Long Short-Term Memory–Driven Modeling of Dynamic Hepatocellular Carcinoma Microenvironments: A Deep Learning Framework for Precision Treatment Prediction

This study demonstrates that LSTMs are powerful tools for analyzing and understanding the dynamic changes in the HCC immune microenvironment. Their strong capability in time-series data analysis provides new opportunities for uncovering the mechanisms of HCC progression and optimizing therapeutic strategies. However, because of the lack of detailed etiological information, stratified validation across different etiologies such as hepatitis B virus, hepatitis C virus, and non-alcoholic steatohepatitis has not yet been conducted in this study and should be addressed in future work. The findings highlight the important role of DL technologies in future research and treatment of HCC.