Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
603-03-006 ISMRM Abstract

Interpretable MRI Automated Machine Learning Model for Predicting Response to Combination of Antiangiogenic and Immunotherapy

Primary: Body - Liver
Secondary: Analysis Methods - Classification and Prediction
603-03-006 · AI for Diagnostic and Prognostic Applications · Thursday, 14 May, 4:00 PM–5:50 PM · Auditorium 1
Keywords: Treatment Response Hepatocellular carcinoma MRI Machine learning (ML)
Accepted
Huihui Wang1, Ying Xu1, Sicong Wang2,3,4, Lizhi Xie 3,5,6, Feng Ye1, Xinming Zhao1
1National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
2GE Healthcare, Munich, Germany
3GE Healthcare, Beijing, China
4MRI Research, GE Healthcare, Beijing, China
5GE Healthcare, Beijing, China
6GE Healthcare, MR Research China, Beijing, 100176, P.R. China, China
Presenting Author: Lizhi Xie

Synopsis

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References

1. Filho, A. M. et al. The GLOBOCAN 2022 cancer estimates: Data sources, methods, and a snapshot of the cancer burden worldwide. Int J Cancer 156, 1336-1346, doi:10.1002/ijc.35278 (2025). [doi]
2. Rimassa, L., Finn, R. S. & Sangro, B. Combination immunotherapy for hepatocellular carcinoma. J Hepatol 79, 506-515, doi:10.1016/j.jhep.2023.03.003 (2023). [doi]
3. Cappuyns, S., Corbett, V., Yarchoan, M., Finn, R. S. & Llovet, J. M. Critical Appraisal of Guideline Recommendations on Systemic Therapies for Advanced Hepatocellular Carcinoma: A Review. JAMA Oncol 10, 395-404, doi:10.1001/jamaoncol.2023.2677 (2024). [doi]
4. Bo, Z. et al. Prediction of Response to Lenvatinib Monotherapy for Unresectable Hepatocellular Carcinoma by Machine Learning Radiomics: A Multicenter Cohort Study. Clin Cancer Res 29, 1730-1740, doi:10.1158/1078-0432.CCR-22-2784 (2023). [doi]
5. Han, X. et al. Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response. Breast Cancer Res 26, 18, doi:10.1186/s13058-024-01776-y (2024). [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/603-03-006