Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
562-02-004 ISMRM Abstract

Integrating Global and Habitat-Based Radiomics from DCE-MRI for Improved Characterization of Breast Lesions

Accepted
Kexin Chen1,2,3, Ya Ren4, Xuetong Tao1,2, Meng Wang4, Lin Li4, Shuluan Chen4, Jie Wen4, wei cui5, Zhanli Hu1,6, Xin Liu1,2, Dong Liang1,6, Hairong Zheng1,2, Qian Wan1,2, Zhou Liu 4, Na Zhang1,2
1State Key Laboratory of Biomedical Imaging Science and System, Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China
2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3Southern University of Science and Technology, Shenzhen, China
4Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, shenzhen, China
5MRI Research, GE Healthcare, Beijing, China
6Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Presenting Author: Zhou Liu

Synopsis

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References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. United States; 2024; doi: 10.3322/caac.21834. [doi]
2. Huang W, Tudorica LA, Li X, et al. Discrimination of benign and malignant breast lesions by using shutter-speed dynamic contrast-enhanced MR imaging. Radiology. United States; 2011;261(2):394–403. doi: 10.1148/radiol.11102413. [doi]
3. Daimiel Naranjo I, Gibbs P, Reiner JS, et al. Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics (Basel, Switzerland). Switzerland; 2021;11(6). doi: 10.3390/diagnostics11060919. [doi]
4. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. United States; 2012;366(10):883–892. doi: 10.1056/NEJMoa1113205. [doi]
5. Kim J-H, Ko ES, Lim Y, et al. Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. Radiology. United States; 2017;282(3):665–675. doi: 10.1148/radiol.2016160261. [doi]
6. Kim JY, Kim JJ, Hwangbo L, et al. Kinetic Heterogeneity of Breast Cancer Determined Using Computer-aided Diagnosis of Preoperative MRI Scans: Relationship to Distant Metastasis-Free Survival. Radiology. United States; 2020;295(3):517–526. doi: 10.1148/radiol.2020192039. [doi]
7. Yao Y, Mou F, Kong J, Liu X. Kinetic Heterogeneity Improves the Specificity of Dynamic Enhanced MRI in Differentiating Benign and Malignant Breast Tumours. Acad Radiol. United States; 2024;31(3):812–821. doi: 10.1016/j.acra.2023.10.006. [doi]
8. Xu C, Wang Z, Wang A, et al. Breast Cancer: Multi-b-Value Diffusion Weighted Habitat Imaging in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy. Acad Radiol. United States; 2024;31(12):4733–4742. doi: 10.1016/j.acra.2024.06.004. [doi]
9. Chen H, Liu Y, Zhao J, et al. Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and -zero breast cancers: a multicenter study. Breast Cancer Res. England; 2024;26(1):160. doi: 10.1186/s13058-024-01921-7. [doi]
10. Huang YH, Shi ZY, Zhu T, Zhou TH, Li Y, Li W, Qiu H, Wang SQ, He LF, Wu ZY, Lin Y, Wang Q, Gu WC, Gu CC, Song XY, Zhou Y, Guan DG, Wang K. Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer. Adv Sci (Weinh). 2025 Mar;12(12):e2413702. doi: 10.1002/advs.202413702. [doi]
11. Hollmann, N., Müller, S., Purucker, L. et al. Accurate predictions on small data with a tabular foundation model. Nature 637, 319–326 (2025). https://doi.org/10.1038/s41586-024-08328-6 [doi]

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