber Tracking[J]. Acad Radiol, 2021, 28(12): 1721-1732. DOI: 10.1016/j.acra.2020.09.007.
[24]
BARAJAS R F, HESS C P, PHILLIPS J J, et al. Super-resolution track density imaging of glioblastoma: histopathologic correlation[J]. AJNR Am J Neuroradiol, 2013, 34(7): 1319-1325. DOI: 10.3174/ajnr.A3400.
[25]
STIKOV N, CAMPBELL J S, STROH T, et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging[J]. Neuroimage, 2015, 118: 397-405. DOI: 10.1016/j.neuroimage.2015.05.023.
[26]
PRYSIAZHNIUK Y, SERVER A, LESKE H, et al. Diffuse glioma molecular profiling with arterial spin labeling and dynamic susceptibility contrast perfusion MRI: A comparative study[J/OL]. Neurooncol Adv, 2024, 6(1): vdae113 [2026-02-28]. https://doi.org/10.1093/noajnl/vdae113. DOI: 10.1093/noajnl/vdae113.
[27]
TASCI E, ZHUGE Y, ZHANG L, et al. Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach[J/OL]. Diagnostics (Basel), 2025, 15(10): 1292 [2026-02-28]. https://doi.org/10.3390/diagnostics15101292. DOI: 10.3390/diagnostics15101292.
[28]
BUONAIUTO M, CUOMO M, COSTABILE D, et al. DNA methylation remodeling in temozolomide resistant recurrent glioblastoma: comparing epigenetic dynamics in vitro and in vivo[J/OL]. J Transl Med, 2025, 23(1): 779 [2026-02-28]. https://doi.org/10.1186/s12967-025-06767-x. DOI: 10.1186/s12967-025-06767-x.
[29]
MARTIN P, HOLLOWAY L, METCALFE P, et al. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation[J/OL]. Cancers (Basel), 2022, 14(16): 3897 [2026-02-28]. https://doi.org/10.3390/cancers14163897. DOI: 10.3390/cancers14163897.
[30]
HUANG L, LI Z, ZHU X, et al. Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T(2)-FLAIR: a multi-centre study[J/OL]. NPJ Digit Med, 2025, 8(1): 444 [2026-02-28]. https://doi.org/10.1038/s41746-025-01813-w. DOI: 10.1038/s41746-025-01813-w.
[31]
HUANG Z, LU C, LI G, et al. Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters[J/OL]. Front Oncol, 2021, 11: 627202 [2026-02-28]. https://doi.org/10.3389/fonc.2021.627202. DOI: 10.3389/fonc.2021.627202.
[32]
PROKOP G, WIESTLER B, HIEBER D, et al. Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma[J]. Int J Cancer, 2023, 153(9): 1658-1670. DOI: 10.1002/ijc.34665.
[33]
WEST J, SCHENCK R O, GATENBEE C, et al. Normal tissue architecture determines the evolutionary course of cancer[J/OL]. Nat Commun, 2021, 12(1): 2060 [2026-02-28]. https://doi.org/10.1038/s41467-021-22123-1. DOI: 10.1038/s41467-021-22123-1.
[34]
MALEKI E H, BAHRAMI A R, MATIN M M. Cancer cell cycle heterogeneity as a critical determinant of therapeutic resistance[J]. Genes Dis, 2024, 11(1): 189-204. DOI: 10.1016/j.gendis.2022.11.025.
[35]
SYGA S, JAIN H P, KRELLNER M, et al. Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy[J/OL]. PLoS Comput Biol, 2024, 20(8): e1012003[2026-02-28]. https://pubmed.ncbi.nlm.nih.gov/39121170/. DOI: 10.1371/journal.pcbi.1012003.

NEXT Hemodynamic characterization of left ventricular thrombus in myocardial infarction patients based on 4D Flow MRI
  


LINK


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