Abstract / Summary
ABSTRACT Hepatocellular carcinoma (HCC) patients show heterogeneous responses to immune checkpoint inhibitors (ICIs). This study developed ECOS‐Net, a transformer‐based multitask network integrating CT‐derived habitat and 2.5‐dimensional (2.5D) deep learning features for simultaneously predicting early disease control (DC) and overall survival (OS). Of 1,234 patients with HCC enrolled from eight institutions and public databases, 832 ICI‐treated patients were used for model development. ECOS‐Net fused features using multi‐head attention and generated early DC probabilities and OS risk scores. ECOS‐DC achieved AUCs of 0.836, 0.822, and 0.817 in training, internal validation, and external test sets, outperforming clinical models (all p values < 0.05). ECOS‐OS yielded C‐indices of 0.730, 0.722, and 0.720, respectively. Integrated models also showed favorable external performance (early DC AUC: 0.825; OS C‐index: 0.741). Patients with higher ECOS‐DC probabilities had a higher likelihood of early DC, whereas those with higher ECOS‐OS risk had shorter OS, with directionally consistent associations across most subgroups. Exploratory biological analyses suggested that the higher ECOS‐DC probability and lower ECOS‐OS risk groups were associated with immune‐active tumor microenvironment features. Therefore, ECOS‐Net shows potential as a non‐invasive imaging‐based risk stratification framework for simultaneously predicting early DC and OS in ICI‐treated HCC patients.