Share:
Share this content in WeChat
X
Clinical Articles
Predictive value of DTI-ALPS combined with FA and standard MRI quantitative parameters for IDH mutation in adult-type diffuse high-grade gliomas
JIN Mingtian  XU Muyuan  HE Hairong  SU Chunqiu  HONG Kexuan  HONG Xunning 

Cite this article as JIN M T, XU M Y, HE H R, et al. Predictive value of DTI-ALPS combined with FA and standard MRI quantitative parameters for IDH mutation in adult-type diffuse high-grade gliomas[J]. Chin J Magn Reson Imaging, 2026, 17(8): 71-79. DOI:10.12015/issn.1674-8034.2026.08.007.


[Abstract] Objective To explore the predictive value of combining diffusion tensor image analysis along the perivascular space (DTI-ALPS), fractional anisotropy (FA) and standard MRI quantitative parameters for isocitrate dehydrogenase (IDH) mutation status in adult-type diffuse high-grade gliomas.Materials and Methods A total of 106 patients with adult-type diffuse high-grade gliomas who underwent standard MRI and diffusion tensor imaging (DTI) examinations and were definitively diagnosed by postoperative histopathological examination were enrolled in this retrospective analysis. The clinical, imaging and pathological data of all patients were collected. Standard MRI quantitative parameters were obtained by layer-by-layer delineation of the entire tumor using 3D-Slicer software. DSI studio software was applied to calculate FA and mean diffusivity (MD) in the tumor and peritumoral edema regions, as well as the ALPS index on the ipsilateral and contralateral sides of the tumor. Clinical data, standard MRI quantitative parameters, DTI metrics and ALPS index were compared between IDH wild-type and IDH-mutant groups. Univariate and multivariate analyses were performed for indicators with statistically significant intergroup differences. Multivariate binary logistic regression analysis was used to identify the optimal diagnostic parameters for predicting IDH mutation status. Receiver operating characteristic (ROC) curves were plotted to evaluate diagnostic efficiency.Results Tumor FA, tumor MD, ipsilateral ALPS index, contralateral ALPS index, tumor parenchymal volume, peritumoral edema volume, tumor-to-edema volume ratio and age were significantly different between the IDH wild-type and IDH-mutant groups (P < 0.05). Multivariate binary logistic regression analysis revealed that age, tumor parenchymal volume, peritumoral edema volume, tumor FA and ipsilateral ALPS index were independent predictors of IDH mutation status. DeLong test indicated that the ipsilateral ALPS index and tumor FA exerted distinct incremental predictive values based on corresponding baseline models. ROC curve analysis demonstrated that the combined model incorporating age, standard MRI quantitative parameters, DTI metrics and ALPS index achieved the optimal predictive performance for IDH mutation status in adult-type diffuse high-grade gliomas. The area under the curve (AUC) was 0.952, with a sensitivity of 89.0%, specificity of 97.0% and accuracy of 93.4%. After 1000 bootstrap internal validation, the corrected 95% confidence interval (CI) of AUC was 0.879 to 0.995. The area under the precision-recall curve (PR-AUC) was 0.933, and the Brier score was 0.062, suggesting favorable model calibration.Conclusions DTI-ALPS combined with FA and standard MRI quantitative parameters shows good efficacy in predicting IDH mutation in adult-type diffuse high-grade gliomas. Ipsilateral ALPS index and tumor FA yield extra diagnostic value and can serve as imaging evidence for preoperative assessment and individualized treatment.
[Keywords] gliomas;magnetic resonance imaging;diffusion tensor imaging;diffusion tensor image analysis along the perivascular space;isocitrate dehydrogenase

JIN Mingtian   XU Muyuan   HE Hairong   SU Chunqiu   HONG Kexuan   HONG Xunning*  

Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China

Corresponding author: HONG X N, E-mail: hongxunning@sina.com

Conflicts of interest   None.

Received  2026-04-02
Accepted  2026-07-14
DOI: 10.12015/issn.1674-8034.2026.08.007
Cite this article as JIN M T, XU M Y, HE H R, et al. Predictive value of DTI-ALPS combined with FA and standard MRI quantitative parameters for IDH mutation in adult-type diffuse high-grade gliomas[J]. Chin J Magn Reson Imaging, 2026, 17(8): 71-79. DOI:10.12015/issn.1674-8034.2026.08.007.

