International Journal of Clinical Science and Medical Research

Modern glioma diagnosis depends on molecular alterations that cannot be determined reliably from conventional imaging alone. Radiomics and machine learning attempt to bridge this gap by transforming magnetic resonance images into quantitative signatures associated with isocitrate dehydrogenase mutation, 1p/19q codeletion, O6-methylguanine-DNA methyltransferase promoter methylation, ATRX loss, and other clinically relevant profiles. This narrative review summarizes the principal workflows, reported performance, methodological limitations, and translational priorities of artificial intelligence in glioma radiogenomics. Evidence is strongest for noninvasive prediction of isocitrate dehydrogenase status, while results for 1p/19q and O6-methylguanine-DNA methyltransferase are promising but less stable across institutions. Handcrafted radiomics offers interpretability and modest data requirements, whereas deep learning can learn hierarchical spatial patterns but is more vulnerable to hidden confounding, scanner variation, and overfitting. Clinical adoption will require harmonized imaging, reproducible segmentation, transparent reporting, prospective multicenter validation, calibration analysis, and workflows that preserve tissue-based diagnosis as the reference standard. Artificial intelligence is therefore best viewed as a preoperative decision support and sampling tool rather than a replacement for neuropathology.

artificial intelligence; glioma; radiomics; radiogenomics; machine learning; magnetic resonance imaging; molecular biomarkers

1. Akkus, Z., Ali, I., Sedlar, J., Kline, T. L., Agrawal, J. P., Parney, I. F., Giannini, C., & Erickson, B. J. (2017). Predicting 1p/19q chromosomal deletion of low-grade gliomas from MR images using deep learning. Journal of Digital Imaging, 30(4), 469–476.
2. Al-Rahbi, A., et al. (2024). Uses of artificial intelligence in glioma: A systematic review. Sultan Qaboos University Medical Journal, 24, 3–18.
3. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4, 170117.
4. Chang, K., Bai, H. X., Zhou, H., Su, C., Bi, W. L., Agbodza, E., Kavouridis, V. K., Senders, J. T., Boaro, A., Beers, A., Zhang, B., Capellini, A., Liao, W., Shen, Q., Li, X., Xiao, B., Cryan, J., Ramkissoon, S., Ramkissoon, L., ... Huang, R. Y. (2018). Residual convolutional neural network for the determination of IDH status in low and high grade gliomas from MR imaging. Clinical Cancer Research, 24(5), 1073–1081.
5. Chen, S., et al. (2022). Predicting MGMT promoter methylation in diffuse gliomas using deep learning with MRI: A systematic review and meta-analysis. European Radiology, 32, 2496–2508.
6. Chen, X., et al. (2024). Diagnostic accuracy of machine learning based radiomics for prediction of IDH mutation in glioma: A systematic review and meta-analysis. Frontiers in Oncology, 14, 1376228.
7. Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis: The TRIPOD statement. Annals of Internal Medicine, 162(1), 55–63.
8. Fan, H., et al. (2024). Artificial intelligence based MRI radiomics and radiogenomics in glioma. Cancer Imaging, 24, 37.
9. Kawaguchi, R. K., et al. (2021). Assessing versatile machine learning models for glioma radiogenomic studies across hospitals. Cancers, 13(14), 3611.
10. Kickingereder, P., et al. (2016). Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology, 280(3), 880–889.
11. Kim, D., et al. (2019). Prediction of 1p/19q codeletion in diffuse glioma patients using preoperative multiparametric magnetic resonance imaging. European Radiology, 29, 6188–6198.
12. Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., van Wijk, Y., Woodruff, H., van Soest, J., Lustberg, T., Roelofs, E., van Elmpt, W., Dekker, A., Mottaghy, F. M., Wildberger, J. E., & Walsh, S. (2017). Radiomics: The bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 14(12), 749–762.
13. Leone, A., et al. (2025). Virtual biopsy for prediction of MGMT promoter methylation status in gliomas: A systematic review of radiomics and deep learning. Diagnostics, 15(3), 251.
14. Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., Hawkins, C., Ng, H. K., Pfister, S. M., Reifenberger, G., Soffietti, R., von Deimling, A., & Ellison, D. W. (2021). The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro-Oncology, 23(8), 1231–1251.
15. Menze, B. H., et al. (2015). The multimodal brain tumor image segmentation benchmark. IEEE Transactions on Medical Imaging, 34(10), 1993–2024.
16. Mongan, J., Moy, L., & Kahn, C. E., Jr. (2020). Checklist for artificial intelligence in medical imaging: A guide for authors and reviewers. Radiology: Artificial Intelligence, 2(2), e200029.
17. Nayak, S. S., et al. (2024). Quality assessment of MRI radiomics based machine learning studies for glioma classification: A systematic review. Diagnostics, 14, 2707.
18. Singh, G., Manjila, S., Sakla, N., True, A., Wardeh, A. H., Beig, N., Vaysberg, A., Matthews, J., Prasanna, P., & Spektor, V. (2021). Radiomics and radiogenomics in gliomas: A contemporary update. British Journal of Cancer, 125, 641–657.
19. Tejani, A. S., et al. (2024). Checklist for artificial intelligence in medical imaging: 2024 update. Radiology: Artificial Intelligence, 6(4), e240300.
20. van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillion-Robin, J. C., Pieper, S., & Aerts, H. J. W. L. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104–e107.
21. WHO Classification of Tumours Editorial Board. (2021). Central nervous system tumours (5th ed., Vol. 6). International Agency for Research on Cancer.
22. Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019). PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1), 51–58.
23. Yogananda, C. G. B., et al. (2021a). A novel fully automated MRI based deep learning method for classification of 1p/19q codeletion status in brain gliomas. Neuro-Oncology Advances, 3(1), vdab004.
24. Yogananda, C. G. B., et al. (2021b). MRI based deep learning method for determining glioma MGMT promoter methylation status. American Journal of Neuroradiology, 42(5), 845–852.
25. Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., Bogowicz, M., Boldrini, L., Buvat, I., Cook, G. J. R., Davatzikos, C., Depeursinge, A., Desseroit, M. C., Dinapoli, N., Dinh, C. V., ... Löck, S. (2020). The Image Biomarker Standardisation Initiative: Standardized quantitative radiomics for high throughput image based phenotyping. Radiology, 295(2), 328–338.