Integration of Artificial Intelligence in Magnetic Resonance Imaging analysis and Liquid Biopsy in diagnosing and monitoring Glioblastoma Multiforme - A Systematic Review
DOI:
https://doi.org/10.12775/QS.2026.54.70320Keywords
Glioblastoma, Glioblastoma Multiforme, molecular mechanisms, pathogenesis, magnetic resonance, artificial intelligence, radiomics, diagnosticsAbstract
Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor among adults and is associated with rapid progressions and poor prognosis. Modern diagnostic techniques, including molecular biology, have improved the perception of disease development emphasizing the role of disturbed signaling pathways, genetic mutations and tumor microenvironment.
The following review aims to conclude current comprehension of molecular mechanisms leading to GBM development and contemporary diagnostic and therapeutic approaches. Articles present in PubMed database published from 2022 onwards were researched to perform analysis of tumor biology, subcellular mechanisms and diagnostic possibilities.
Magnetic resonance imaging (MRI) remains the most fundamental diagnostic method which enhanced by modern approaches such as radiomics and artificial intelligence (AI) supports proper diagnosis and enables tumor characteristics analysis. Moreover, combining imaging with modern pathomorphological methods such as liquid biopsy can lead to increased accuracy and personalization in diagnostics.
To conclude, GBM development is a complex process combining genetic alterations and disturbed molecular pathways that can be detected using modern diagnostic techniques. Advanced pathomorphological methods and AI-supported imaging lead to accurate diagnosis, proper therapeutic decisions and better patient outcome.
References
1. Agosti E, Mapelli K, Grimod G, Piazza A, Fontanella MM, Panciani PP. (2026). MRI-Based Radiomics for Non-Invasive Prediction of Molecular Biomarkers in Gliomas. Cancers (Basel);18(3):491. doi: 10.3390/cancers18030491
2. Ahangari G, Norioun H, Ghaemi S, Zali A. (2025). Artificial intelligence in glioblastoma diagnostics: integrating MRI, histopathology, and molecular profiling. Cancer Treat Res Commun;45:101040. doi: 10.1016/j.ctarc.2025.101040
3. Alizadeh M, Lomer NB, Azami M, Khalafi M, Shobeiri P, Bafrani MA, Sotoudeh H. (2023). Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme. Cancers (Basel);15(18):4429. doi: 10.3390/cancers15184429
4. Angom RS, Nakka NMR, Bhattacharya S. (2023). Advances in glioblastoma therapy: An update on current approaches. Brain Sci;13(11):1536. doi: 10.3390/brainsci13111536
5. Bijari S, Jahanbakhshi A, Hajishafiezahramini P, Abdolmaleki P. (2022). Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models. Biomed Res Int;2022:2016006. doi: 10.1155/2022/2016006
6. Chelliah A, Wood DA, Canas LS, Shuaib H, Currie S, Fatania K, et al. (2024). Glioblastoma and radiotherapy: A multicenter AI study for survival predictions from MRI (GRASP study). Neuro Oncol;26(6):1138–1151. doi: 10.1093/neuonc/noae017
7. Contreras K, Velez-Varela PE, Casanova-Carvajal O, Alvarez AL, Urbano-Bojorge AL. (2025). A review of artificial intelligence-based systems for non-invasive glioblastoma diagnosis. Life (Basel);15(4):643. doi: 10.3390/life15040643
8. Corr F, Grimm D, Saß B, Pojskić M, Bartsch JW, Carl B, Nimsky C, Bopp M. (2022). Radiogenomic predictors of recurrence in glioblastoma: A systematic review. J Pers Med;12(3):402. doi: 10.3390/jpm12030402
