Learning from Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915540879015936 |
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| author | Yu, Hsin-Pei Lyu, Si-Qin Hsieh, Yi-Hsien Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che |
| author_facet | Yu, Hsin-Pei Lyu, Si-Qin Hsieh, Yi-Hsien Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che |
| contents | Early recurrence (ER) prediction after curative-intent resection remains a critical challenge in the clinical management of hepatocellular carcinoma (HCC). Although contrast-enhanced computed tomography (CT) with full multi-phase acquisition is recommended in clinical guidelines and routinely performed in many tertiary centers, complete phase coverage is not consistently available across all institutions. In practice, single-phase portal venous (PV) scans are often used alone, particularly in settings with limited imaging resources, variations in acquisition protocols, or patient-related factors such as contrast intolerance or motion artifacts. This variability results in a mismatch between idealized model assumptions and the practical constraints of real-world deployment, underscoring the need for methods that can effectively leverage limited multi-phase data. To address this challenge, we propose a Dual-Branch Prototype-guided (DuoProto) framework that enhances ER prediction from single-phase CT by leveraging limited multi-phase data during training. DuoProto employs a dual-branch architecture: the main branch processes single-phase images, while the auxiliary branch utilizes available multi-phase scans to guide representation learning via cross-domain prototype alignment. Structured prototype representations serve as class anchors to improve feature discrimination, and a ranking-based supervision mechanism incorporates clinically relevant recurrence risk factors. Extensive experiments demonstrate that DuoProto outperforms existing methods, particularly under class imbalance and missing-phase conditions. Ablation studies further validate the effectiveness of the dual-branch, prototype-guided design. Our framework aligns with current clinical application needs and provides a general solution for recurrence risk prediction in HCC, supporting more informed decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning from Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC Yu, Hsin-Pei Lyu, Si-Qin Hsieh, Yi-Hsien Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Quantitative Methods Image and Video Processing Early recurrence (ER) prediction after curative-intent resection remains a critical challenge in the clinical management of hepatocellular carcinoma (HCC). Although contrast-enhanced computed tomography (CT) with full multi-phase acquisition is recommended in clinical guidelines and routinely performed in many tertiary centers, complete phase coverage is not consistently available across all institutions. In practice, single-phase portal venous (PV) scans are often used alone, particularly in settings with limited imaging resources, variations in acquisition protocols, or patient-related factors such as contrast intolerance or motion artifacts. This variability results in a mismatch between idealized model assumptions and the practical constraints of real-world deployment, underscoring the need for methods that can effectively leverage limited multi-phase data. To address this challenge, we propose a Dual-Branch Prototype-guided (DuoProto) framework that enhances ER prediction from single-phase CT by leveraging limited multi-phase data during training. DuoProto employs a dual-branch architecture: the main branch processes single-phase images, while the auxiliary branch utilizes available multi-phase scans to guide representation learning via cross-domain prototype alignment. Structured prototype representations serve as class anchors to improve feature discrimination, and a ranking-based supervision mechanism incorporates clinically relevant recurrence risk factors. Extensive experiments demonstrate that DuoProto outperforms existing methods, particularly under class imbalance and missing-phase conditions. Ablation studies further validate the effectiveness of the dual-branch, prototype-guided design. Our framework aligns with current clinical application needs and provides a general solution for recurrence risk prediction in HCC, supporting more informed decision-making. |
| title | Learning from Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC |
| topic | Quantitative Methods Image and Video Processing |
| url | https://arxiv.org/abs/2510.07347 |