[1]
WELLER M, WEN P Y, CHANG S M, et al. Glioma[J/OL]. Nat Rev Dis Primers, 2024, 10(1): 33 [2026-01-30]. https://doi.org/10.1038/s41572-024-00516-y. DOI: 10.1038/s41572-024-00516-y.
[2]
HU X Q, ZHANG H. Advances in the treatment of high-grade gliomas[J]. Cancer Progress, 2024, 22(9): 944-948, 955. DOI: 10.11877/j.issn.1672-1535.2024.22.09.03.
[3]
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[4]
GRITSCH S, BATCHELOR T T, GONZALEZ CASTRO L N. Diagnostic, therapeutic, and prognostic implications of the 2021 World Health Organization classification of tumors of the central nervous system[J]. Cancer, 2022, 128(1): 47-58. DOI: 10.1002/cncr.33918.
[5]
LALLY A R, GHOSH S R, PECORARI I L, et al. Do the benefits of IDH mutations in high-grade glioma persist beyond the first recurrence? A multi-institutional retrospective analysis[J]. J Neurooncol, 2025, 174(1): 167-175. DOI: 10.1007/s11060-025-05049-2.
[6]
BERGER T R, WEN P Y, LANG-ORSINI M, et al. World Health Organization 2021 Classification of Central Nervous System Tumors and Implications for Therapy for Adult-Type Gliomas: A Review[J]. JAMA Oncol, 2022, 8(10): 1493-1501. DOI: 10.1001/jamaoncol.2022.2844.
[7]
GUARNERA A, IUS T, ROMANO A, et al. Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?[J/OL]. Medicina (Kaunas), 2025, 61(8): 1453 [2026-01-30]. https://doi.org/10.3390/medicina61081453. DOI: 10.3390/medicina61081453.
[8]
WANG Q, ZHANG J, LI F, et al. Diagnostic performance of clinical properties and conventional magnetic resonance imaging for determining the IDH1 mutation status in glioblastoma: a retrospective study[J/OL]. PeerJ, 2019, 7: e7154 [2026-01-30]. https://doi.org/10.7717/peerj.7154. DOI: 10.7717/peerj.7154.
[9]
HONG E K, CHOI S H, SHIN D J, et al. Comparison of Genetic Profiles and Prognosis of High-Grade Gliomas Using Quantitative and Qualitative MRI Features: A Focus on G3 Gliomas[J]. Korean J Radiol, 2021, 22(2): 233-242. DOI: 10.3348/kjr.2020.0011.
[10]
LI Y, ZHANG W. Quantitative evaluation of diffusion tensor imaging for clinical management of glioma[J]. Neurosurg Rev, 2020, 43(3): 881-891. DOI: 10.1007/s10143-018-1050-1.
[11]
HAN X, LU J. Advances in quantitative analysis of diffusion tensor imaging in glioma grading and molecular typing[J]. Chin J Magn Reson Imaging, 2024, 15(8): 201-206. DOI: 10.12015/issn.1674-8034.2024.08.032.
[12]
WU X Y, WU Y K. Research progress on predicting the grade and genotype of brain gliomas using MR diffusion tensor imaging[J]. Chin J Magn Reson Imaging, 2024, 15(6): 190-195. DOI: 10.12015/issn.1674-8034.2024.06.030.
[13]
TAOKA T, MASUTANI Y, KAWAI H, et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer's disease cases[J]. Jpn J Radiol, 2017, 35(4): 172-178. DOI: 10.1007/s11604-017-0617-z.
[14]
LIAN X Y, GAO X, LIU X H, et al. Advances in the application of diffusion tensor imaging analysis along perivascular spaces in brain-related lymphatic system diseases[J]. International Journal of Medical Radiology, 2024, 47(1): 37-42. DOI: 10.19300/j.2024.Z21094.
[15]
FIALLO ARROYO J, LEON-ROJAS J E. The Glymphatic-Immune Axis in Glioblastoma: Mechanistic Insights and Translational Opportunities[J/OL]. Int J Mol Sci, 2026, 27(2): 928 [2026-01-30]. https://doi.org/10.3390/ijms27020928. DOI: 10.3390/ijms27020928.
[16]
XU D, ZHOU J, MEI H, et al. Impediment of Cerebrospinal Fluid Drainage Through Glymphatic System in Glioma[J/OL]. Front Oncol, 2022, 11: 790821 [2026-01-30]. https://doi.org/10.3389/fonc.2021.790821. DOI: 10.3389/fonc.2021.790821.
[17]
CASTAÑEYRA-RUIZ L, GONZÁLEZ-MARRERO I, GARCÍA-ABAD L H, et al. Aquaporin-4 in glioblastoma: a nexus of glymphatic dysfunction, edema, immune evasion, and treatment resistance[J/OL]. Front Cell Neurosci, 2025, 19: 1685491 [2026-01-30]. https://doi.org/10.3389/fncel.2025.1685491. DOI: 10.3389/fncel.2025.1685491.
[18]
