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• Clinical Article •
Conventional MRI features combined with T1WI enhanced histogram analysis to differentiate glioblastoma from grade Ⅳ astrocytoma
LUO Pan  HAN Tao  ZHANG Bin  CHEN Chunhui  LIU Qing  WANG Haisheng  ZHU Kaibo  ZHOU Junlin 

DOI:10.12015/issn.1674-8034.2025.11.014.


[Abstract] Objective To investigate the clinical value of conventional MRI features and T1WI enhancement histogram analysis in the preoperative non-invasive differentiation of isocitrate dehydrogenase (IDH) wild-type glioblastoma (IDH-wt GBM) and IDH mutant grade 4 astrocytoma (IDHmut-Astro-4).Materials and Methods A retrospective analysis was conducted on clinical, imaging, and pathological data from IDH-wt GBM (n = 44) and IDHmut-Astro-4 (n = 40) cases confirmed by histopathological diagnosis. Using FireVoxel software, the entire tumor was contoured layer by layer on axial T1WI enhanced images to obtain histogram parameters of the tumor region. Categorical variables were analyzed using chi-square tests or Fisher's exact tests, while continuous variables were analyzed using independent samples t-tests or Mann-Whitney U tests. Diagnostic performance between the two groups was assessed using receiver operating characteristic (ROC) curves.Results The IDH-wt GBM group, age (P < 0.001), tumor necrosis (P < 0.001), tumor enhancement degree (P = 0.021), and maximum diameter of peritumoral edema (P < 0.001) were all greater than those in the IDHmut-Astro-4 group. In the IDH-wt GBM group, the variance coefficient (P = 0.009), skewness (P = 0.002), kurtosis (P < 0.001), and entropy (P < 0.001) of the T1WI enhancement histogram parameters were all greater than those in the IDHmut-Astro-4 group, with statistically significant differences. ROC curve analysis showed that age + fusion of conventional MRI features + fusion of histogram parameters had the best diagnostic performance for distinguishing between the two groups, with an area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.962 (0.896 to 0.992), 86.36%, 92.50%, 89.26%, 92.68%, and 86.05%, respectively.Conclusions Conventional MRI features and T1WI-enhanced histogram analysis aid in the preoperative non-invasive differentiation of IDH-wt GBM and IDHmut-Astro-4, with the highest diagnostic efficacy achieved by the combination of age + fused conventional MRI features + fused histogram parameters.
[Keywords] isocitrate dehydrogenase wild-type；isocitrate dehydrogenase mutant type；glioblastoma；astrocytoma；magnetic resonance imaging；histogram analysis

LUO Pan1, 2, 3, 4   HAN Tao1, 2, 3, 4   ZHANG Bin1, 2, 3, 4   CHEN Chunhui1, 2, 3, 4   LIU Qing1, 2, 3, 4   WANG Haisheng1, 2, 3, 4   ZHU Kaibo1, 2, 3, 4   ZHOU Junlin1, 3, 4*  

1 Department of Radiology, the Second Hospital of Lanzhou University, Lanzhou 730000, China

2 Second Clinical School of Lanzhou University, Lanzhou 730000, China

3 Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China

4 Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China

Corresponding author: ZHOU J L, E-mail: ery_zhoujl@lzu.edu.cn

Conflicts of interest   None.

Received  2025-07-31
Accepted  2025-10-14
DOI: 10.12015/issn.1674-8034.2025.11.014
DOI:10.12015/issn.1674-8034.2025.11.014.

