Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
460-03-003 ISMRM Abstract

Application of Radiomics Based on MR Cytometry Parameter Mapping in Differentiating Benign and Malignant Breast Tumors

Primary: Body - Breast
Secondary: Analysis Methods - Radiomics
460-03-003 · Image Classification for Breast Cancer · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row A
Keywords: Radiomics Breast cancer Diffusion-weighted MRI MR-cytometry
Accepted
Xinyi Luo 1, Lei Wu2, Yilan Ji3, Fan Liu4, Li Chen1, Haihua Bao2, Hua Guo4, Diwei Shi1,5
1Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing, China
2Qinghai University Affiliated Hospital, Xining, China
3Beijing National Day School, Beijing, China
4Tsinghua University, Beijing, China
5Research Institute of Tsinghua University in Shenzhen, China
Presenting Author: Xinyi Luo

Synopsis

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References

1. Xu J, Jiang X, Li H, Arlinghaus LR, McKinley ET, Devan SP, et al. Magnetic resonance imaging of mean cell size in human breast tumors. Magn Reson Med. 2020 Jun;83(6):2002–14. PMID: 31765494. [pmid]
2. Jiang X, Devan SP, Xie J, Gore JC, Xu J. Improving MR cell size imaging by inclusion of transcytolemmal water exchange. NMR Biomed. 2022 Dec;35(12):e4799. PMID: 35794795. [pmid]
3. Shi D, Li S, Liu F, Jiang X, Wu L, Chen L, et al. Comprehensive characterization of tumor therapeutic response with simultaneous mapping cell size, density, and transcytolemmal water exchange. ArXiv. 2024 Aug 4;arXiv:2408.01918v1. PMID: 39130198. [pmid]
4. Panagiotaki E, Walker-Samuel S, Siow B, Johnson SP, Rajkumar V, Pedley RB, et al. Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res. 2014 Apr 1;74(7):1902–12. PMID: 24491802. [pmid]
5. Liu F, Wu L, Luo X, Li S, Wang Y, Zhong W, et al. Evaluating the Diagnostic Performance of MR Cytometry Imaging in Differentiating Benign and Malignant Breast Tumors. J Magn Reson Imaging. 2025 Aug;62(2):521–33. https://doi.org/10.1002/jmri.29757. [doi]
6. Wu D, Jiang K, Li H, Zhang Z, Ba R, Zhang Y, et al. Time-Dependent Diffusion MRI for Quantitative Microstructural Mapping of Prostate Cancer. Radiology. 2022 Jun;303(3):578–87. PMID: 35258368. [pmid]
7. Ren H, Shi D, Huang J, Yu H, Yin T, Liu D, et al. Differentiating Benign Thyroid Nodules and Papillary Thyroid Carcinoma Using Time-Dependent Diffusion MRI: A Feasibility Study. J Magn Reson Imaging. 2025 Aug 20. doi: 10.1002/jmri.70065. Epub ahead of print. PMID: 40836573. [doi] [pmid]
8. Zhao Y, Zhao F, Cheng M, Wang G, Wang D, Yin H, et al. Risk Stratification Prediction of Endometrial Cancer Using Microstructural Mapping Based on Time-Dependent Diffusion MRI. Cancer Sci. 2025;116(6):1627–37. https://doi.org/10.1111/cas.70036. [doi]
9. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RGPM, Granton P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012 Mar;48(4):441–6. https://doi.org/10.1016/j.ejca.2011.11.036. [doi]
10. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017 Oct 31;77(21):e104–7. https://doi.org/10.1158/0008-5472.CAN-17-0339. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/460-03-003