9. Czarnywojtek A, Borowska M, Dyrka K, Van Gool S, Sawicka-Gutaj N, Moskal J. (2023). Glioblastoma Multiforme: The Latest Diagnostics and Treatment Techniques. Pharmacology;108(5):423–431. doi: 10.1159/000531319
10. Eibl RH, Schneemann M. (2023). Liquid biopsy and glioblastoma. Explor Target Antitumor Ther;4(1):28–41. doi: 10.37349/etat.2023.00121
11. Hooper GW, Ansari S, Johnson JM, Ginat DT. (2023). Advances in the Radiological Evaluation of and Theranostics for Glioblastoma. Cancers (Basel);15(16):4162. doi: 10.3390/cancers15164162
12. Khabibov M, Garifullin A, Boumber Y, Khaddour K, Fernandez M, Khamitov F, et al. (2022). Signaling pathways and therapeutic approaches in glioblastoma multiforme. Int J Oncol;60(6):69. doi: 10.3892/ijo.2022.5359
13. Królikowska K, Błaszczak K, Ławicki S, Zajkowska M, Gudowska-Sawczuk M. (2025). Glioblastoma—A contemporary overview of epidemiology, classification, pathogenesis, diagnosis, and treatment. Int J Mol Sci;26(24):12162. doi: 10.3390/ijms262412162
14. Kwiatkowska-Miernik A, Mruk B, Sklinda K, Zaczyński A, Walecki J. (2023). Radiomics in the diagnosis of glioblastoma. Pol J Radiol;88:e461–e466. doi: 10.5114/pjr.2023.132168
15. Kumari S, Gupta R, Ambasta RK, Kumar P. (2023). Multiple therapeutic approaches of glioblastoma multiforme: From terminal to therapy. Biochim Biophys Acta Rev Cancer;1878(4):188913. doi: 10.1016/j.bbcan.2023.188913
16. Makowska M, Smolarz B, Romanowska H. (2023). MicroRNAs in Glioblastoma Multiforme – Recent Literature Review. Int J Mol Sci;24(4):3521. doi: 10.3390/ijms24043521
17. Pouyan A, Ghorbanlo M, Eslami M, Jahanshahi M, Ziaei E, Salami A, et al. (2025). Glioblastoma multiforme: insights into pathogenesis, key signaling pathways, and therapeutic strategies. Mol Cancer;24:58. doi: 10.1186/s12943-025-02267-0
18. Sadowski K, Jażdżewska A, Kozłowski J, Zacny A, Lorenc T, Olejarz W. (2024). Revolutionizing glioblastoma treatment: A comprehensive overview of modern therapeutic approaches. Int J Mol Sci;25(11):5774. doi: 10.3390/ijms25115774
19. Seyhan AA. (2024). Circulating liquid biopsy biomarkers in glioblastoma: Advances and challenges. Int J Mol Sci;25(14):7974. doi: 10.3390/ijms25147974
20. Szylberg M, Sokal P, Śledzińska P, Bebyn M, Krajewski S, Szylberg Ł, et al. (2022). MGMT promoter methylation as a prognostic factor in primary glioblastoma. Biomedicines;10(8):2030. doi: 10.3390/biomedicines10082030
21. Tan R, Sui C, Wang C, Zhu T. (2024). MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study. Front Oncol;14:1401977. doi: 10.3389/fonc.2024.1401977
22. Wang Z, Wang L, Wang Y. (2025). Radiomics in glioma: emerging trends and challenges. Ann Clin Transl Neurol;12(3):460–477. doi: 10.1002/acn3.52306
23. Zhang H, Zhang B, Pan W, Dong X, Li X, Chen J, Wang D, Ji W. (2022). Preoperative Contrast-Enhanced MRI in Differentiating Glioblastoma From Low-Grade Gliomas: A Proof-of-Concept Study. Front Oncol;11:761359. doi: 10.3389/fonc.2021.761359
24. Zhu M, Li S, Kuang Y, Hill VB, Heimberger AB, Zhai L, Zhai S. (2022). Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective. Front Oncol;12:924245. doi: 10.3389/fonc.2022.924245
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Copyright (c) 2026 Antonina Zatyka, Aleksander Krupski, Michał Marusza, Victoria Stielow
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