Learning From Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC

Contrast-enhanced computed tomography (CT) is recommended in clinical guidelines for managing hepatocellular carcinoma (HCC), yet complete multi-phase acquisition is often unavailable. Here, we present DuoProto, a novel dualbranch, prototype-guided framework that leverages limited multi-phase CT during training to enhance single-phase ER prediction in HCC. DuoProto aligns class-level representations across single- and multi-phase inputs via prototype learning, incorporates clinically informed ranking, and infers with single-phase only. Under conditions of class imbalance and missing phase, DuoProto consistently outperforms existing methods, achieving absolute gains of$3-9 \%$across all metrics. The proposed framework provides a clinically aligned solution, enabling more informed decision-making. Code is available at https://github.com/idssplab/DuoProto.