Current Advances in Brain Mri for The Diagnosis of Glioblastoma: the Role of Tractography and Mr Spectroscopy

Alisher Ibrokhimov Arslonboy oʻgʻli; Otabek Vakhobovich Ablyazov

Authors

Alisher Ibrokhimov Arslonboy oʻgʻli Tashkent State Medical University, Tashkent, Uzbekistan
Otabek Vakhobovich Ablyazov Tashkent State Medical University, Tashkent, Uzbekistan

Keywords:

glioblastoma, MRI, tractography, magnetic resonance spectroscopy, neuroimaging, brain tumors, speckle tracking, perfusion imaging, deep learning, intraoperative MRI

Abstract

Dental Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, characterized by rapid infiltrative growth, a poor prognosis, and a high rate of recurrence. Magnetic resonance imaging (MRI) remains the gold standard for non-invasive diagnosis, treatment planning, and postoperative monitoring of GBM. This review examines current advances in MRI-based neuroimaging of glioblastoma, with emphasis on tractography, magnetic resonance spectroscopy (MRS), perfusion imaging, automated segmentation, deep learning-assisted diagnosis, and intraoperative MRI. A synthesis of recent literature demonstrates that multiparametric and advanced MRI techniques substantially improve diagnostic accuracy, facilitate safer maximal surgical resection, and support the differentiation of tumor progression from treatment-related changes, thereby contributing to improved patient outcomes.

References

J. Madour, J. Bessette, J. Z. Gu, E. L. Thayer, H. Elinzano, and A. J. Sax, "Glioblastoma: Epidemiology and imaging-based review," Rhode Island Medical Journal, vol. 108, no. 8, pp. 50–56, 2025.

G. Shukla, G. S. Alexander, S. Bakas, R. Nikam, K. Talekar, J. D. Palmer, and W. Shi, "Advanced magnetic resonance imaging in glioblastoma: a review," Chinese Clinical Oncology, vol. 6, no. 4, p. 40, 2017, doi: 10.21037/cco.2017.06.28.

F. G. Gonçalves, S. Chawla, and S. Mohan, "Emerging MRI techniques to redefine treatment response in patients with glioblastoma," Journal of Magnetic Resonance Imaging, vol. 52, no. 4, pp. 978–997, 2020, doi: 10.1002/jmri.27105.

K. Scheu et al., "MRI-defined patterns of infiltration and outcome in patients with glioblastoma," Neuro-Oncology Advances, vol. 7, no. 1, p. vdaf114, 2025, doi: 10.1093/noajnl/vdaf114.

S. De Sutter, J. Wuts, W. Geens, A. M. Vanbinst, J. Duerinck, and J. Vandemeulebroucke, "Modality redundancy for MRI-based glioblastoma segmentation," International Journal of Computer Assisted Radiology and Surgery, vol. 19, no. 10, pp. 2101–2109, 2024, doi: 10.1007/s11548-024-03238-4.

C. H. Suh, H. S. Kim, S. C. Jung, J. E. Park, C. G. Choi, and S. J. Kim, "MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta-analysis," Journal of Magnetic Resonance Imaging, vol. 50, no. 2, pp. 560–572, 2019, doi: 10.1002/jmri.26602.

C. Kong, D. Yan, K. Liu, Y. Yin, and C. Ma, "Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study," BMC Medical Imaging, vol. 25, no. 1, p. 171, 2025, doi: 10.1186/s12880-025-01703-3.

P. Familiari, A. Frati, A. Pesce, M. Miscusi, M. Cimatti, and A. Raco, "Real impact of intraoperative magnetic resonance imaging in newly diagnosed glioblastoma multiforme resection: an observational analytic cohort study from a single surgeon experience," World Neurosurgery, vol. 116, pp. e9–e17, 2018, doi: 10.1016/j.wneu.2017.12.176.

R. S. D’Amico, B. C. Kennedy, and J. N. Bruce, "Neurosurgical oncology: advances in operative technologies and adjuncts," Journal of Neuro-Oncology, vol. 119, no. 3, pp. 451–463, 2014, doi: 10.1007/s11060-014-1493-3.

I. Khan, M. Waqas, and M. S. Shamim, "Role of intra-operative MRI (iMRI) in improving extent of resection and survival in patients with glioblastoma multiforme," JPMA: The Journal of the Pakistan Medical Association, vol. 67, no. 7, pp. 1121–1123, 2017.

C. N. Kersch, P. Ambady, B. E. Hamilton, and R. F. Barajas Jr., "MRI and PET of brain tumor neuroinflammation in the era of immunotherapy," American Journal of Roentgenology, vol. 218, no. 4, pp. 582–596, 2022, doi: 10.2214/AJR.21.26159.

D. Schomas et al., "Glioblastoma imaging biomarkers for recurrence prediction: a prospective multicenter study," Neuro-Oncology, vol. 22, no. 5, pp. 675–684, 2020, doi: 10.1093/neuonc/noaa003.

T. Batchala et al., "Neuroimaging-based classification algorithm for predicting IDH-mutation status in glioblastoma," American Journal of Neuroradiology, vol. 40, no. 3, pp. 426–432, 2019, doi: 10.3174/ajnr.A5957.

M. López-González et al., "Diffusion tensor imaging and white matter tractography in glioblastoma: systematic review and meta-analysis of preoperative planning utility," Journal of Neurosurgery, vol. 136, no. 2, pp. 382–393, 2022, doi: 10.3171/2021.1.JNS203348.

B. Ellingson et al., "Consensus recommendations for standardized brain tumor imaging protocols in clinical trials," Neuro-Oncology, vol. 17, no. 9, pp. 1188–1198, 2015, doi: 10.1093/neuonc/nov095.