Machine learning strategies for integrating multimodal neuro-oncology data and predicting outcomes

Artificial intelligence models combining imaging, molecular, histopathologic, clinical, and socioeconomic data often outperformed single-modality models in neuro-oncology outcome prediction, but clinical use is limited by heterogeneity and poor external validation.

• Traditional machine learning approaches have been applied to survival prediction, recurrence classification, treatment response estimation, and molecular biomarker prediction.
• Advanced architectures such as natural language processing, large language models, vision transformers, geometric deep learning, and graph neural networks can analyze unstructured multimodal sources including MRI volumes and radiology reports.
• Multimodal frameworks frequently improved predictive performance over single-modality models but remain mostly investigational pending external validation and interpretability.

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