[1]
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]. Neuro Oncol, 2024, 26(Supplement_6): vi1-vi85. DOI: 10.1093/neuonc/noae145.
[2]
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.
[3]
ZHANG W, WANG Z. Chinese Anti-Cancer Association Guidelines for the Integrated Diagnosis and Treatment of Brain Glioma (Abridged Version)[J]. Chinese Journal of Clinical Oncology, 2022, 49(16): 811-818. DOI: 10.12354/j.issn.1000-8179.2022.20220728.
[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]
Chinese Society of Neuropathology, Chinese Medical Association. Chinese Expert Consensus on Molecular Pathological Diagnosis of Gliomas (2025 Edition)[J]. Chinese Journal of Pathology, 2025, 54(6): 580-592. DOI: 10.3760/cma.j.cn112151-20250212-00090.
[6]
ZHU X Y, ZOU W T, YOU D Y, et al. Value of Conventional and Functional Magnetic Resonance Imaging in Predicting IDH Subtypes of Brain Gliomas[J]. Chinese Journal of CT and MRI, 2025, 23(4): 24-27. DOI: 10.3969/j.issn.1672-5131.2025.04.008.
[7]
FANG P, ZHAO X M, WANG L N, et al. MRI-Based Differentiation of Grade IV Astrocytoma and Glioblastoma[J]. Chinese Journal of CT and MRI, 2023, 21(3): 9-11. DOI: 10.3969/j.issn.1672-5131.2023.03.004.
[8]
HAN T, LIU X W, JIANG J, et al. T1-weighted Image Enhancement Histogram Analysis for Differentiating Hemangiomatous Meningiomas from Atypical Meningiomas[J]. Chin J Magn Reson Imaging, 2024, 15(3): 37-42. DOI: 10.12015/issn.1674-8034.2024.03.007.
[9]
ZHANG B, ZHOU Q, ZHANG P, et al. MRI histogram and Visually AcceSAble Rembrandt Images features nomogram to predict PD-L1 levels in glioblastoma[J/OL]. Neurosurg Rev, 2025, 48(1): 371 [2025-07-31]. https://pubmed.ncbi.nlm.nih.gov/40257668/. DOI: 10.1007/s10143-025-03528-8.
[10]
SUN Y, YANG Z, DENG K, et al. Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas[J]. Quant Imaging Med Surg, 2023, 13(12): 8681-8693. DOI: 10.21037/qims-23-832.
[11]
CHEN L, WU M, LI Y, et al. Assessment of MGMT and TERT Subtypes and Prognosis of Glioblastoma by Whole Tumor Apparent Diffusion Coefficient Histogram Analysis[J/OL]. Brain Behav, 2025, 15(1): e70175 [2025-07-31]. https://doi.org/10.1002/brb3.70175. DOI: 10.1002/brb3.70175.
[12]
WANG X, ZHANG X, CHEN S, et al. Infectious disease-specific health literacy and its influencing factors: Research results based on a cross-sectional design study carried out in Shandong Province's rural areas[J/OL]. Medicine (Baltimore), 2025, 104(14): e42084 [2025-07-31]. https://doi.org/10.1097/md.0000000000042084. DOI: 10.1097/md.0000000000042084.
[13]
DOU Y, LIU Y Q, LI Y J. A Combined Model of Multi-parameter MRI Radiomics-Transformer Deep Learning Features Based on Intratumoral and Peritumoral Edema for Predicting IDH-1 Mutation Status in Low-Grade Gliomas[J]. Chin J Magn Reson Imaging, 2025, 16(9): 46-52, 59. DOI: 10.12015/issn.1674-8034.2025.09.008.
[14]
XU W, LI Y, ZHANG J, et al. Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model[J/OL]. BMC Cancer, 2025, 25(1): 1171 [2025-07-31]. https://doi.org/10.1186/s12885-025-14529-7. DOI: 10.1186/s12885-025-14529-7.
[15]
GE X, GAN T, YANG Z, et al. Whole-tumor histogram analysis of synthetic magnetic resonance imaging predicts isocitrate dehydrogenase mutation status in gliomas[J]. Quant Imaging Med Surg, 2024, 14(3): 2225-2239. DOI: 10.21037/qims-23-1288.
[16]
SUN Y F, YANG Z T, DENG K J, et al. QSM Three-Dimensional Histogram Analysis for Assessing IDH Genotype and Tumor Proliferative Activity in Adult Diffuse Gliomas[J]. Radiology Practice, 2023, 38(12): 1520-1525. DOI: 10.13609/j.cnki.1000-0313.2023.12.005.
[17]
