Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
361-01-011 ISMRM Abstract

Habitat Radiomics of Multi-parametric MRI Predicts Treatment Response and Survival in Colorectal Liver Metastases

Primary: Body - Liver
Secondary: Analysis Methods - Radiomics
361-01-011 · Advanced Quantitative Liver MRI: Radiomics, Elastography, and AI · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row B
Keywords: Habitat radiomics Colorectal liver metastases
Accepted
Ailing Wang1,2, HUAN ZHANG3, Yang Song4,5, Chenglong Wang1, Tong Tong6, Guang Yang 1,7
1Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China
2Institute of Magnetic Resonance and Molecular Imaging in Medicine, Shanghai, China
3Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
4MR Research Collaboration, Shanghai, China
5MR Research Collaboration Team, Siemens Healthineers Ltd. Shanghai, Shanghai, China
6Fudan University Shanghai Cancer Center, Shanghai, China
7Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
Presenting Author: Guang Yang

Synopsis

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References

1. Reboux N, Jooste V, Goungounga J, Robaszkiewicz M, Nousbaum JB, Bouvier AM. Incidence and Survival in Synchronous and Metachronous Liver Metastases From Colorectal Cancer. JAMA Netw Open. 2022;5(10):e2236666. Published 2022 Oct 3. doi:10.1001/jamanetworkopen.2022.36666 [doi]
2. Chung WS, Park MS, Shin SJ, et al. Response evaluation in patients with colorectal liver metastases: RECIST version 1.1 versus modified CT criteria. AJR Am J Roentgenol. 2012;199(4):809-815. doi:10.2214/AJR.11.7910 [doi]
3. Yin X, Liu P, Zhang C, et al. Dynamic Contrast-Enhanced MRI for Assessing Histopathological Growth Patterns and Prognosis in Colorectal Liver Metastases. J Magn Reson Imaging. Published online July 31, 2025. doi:10.1002/jmri.70060 [doi]
4. Inchingolo R, Maino C, Cannella R, et al. Radiomics in colorectal cancer patients. World J Gastroenterol. 2023;29(19):2888-2904. doi:10.3748/wjg.v29.i19.2888 [doi]
5. Song Y, Zhang J, Zhang YD, et al. FeAture Explorer (FAE): A tool for developing and comparing radiomics models. PLoS One. 2020;15(8):e0237587. Published 2020 Aug 17. doi:10.1371/journal.pone.0237587 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/361-01-011