Traditional Cox regression outperforms large language models in predicting long-term progression of intermediate to advanced hepatocellular carcinoma.

Objective: This study aimed to evaluate and compare the performance of large language models (LLMs) and traditional Cox regression models in predicting the long-term progression risk in patients with intermediate to advanced hepatocellular carcinoma (HCC). Methods: A total of 576 patients with intermediate to advanced HCC were included, comprising a training cohort ( Results: Based on transarterial chemoembolization combined with targeted therapy, the addition of immune checkpoint inhibitors (ICIs) and/or ablation prolonged the progression-free survival (PFS): all four treatments combined showed optimal outcome (median PFS = 12.3 months, 95%CI = 9.9-14.1). Univariate and multivariate Cox analyses identified independent prognostic factors, which were utilized to develop a progression risk nomogram. The model had good discrimination, with training cohort AUCs (at 12, 24, and 36 months) of 0.72 (95%CI = 0.67-0.78), 0.77 (95%CI = 0.69-0.86), and 0.96 (95%CI = 0.93-0.99), respectively, and validation cohort AUCs of 0.75 (95%CI = 0.67-0.83), 0.81 (95%CI = 0.71-0.91), and 0.97 (95%CI = 0.94-1.0), respectively. Three LLMs were evaluated on the same dataset. Except for DeepSeek R1 at 12 and 24 months (training cohort), all LLMs underperformed the Cox model across time points, indicating current limitations in predicting long-term progression risk. Conclusion: The combination of ablation and/or ICIs with standard treatment could prolong PFS. In predicting the long-term HCC progression risk, the traditional Cox model exceeded the LLMs. Their combination may merge structured modeling stability with the multi-source data processing capacity of LLMs, potentially improving prediction accuracy.