NOTARANGELO G, SPINELLI J B, PEREZ E M, et al. Oncometabolite d-2HG alters T cell metabolism to impair CD8(+) T cell function[J]. Science, 2022, 377(6614): 1519-1529. DOI: 10.1126/science.abj5104.
[18]
SUN C, ZHAO Y, SHI J, et al. Isocitrate dehydrogenase1 mutation reduces the pericyte coverage of microvessels in astrocytic tumours[J]. J Neurooncol, 2019, 143(2): 187-196. DOI: 10.1007/s11060-019-03156-5.
[19]
WELLER M, VAN DEN BENT M, PREUSSER M, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood[J]. Nat Rev Clin Oncol, 2021, 18(3): 170-186. DOI: 10.1038/s41571-020-00447-z.
[20]
KANDEMIRLI S G, CHOPRA S, PRIYA S, et al. Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging[J/OL]. Clin Neurol Neurosurg, 2020, 198: 106205 [2025-07-31]. https://doi.org/10.1016/j.clineuro.2020.106205. DOI: 10.1016/j.clineuro.2020.106205.
[21]
XUE C, ZHOU Q, ZHANG P, et al. MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma[J/OL]. NeuVOImage Clin, 2023, 37: 103353 [2025-07-31]. https://doi.org/10.1016/j.nicl.2023.103353. DOI: 10.1016/j.nicl.2023.103353.
[22]
DI IEVA A, RUSSO C, LIU S, et al. Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario[J]. Neuroradiology, 2021, 63(8): 1253-1262. DOI: 10.1007/s00234-021-02649-3.
[23]
LEWIS M A, GANESHAN B, BARNES A, et al. Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping[J]. Eur J Radiol, 2019, 113: 116-123. DOI: 10.1016/j.ejrad.2019.02.014.
[24]
BAO S, WATANABE Y, TAKAHASHI H, et al. Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient[J]. Magn Reson Med Sci, 2019, 18(1): 53-61. DOI: 10.2463/mrms.mp.2017-0135.
[25]
LI C, WANG S, SERRA A, et al. Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma[J]. Eur Radiol, 2019, 29(9): 4718-4729. DOI: 10.1007/s00330-018-5984-z.
[26]
ZHANG B, ZHOU Q, XUE C, et al. Nomogram of magnetic resonance imaging (MRI) histogram analysis to predict telomerase reverse transcriptase promoter mutation status in glioblastoma[J]. Quant Imaging Med Surg, 2024, 14(7): 4840-4854. DOI: 10.21037/qims-24-71.
[27]
LIANG Q, LI Q, LIU X, et al. Noninvasive prediction of Ki-67 expression level in IDH-wildtype glioblastoma using MRI histogram analysis: comparison and combination of MRI morphological features[J/OL]. Front Oncol, 2025, 15: 1577816 [2025-07-31]. https://doi.org/10.3389/fonc.2025.1577816. DOI: 10.3389/fonc.2025.1577816.
[28]
LIU X, HAN T, WANG Y, et al. Prediction of O(6)-methylguanine-DNA methyltransferase promoter methylation status in IDH-wildtype glioblastoma using MRI histogram analysis[J/OL]. Neurosurg Rev, 2024, 47(1): 285 [2025-07-31]. https://doi.org/10.1007/s10143-024-02522-w. DOI: 10.1007/s10143-024-02522-w.
[29]
MEYER H J, LEIFELS L, HAMERLA G, et al. Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1α, and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma[J]. Mol Imaging Biol, 2019, 21(4): 740-746. DOI: 10.1007/s11307-018-1283-y.
[30]
JUST N. Improving tumour heterogeneity MRI assessment with histograms[J]. Br J Cancer, 2014, 111(12): 2205-2213. DOI: 10.1038/bjc.2014.512.
[31]
YILDIRIM M, BAYKARA M. Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma[J]. Acta Neurol Belg, 2022, 122(2): 363-368. DOI: 10.1007/s13760-021-01607-3.
[32]
LI Q, WANG T, HUANG Y, et al. Whole-Tumor Histogram and Texture Imaging Features on Magnetic Resonance Imaging Combined With Epstein-Barr Virus Status to Predict Disease Progression in Patients With Nasopharyngeal Carcinoma[J/OL]. Front Oncol, 2021, 11: 610804 [2025-07-31]. https://doi.org/10.3389/fonc.2021.610804. DOI: 10.3389/fonc.2021.610804.

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