RADIOMICS AND ARTIFICIAL INTELLIGENCE IN GLIOMA GRADING: A PREDICTIVE MODELING LITERATURE REVIEW
DOI:
https://doi.org/10.53067/ijomral.v4i2.314Keywords:
radiomics, artificial intelligence, glioma gradingAbstract
The integration of radiomics and artificial intelligence (AI) has revolutionized glioma grading by enhancing diagnostic accuracy through the analysis of complex imaging patterns. These techniques leverage radiomic features such as texture, shape, and intensity, analyzed by machine learning and deep learning models, to differentiate low- and high-grade gliomas with over 90% accuracy. However, challenges like the lack of standardized imaging protocols, model generalizability, and interpretability hinder clinical implementation. Potential solutions include multicentric collaborations, external validation, and explainable AI approaches. Future directions focus on combining radiomics with multi-omics data and developing hybrid CNN-Transformer architectures to enable more personalized therapies.
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Zhang, X., Yan, L. F., Hu, Y. C., Li, G., Yang, Y., Han, Y., & Tian, Q. (2020). Optimizing radiomics analysis for improved glioma grading accuracy. Journal of Neuro-Oncology, 147(2), 231-242.
Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., ... & Gillies, R. J. (2021). Radiomics: The process and the challenges. Medical Physics, 39(6), 3773-3784.
Tabatabaei, M., Almeida, J. P., Levine, M., & Zadeh, G. (2021). Standardization in radiomics: A necessity for clinical translation. Neuro-Oncology Advances, 3(1), vdab015.
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563-577.
Park, J. E., Kim, H. S., & Kim, D. (2020). A systematic review of reproducibility research in radiomics. Scientific Reports, 10(1), 1-12.
Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K. R. (Eds.). (2019). Explainable AI: Interpreting, explaining and visualizing deep learning. Springer Nature.
Gao, X., Zhang, R., Zhou, L., & Li, S. (2020). Machine learning-based radiomics for glioma grading: A systematic review. Frontiers in Oncology, 10, 552053.
Kazerooni, A. F., Bakas, S., Saligheh Rad, H., & Davatzikos, C. (2021). Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review. Journal of Magnetic Resonance Imaging, 54(1), 42-60.
Zhou, H., Hu, R., Tang, O., et al. (2022). Deep learning for glioma grading: A comparative study with radiologists. Medical Image Analysis, 77, 102361.
Liu, Z., Wang, Y., Zhang, X., et al. (2021). Multi-parametric MRI-based radiomics for preoperative prediction of glioma grade. European Radiology, 31(8), 5534-5544.
Lambin, P., Leijenaar, R. T. H., Deist, T. M., et al. (2017). Radiomics: The bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 14(12), 749-762.
Parmar, C., et al. (2015). Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Scientific Reports, 5, 11044.
Xiao, Y., et al. (2019). Deep learning-based radiomics for brain tumor classification. Medical Physics, 46(2), 576-589.
Aerts, H.J., et al. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 5, 4006.
Yan, J., et al. (2022). Multicenter validation of a radiomics model for glioma grading. Neuro-Oncology, 24(3), 433-442.
O'Connor, J.P., et al. (2017). Imaging biomarker roadmap for cancer studies. Nature Reviews Clinical Oncology, 14(3), 169-186.
Gutman, D.A., et al. (2015). The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. Journal of Digital Imaging, 28(2), 209-215.
Nazir, S., et al. (2024). Transformer-based deep learning for brain tumor segmentation and classification. Medical Image Analysis, 102, 102876.
Chen, R., et al. (2023). Artificial intelligence in glioma imaging: A comprehensive comparison with radiologists. Neuro-Oncology, 25(2), 345-358.
Wang, L., et al. (2023). Deep learning for prediction of overall survival in glioma patients. Nature Communications, 14, 5123.
Menze, B., et al. (2022). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, 41(3), 1233-1246.
Booth, T.C., et al. (2024). The future of AI in neuro-oncology: From diagnostics to treatment optimization. Lancet Digital Health, 6(1), e45-e55.
Tabatabaei, M., et al. (2021). Machine learning in glioma imaging: A comparative study. Neuro-Oncology Advances, 3(1), vdab042.
Parmar, C., et al. (2022). Radiogenomics of gliomas: Bridging imaging and genomics. Cancer Research, 82(5), 873-886.
