Interpreting Multimodal Data Fusion for Glioblastoma Prognosis: A Systematic Literature Review of Explainable AI Applications
DOI:
https://doi.org/10.70112/ajcst-2026.15.2.4434Keywords:
Glioblastoma, Multimodal Data Fusion, Explainable AI (XAI), Radiogenomics, Deep Learning, Survival Prediction, Transformer ArchitecturesAbstract
A representative clinical scenario involves a 57-year-old patient whose brain MRI reveals an infiltrative mass with central necrosis, with biopsy confirming glioblastoma (GBM, WHO Grade 4). Despite maximal resection, concurrent temozolomide, and radiotherapy, median survival remains 14–16 months, a figure that has remained largely unchanged since 2005. This persistent survival plateau highlights a deeper diagnostic limitation: conventional prognostic tools account for age and surgical extent but overlook molecular determinants such as IDH mutation status, MGMT promoter methylation, and transcriptomic subtype, which critically influence patient outcomes. Multimodal data fusion directly addresses this gap by integrating multi-parametric MRI (mpMRI), PET imaging, next-generation genomics, and clinical variables (age, Karnofsky Performance Status [KPS]) into a unified predictive framework. Deep learning architectures leveraging these multimodal inputs have achieved Concordance Indices (C-indices) up to 0.80, compared to 0.65 for unimodal approaches. However, interpretability remains a significant barrier to clinical adoption: models such as Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) that surpass clinician performance on test sets cannot inherently explain their risk estimates. Without interpretability, these models cannot be validated against biological mechanisms, reconciled with clinical judgment, or approved by regulatory authorities. Explainable AI (XAI) methods, including SHAP (Shapley Additive exPlanations), Grad-CAM, and cross-modal attention mechanisms, translate opaque model outputs into biologically interpretable rationales. This PRISMA-compliant systematic review synthesizes 22 peer-reviewed studies (2023–2026), finding that hybrid transformer-based fusion dominates the field (45% of studies, C-index 0.71–0.80), with SHAP (55%) and attention maps (32%) leading in XAI adoption. Notably, XAI-integrated models have advanced beyond replicating established biomarker associations: cross-modal attention analyses have uncovered a previously undocumented interaction between FLAIR-hyperintense peritumoral oedema and EGFR amplification that predicts early recurrence, demonstrating that interpretability tools facilitate biological discovery as well as accountability.
References
[1] Q. T. Ostrom et al., “CBTRUS statistical report: Primary brain and other central nervous system tumours diagnosed in the United States in 2015–2019,” Neuro-Oncology, vol. 24, suppl. 2, pp. v1–v95, 2022. https://doi.org/10.1093/neuonc/noac202.
[2] R. Stupp et al., “Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma,” New England Journal of Medicine, vol. 352, no. 10, pp. 987–996, 2005. https://doi.org/10.1056/NEJMoa043330.
[3] P. Y. Wen et al., “Glioblastoma in adults: A Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions,” Neuro-Oncology, vol. 22, no. 8, pp. 1073–1113, 2020. https://doi.org/10.1093/neuonc/noaa106.
[4] U. Baid et al., “The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification,” arXiv preprint arXiv:2107.02314, 2021.
[5] R. G. W. Verhaak et al., “Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterised by abnormalities in PDGFRA, IDH1, EGFR, and NF1,” Cancer Cell, vol. 17, no. 1, pp. 98–110, 2010. https://doi.org/10.1016/j.ccr.2009.12.020.
[6] J. Lao et al., “A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme,” Scientific Reports, vol. 7, no. 1, Art. no. 10353, 2017. https://doi.org/10.1038/s41598-017-10649-8.
[7] B. Arrieta et al., “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, 2020. https://doi.org/10.1016/j.inffus.2019.12.012.
[8] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing Systems, vol. 30, 2017. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.
[9] Vaswani et al., “Attention is all you need,” Advances in Neural Information Processing Systems, vol. 30, 2017. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html.
[10] P. F. Whiting et al., “QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies,” Annals of Internal Medicine, vol. 155, no. 8, pp. 529–536, 2011. https://doi.org/10.7326/0003-4819-155-8-201110180-00009.
[11] D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” in Proceedings of the 2nd International Conference on Learning Representations (ICLR), 2014. https://arxiv.org/abs/1312.6114.
[12] M. J. Sheller et al., “Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data,” Scientific Reports, vol. 10, no. 1, Art. no. 12598, 2020. https://doi.org/10.1038/s41598-020-69250-1.
[13] R. L. Siegel, K. D. Miller, N. S. Wagle, and A. Jemal, “Cancer statistics, 2023,” CA: A Cancer Journal for Clinicians, vol. 73, no. 1, pp. 17–48, 2023. https://doi.org/10.3322/caac.21763.
[14] D. N. Louis et al., “The 2021 WHO classification of tumors of the central nervous system: A summary,” Neuro-Oncology, vol. 23, no. 8, pp. 1231–1251, 2021. https://doi.org/10.1093/neuonc/noab106.
[15] F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv preprint arXiv:1702.08608, 2017. https://arxiv.org/abs/1702.08608.
[16] U.S. Food and Drug Administration, “Artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) action plan,” U.S. Department of Health and Human Services, 2021. https://www.fda.gov/media/145022/download.
[17] R. R. Selvaraju et al., “Grad-CAM: Visual explanations from deep networks via gradient-based localisation,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 618–626, 2017. https://doi.org/10.1109/ICCV.2017.74.
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