VILLACIS G, SCHMIDT A, RUDOLF J C, et al. Evaluating the glymphatic system via magnetic resonance diffusion tensor imaging along the perivascular spaces in brain tumor patients[J]. Jpn J Radiol, 2024, 42(10): 1146-1156. DOI: 10.1007/s11604-024-01602-7.
[19]
ZENG S, HUANG Z, ZHOU W, et al. Noninvasive evaluation of the glymphatic system in diffuse gliomas using diffusion tensor image analysis along the perivascular space[J]. J Neurosurg, 2024, 142(1): 187-196. DOI: 10.3171/2024.4.JNS232724.
[20]
ZHU H, XIE Y, LI L, et al. Diffusion along the perivascular space as a potential biomarker for glioma grading and isocitrate dehydrogenase 1 mutation status prediction[J]. Quant Imaging Med Surg, 2023, 13(12): 8259-8273. DOI: 10.21037/qims-23-541.
[21]
LUO P, HAN T, ZHANG B, et al. Differential diagnosis of glioblastoma and grade 4 astrocytoma based on conventional MRI features combined with T1WI enhanced histogram analysis[J]. Chin J Magn Reson Imaging, 2025, 16(11): 94-100. DOI: 10.12015/issn.1674-8034.2025.11.014.
[22]
KIKUCHI M, TAKAMI H, KOBAYASHI Y, et al. Dissecting the Immunological Microenvironment of Glioma Based on IDH Status: Implications for Immunotherapy[J/OL]. Cells, 2025, 14(13): 1035 [2026-01-30]. https://doi.org/10.3390/cells14131035. DOI: 10.3390/cells14131035.
[23]
AHMAD O, AHMAD T, PFISTER S M. IDH mutation, glioma immunogenicity, and therapeutic challenge of primary mismatch repair deficient IDH-mutant astrocytoma PMMRDIA: a systematic review[J]. Mol Oncol, 2024, 18(12): 2822-2841. DOI: 10.1002/1878-0261.13598.
[24]
KRÓLIKOWSKA K, BŁASZCZAK K, ŁAWICKI S, et al. Glioblastoma-A Contemporary Overview of Epidemiology, Classification, Pathogenesis, Diagnosis, and Treatment: A Review Article[J/OL]. Int J Mol Sci, 2025, 26(24): 12162 [2026-01-30]. https://doi.org/10.3390/ijms262412162. DOI: 10.3390/ijms262412162.
[25]
DUBINSKI D, WON S Y, RAUCH M, et al. Association of Isocitrate Dehydrogenase (IDH) Status With Edema to Tumor Ratio and Its Correlation With Immune Infiltration in Glioblastoma[J/OL]. Front Immunol, 2021, 12: 627650 [2026-01-30]. https://doi.org/10.3389/fimmu.2021.627650. DOI: 10.3389/fimmu.2021.627650.
[26]
SONG Z Z. Advances in the study of the pathogenesis of peritumoral edema and its relationship with imaging[J]. Imaging Research and Medical Applications, 2025, 9(14): 6-10. DOI: 10.20267/j.issn.2096-3807.2025.14.002.
[27]
OHMURA K, TOMITA H, HARA A. Peritumoral Edema in Gliomas: A Review of Mechanisms and Management[J/OL]. Biomedicines. 2023, 11(10): 2731 [2026-01-30]. https://doi.org/10.3390/biomedicines11102731. DOI: 10.3390/biomedicines11102731.
[28]
FIGINI M, RIVA M, GRAHAM M, et al. Prediction of Isocitrate Dehydrogenase Genotype in Brain Gliomas with MRI: Single-Shell versus Multishell Diffusion Models[J]. Radiology, 2018, 289(3): 788-796. DOI: 10.1148/radiol.2018180054.
[29]
LIANG Y X, SHANG Y, REN Y H, et al. Prediction of glioma isocitrate dehydrogenase status based on diffusion tensor imaging parameters and clinical characteristics[J]. Journal of Practical Radiology, 2024, 40(3): 347-351. DOI: 10.3969/j.issn.1002-1671.2024.03.001.
[30]
YUAN J, SIAKALLIS L, LI H B, et al. Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach[J/OL]. BMC Med Imaging, 2024, 24(1): 104 [2026-01-30]. https://doi.org/10.1186/s12880-024-01274-9. DOI: 10.1186/s12880-024-01274-9.
[31]
LIANG W, SUN W, LI C, et al. Glymphatic system dysfunction and cerebrospinal fluid retention in gliomas: evidence from perivascular space diffusion and volumetric analysis[J/OL]. Cancer Imaging, 2025, 25(1): 51 [2026-01-30]. https://doi.org/10.1186/s40644-025-00868-y. DOI: 10.1186/s40644-025-00868-y.
[32]
TIAN B, JIANG X, LUO X, et al. Analysis of the glymphatic system function in high-grade glioma patients using diffusion tensor imaging along perivascular spaces[J/OL]. BMC Neurol, 2025, 25(1): 181 [2026-01-30]. https://doi.org/10.1186/s12883-025-04166-9. DOI: 10.1186/s12883-025-04166-9.