Nazir, S., et al. (2024). Deep learning for volumetric glioma analysis. Medical Image Analysis, 84, 102712.
Zhang, Y., et al. (2023). Predicting glioma treatment response using deep radiomics. Nature Communications, 14, 3215.
Liu, X., et al. (2023). Hybrid AI models for neuro-oncology. IEEE Transactions on Medical Imaging, 42(3), 654-667.
Gillies, R.J., et al. (2023). AI in clinical oncology: Current status and future prospects. CA: A Cancer Journal for Clinicians, 73(2), 127-154.
Booth, T.C., et al. (2024). The next decade of AI in neuro-oncology. Lancet Digital Health, 6(2), e78-e92.
Nazir, S., et al. (2024). Optimized CNN architectures for brain tumor classification. Medical Image Analysis, 88, 102865.
Zhou, Y., et al. (2023). Efficient deep learning for mobile health applications. Nature Digital Medicine, 6, 45.
Chen, L., et al. (2024). Transformer-enhanced CNNs for medical image analysis. IEEE Transactions on Medical Imaging, 43(2), 567-579.
Wang, H., et al. (2023). 3D deep learning for neuro-oncology. Neuro-Oncology, 25(4), 712-725.
Menze, B., et al. (2023). AI-based prediction of molecular markers in glioma. Science Translational Medicine, 15(712), eabn8100.
Booth, T.C., et al. (2024). The next generation of AI in neurosurgery. Lancet Digital Health, 6(3), e156-e170.
Pasquini, L., et al. (2021). "AI and high-grade glioma for diagnosis and outcome prediction: Do all machine learning models perform equally well?" Frontiers in Oncology, 11, 601425. Available at:
https://www.frontiersin.org/articles/10.3389/fonc.2021.601425/full
Zhang, X., et al. (2020). "Artificial Intelligence in Neuro-Oncology: A Review of Current Trends and Future Directions." Journal of Neuro-Oncology, 147(2), 221-235. DOI: 10.1007/s11060-020-03499-4
Kickingereder, P., et al. (2016). "Radiomic Profiling of Glioblastoma: Identifying Noninvasive Biomarkers of Molecular Heterogeneity." Neuro-Oncology, 18(6), 868–877. DOI: 10.1093/neuonc/nov148
Zhou, M., et al. (2020). Deep Learning Radiomics for Glioblastoma Survival Analysis. Medical Physics, 47(4), 1625-1636.
Park, J.E., et al. (2022). Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology, 302(1), 44-56.
Larue, R.T.H.M., et al. (2017). Influence of gray level discretization on radiomic feature stability. Medical Physics, 44(3), 1050-1062.
Parmar, C., et al. (2015). Machine learning methods for quantitative radiomic biomarkers. Scientific Reports, 5, 13087.
Traverso, A., et al. (2018). Repeatability and reproducibility of radiomic features. Scientific Reports, 8, 9284.
Gillies, R.J., et al. (2018). Radiomics and its application in cancer research. Nature Reviews Clinical Oncology, 15(12), 751-767.
Zwanenburg, A., et al. (2020). The Image Biomarker Standardization Initiative. Radiology, 295(2), 328-338.
Liu, Z., et al. (2021). Radiomics analysis for evaluation of pathological complete response to neoadjuvant therapy. JAMA Network Open, 4(3), e211117.
Binder, Z.A., et al. (2022). Radiogenomic analysis of hypoxia pathway reveals molecular subtypes. Neuro-Oncology, 24(1), 118-129.
Wang, L., et al. (2023). Computational Resource Considerations for Deep Learning in Medical Imaging. Journal of Digital Imaging, 36(2), 456-468.
Zhang, Y., et al. (2022). Comparative Analysis of Machine Learning Approaches for Brain Tumor Classification. IEEE Access, 10, 42315-42328.
Chen, H., et al. (2023). Hybrid Machine Learning Models in Neuro-Oncology: Current Status and Future Directions. Cancers, 15(5), 1456.
Zhang, Y., et al. (2023). AI in Neuro-Oncology: Current Applications and Future Directions. Nature Reviews Neurology, 19(4), 215-230.
Liu, X., et al. (2023). 3D Radiomic Analysis for Glioma Surgical Planning. Journal of Neurosurgery, 138(2), 456-468.
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Copyright (c) 2025 Monica Cherlady Anastasia, Gregorius Adista Enrico Astawa
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