Multimodal large language models in brain tumor imaging: clinical applications and future perspectives

Area of Science:

• Neuro-oncology
• Artificial Intelligence
• Medical Imaging Analysis

Background:

• Multimodal data integration is crucial for precise brain tumor diagnosis and treatment.
• Challenges include spatial resolution, semantic representation, and measurement scale discrepancies across data modalities.
• Artificial intelligence (AI) is key to integrating heterogeneous data for improved neuro-oncology outcomes.

Purpose of the Study:

• To provide a comprehensive overview of Multimodal Large Language Models (MLLMs) in brain tumor analysis.
• To explore the methodological foundations, representation learning, and cross-modal alignment of MLLMs.
• To summarize MLLM applications and discuss limitations and future directions in neuro-oncology.

Main Methods:

• Review of existing literature on Multimodal Large Language Models (MLLMs).
• Analysis of MLLM methodologies, including representation learning and cross-modal alignment.
• Summarization of MLLM applications in research and clinical settings for brain tumor analysis.

Main Results:

• MLLMs offer a unified framework to address challenges in integrating heterogeneous multimodal data for brain tumor analysis.
• MLLMs enhance interpretative and generative capabilities by jointly modeling visual, textual, and structured data.
• Applications span diagnosis support, prognosis prediction, treatment planning, and radiology report generation.

Conclusions:

• MLLMs show significant promise for advancing brain tumor diagnosis, prognosis, and treatment.
• Future directions focus on overcoming data scarcity, interpretability, and clinical deployment barriers.
• Developing robust, explainable, and clinically translatable MLLM systems is essential for neuro-oncology.