[33]
WANG L, LIU L, ZHANG H, et al. Diffusion tensor imaging changes along the perivascular spaces may serve as a prognostic factor for high-grade glioma[J/OL]. Eur J Radiol, 2025, 184: 111993 [2026-01-30]. https://doi.org/10.1016/j.ejrad.2025.111993. DOI: 10.1016/j.ejrad.2025.111993.
[34]
FANG K, WANG C, ZHANG Y, et al. Noninvasive assessment of glymphatic dysfunction and IDH mutation in glioma with DTI-ALPS and DTI metrics[J]. Quant Imaging Med Surg, 2025, 15(6): 5007-5022. DOI: 10.21037/qims-2024-2710.
[35]
LI S, CHEN R, CAO Z, et al. Microstructural Bias in the Assessment of Periventricular Flow as Revealed in Postmortem Brains[J/OL]. Radiology, 2025, 316(3): e250753 [2026-01-30]. https://doi.org/10.1148/radiol.250753. DOI: 10.1148/radiol.250753.
[36]
MOSSIGE I, VALNES LM, STORÅS TH, et al. Comparing Glymphatic Function Measures: Diffusion Tensor Image Analysis Along Perivascular Spaces (DTI-ALPS) versus Intrathecal Contrast-Enhanced MRI[J/OL]. Radiology, 2026, 318(2): e252070 [2026-01-30]. https://doi.org/10.1148/radiol.252070. DOI: 10.1148/radiol.252070.
[37]
OISHI T, KOIZUMI S, KUROZUMI K. Molecular Mechanisms and Clinical Challenges of Glioma Invasion[J/OL]. Brain Sci, 2022, 12(2): 291 [2026-01-30]. https://doi.org/10.3390/brainsci12020291. DOI: 10.3390/brainsci12020291.
[38]
SEKER-POLAT F, PINARBASI DEGIRMENCI N, SOLAROGLU I, et al. Tumor Cell Infiltration into the Brain in Glioblastoma: From Mechanisms to Clinical Perspectives[J/OL]. Cancers (Basel), 2022, 14(2): 443 [2026-01-30]. https://doi.org/10.3390/cancers14020443. DOI: 10.3390/cancers14020443.
[39]
COZZI F M, MAYRAND R C, WAN Y, et al. Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review[J/OL]. J Neuroimaging, 2025, 35(1): e13251 [2026-01-30]. https://doi.org/10.1111/jon.13251. DOI: 10.1111/jon.13251.
[40]
READ R D, TAPP Z M, RAJAPPA P, et al. Glioblastoma microenvironment-from biology to therapy[J]. Genes Dev, 2024, 38(9-10): 360-379. DOI: 10.1101/gad.351427.123.
[41]
LEROY H A, LACOSTE M, MAURAGE C A, et al. Anatomo-radiological correlation between diffusion tensor imaging and histologic analyses of glial tumors: a preliminary study[J]. Acta Neurochir (Wien), 2020, 162(7): 1663-1672. DOI: 10.1007/s00701-020-04323-8.
[42]
HAN X, LI X R, LU J. Prediction of IDH phenotype in adult-type diffuse gliomas using an integrated machine learning model combining MRI visual features and DTI histogram parameters[J]. Chin J Magn Reson Imaging, 2024, 15(11): 51-59, 89. DOI: 10.12015/issn.1674-8034.2024.11.009.
[43]
WHITE M L, ZHANG Y, YU F, et al. Diffusion tensor MR imaging of cerebral gliomas: evaluating fractional anisotropy characteristics[J]. AJNR Am J Neuroradiol, 2011, 32(2): 374-381. DOI: 10.3174/ajnr.A2267.
[44]
LI Y, WANG J, SONG S R, et al. Models for evaluating glioblastoma invasion along white matter tracts[J]. Trends Biotechnol, 2024, 42(3): 293-309. DOI: 10.1016/j.tibtech.2023.09.005.
[45]
GAO M, ZANG H, ZHAO W, et al. Unraveling glymphatic disruption in brain tumors: Insights from perivascular space network analysis[J]. Int J Cancer, 2026, 158(11): 2995-3006. DOI: 10.1002/ijc.70441.
[46]
HAGIWARA A, UCHIDA W, OZAWA T, et al. Contralateral neurofluid dynamics predict survival in IDH wild-type glioblastoma: A DTI-ALPS and free water imaging study[J]. Neuro-Oncol, 2026, 28(1): 299-310. DOI: 10.1093/neuonc/noaf242.

PREV Cerebral blood flow perfusion characteristics in preterm infants with bronchopulmonary dysplasia based on 3D-ASL and its correlation with duration of mechanical ventilation
NEXT Predictive value of normalized epicardial adipose tissue volume for left ventricular reverse remodeling in dilated cardiomyopathy based on CMR
  



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