A multimodal LLM-agent framework for personalized clinical decision-making in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) treatment is challenging due to tumor heterogeneity and patient variability. Current guidelines often overlook individual factors, limiting treatment precision. We developed an integrated framework combining radiomics, deep learning, and large language model (LLM)-based decision agents to generate personalized HCC treatment recommendations. A modified GhostNet incorporating dilated convolutions, channel and spatial attention mechanism (CBAM), and residual channel attention (RCA) modules was trained on MRI to predict pathological markers such as microvascular invasion (MVI), capsule presence, and tumor differentiation. A fusion model integrating radiomics and deep learning enhanced prediction accuracy. Six AI agents processed structured multimodal data and generated individualized treatment strategies, which were evaluated by hepatobiliary surgeons. The fusion model significantly improved prediction accuracy, with MVI and capsule presence reaching 0.8902 and 0.8765, respectively. DeepSeek-R1 achieved the highest clinical relevance score, followed by GPT-4 and Med-PaLM 2. This framework demonstrates the feasibility of AI-assisted, patient-specific HCC decision-making, offering a promising direction for precision oncology.