The Role of MRI in Diagnosing Brain Tumors

• Nagwa R. Bochwal

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Huda M. Ibrahim

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Eman.Y. Abdelsameh

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Wfa. A. A. Nweh

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Retaj.N. saad

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Salwa.F. Ahmed

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Najat.S. Shref

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Retaj. M. Jomaa

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Firdous N. Saad

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Haneen F. Awad

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

• Nedaa M. Mohammed

  College of Health Sciences, University of Tobruk, Tobruk, Libya

  Author

Background: Magnetic resonance imaging (MRI) serves as a cornerstone diagnostic tool for intracranial neoplasms, owing to its superior tissue contrast resolution and spatial definition. Early identification of lesions is critical to optimizing therapeutic strategies and enhancing overall survival rates. Aim: To assess the diagnostic utility of MRI in detecting, characterizing, and localizing brain tumors. Methods: This cross-sectional study evaluated 45 individuals suspected of harboring brain tumors at Tobruk Medical Center between November 2025 and January 2026. Scans were performed using a 1.5-Tesla MRI system employing standard T1-weighted, T2-weighted, and FLAIR protocols, with intravenous gadolinium contrast administered as clinically indicated. Data extracted from radiological archives and clinical records were processed via Microsoft Excel. Results: Among the 45 participants (age range: 18–80 years), males comprised 51.1% and females 48.9%, with a predominant occurrence in individuals over 40 years old. Utilizing a combined protocol of T1, T2, and FLAIR sequences markedly enhanced lesion detection, whereas contrast-enhanced imaging provided superior definition of tumor borders and vascularity. A substantial majority of patients were diagnosed at advanced disease stages. Conclusion: MRI remains a highly reliable imaging technique for evaluating brain tumors and guiding clinical management. Nevertheless, delayed clinical presentation substantially limits its efficacy. Enhancing diagnostic awareness and radiological training is essential to improve outcomes

Afridi M., Jain A., Aboian M., Payabvash S. (2022). Brain Tumor Imaging: Applications of Artificial Intelligence. Seminars in Ultrasound, CT and MRI, 43(2), 153–169. doi: 10.1053/J.SULT.2022.02.005. DOI: https://doi.org/10.1053/j.sult.2022.02.005

Amin J., Sharif M., Gul N., Yasmin M., Shad S.A. (2020). Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognition Letters, 129, 115–122. doi: 10.1016/J.PATREC.2019.11.016. DOI: https://doi.org/10.1016/j.patrec.2019.11.016

Clifton W., Reimer R. (2019). Metastatic brain tumors. In: Comprehensive Overview of Modern Surgical Approaches to Intrinsic Brain Tumors. Elsevier: Academic Press, pp.159–167. doi:10.1016/B978-0-12-811783-5.00008-2. DOI: https://doi.org/10.1016/B978-0-12-811783-5.00008-2

Dekker, A. and Sijbers, J. (2005). Advanced Image Processing in Magnetic Resonance Imaging. CRC Press, Taylor and Francis Group.

Fraioli F., Punwani S. (2014). Clinical and research applications of simultaneous positron emission tomography and MRI. British Journal of Radiology, 87(1033). doi:10.1259/bjr.20130464. DOI: https://doi.org/10.1259/bjr.20130464

Hill C.I., Nixon C.S., Ruehmeier J.L., Wolf L.M. (2002). Brain tumors. Physical Therapy, 82(5), 496–502. doi:10.1093/PTJ/82.5.496. DOI: https://doi.org/10.1093/ptj/82.5.496

Jelski W., Mroczko B. (2021). Molecular and circulating biomarkers of brain tumors. International Journal of Molecular Sciences, 22(13), 7039. DOI: https://doi.org/10.3390/ijms22137039

Kumar A., Ramachandran M., Gandomi A.H., Patan R., Lukasik S., Soundarapandian R.K. (2019). A deep neural network based classifier for brain tumor diagnosis. Applied Soft Computing, 82, 105528. DOI: https://doi.org/10.1016/j.asoc.2019.105528

Lapointe S., Perry A., Butowski N.A. (2018). Primary brain tumours in adults. The Lancet, 392(10145), 432–446. DOI: https://doi.org/10.1016/S0140-6736(18)30990-5

Lecler A., Charbonneau F., Psimaras D., Metten M.A., Gueguen A., Xuan K.H., et al. (2018). Remote brain microhaemorrhages may predict haematoma in glioma patients treated with radiation therapy. European Radiology, 28(10), 4324–4333. DOI: https://doi.org/10.1007/s00330-018-5356-8

Mehta S., Shah A., Jung H. (2017). Diagnosis and treatment options for sequelae following radiation treatment of brain tumors. Clinical Neurology and Neurosurgery, 163, 1–. DOI: https://doi.org/10.1016/j.clineuro.2017.09.010

Nelson S., Taylor L.P. (2014). Headaches in brain tumor patients: Primary or secondary? Headache: The Journal of Head and Face Pain, 54(4), 776–785. DOI: https://doi.org/10.1111/head.12326

Nibib.nih.gov. (2016). Magnetic Resonance Imaging (MRI) | National Institute of Biomedical Imaging and Bioengineering. [online] Available at: https://www.nibib.nih.gov/science-education/science-topics/magnetic-resonance-imaging-mri [Accessed: 01 July, 2017].

Patient. (2016). Magnetic Resonance Imaging. [online] Available at: http://patient.info/doctor/magnetic-resonance-imaging [Accessed: 03 June. 2017].

Revett K. (2011). An Introduction to Magnetic Resonance Imaging: From Image Acquisition to Clinical Diagnosis. Innovations in Intelligent Image Analysis, 127–161. DOI: https://doi.org/10.1007/978-3-642-17934-1_7

Rigi M., Almarzouqi S.J., Morgan M.L., Lee A.G. (2015). Papilledema: Epidemiology, etiology, and clinical management. Eye and Brain, 7, 47. DOI: https://doi.org/10.2147/EB.S69174

Saman S., Jamjala Narayanan S. (2018). Survey on brain tumor segmentation and feature extraction of MR images. International Journal of Multimedia Information Retrieval, 8(2), 79–99. doi:10.1007/S13735-018-0162-2. DOI: https://doi.org/10.1007/s13735-018-0162-2

Udaka Y.T., Packer R.J. (2018). Pediatric brain tumors. Neurologic Clinics, 36(3), 533–536. doi:10.1016/j.ncl.2018.04.009. DOI: https://doi.org/10.1016/j.ncl.2018.04.009

Vargo M. (2011). Brain tumor rehabilitation. American Journal of Physical Medicine & Rehabilitation, 90(5), S50–S62. DOI: https://doi.org/10.1097/PHM.0b013e31820be31f

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