Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a...

Primary liver cancer is the sixth most commonly diagnosed cancer globally and the third leading cause of cancer-related deaths. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and microvascular invasion (MVI) is a significant risk factor affecting postoperative prognosis in HCC. However, accurately predicting MVI preoperatively remains a challenge. This study aims to evaluate the application of large language models (LLMs), specifically ChatGPT 4o, in predicting MVI in HCC and to compare its performance with traditional clinical models. In this retrospective study, 300 HCC patients who underwent curative liver resection between June 2018 and December 2018 were selected at two centers. The collected clinical data included age, gender, HBV infection, liver cirrhosis, AFP levels, and more. ChatGPT 4o were used to process the clinical data of the patients and predict MVI. Subsequently, the predictive results of the ChatGPT 4o were compared with machine learning models, the ROC curves were plotted, and AUC was calculated. The results showed that the AUC of the ChatGPT 4o was 0.755. Machine learning algorithms use Random Forest, Support Vector Machine, Logistic Regression, XGBoost and Decision Tree, the AUC of 5 machine learning algorithms was range from 0.534 to 0.624. ChatGPT 4o achieved the highest AUC and showed statistically significant differences compared to Support Vector Machine, Logistic Regression and Decision Tree. Additionally, the predictive results of the ChatGPT 4o effectively stratified the postoperative overall survival (OS) and recurrence-free survival (RFS) of HCC patients. LLMs have demonstrated significant predictive capabilities for MVI in HCC and for risk stratification regarding postoperative OS and RFS. These advancements possess substantial potential to enhance preoperative management and make surgical planning.