Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
660-01-011 ISMRM Abstract

MRI-based Habitat Radiomics and Clinical-Radiological Integration for Predicting BCS Feasibility after NAC

Primary: Body - Breast
Secondary: Analysis Methods - Radiomics
660-01-011 · A Tour of Body Oncology · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row A
Keywords: Breast cancer Habitat radiomics Breast-conserving surgery
Accepted
jinrui liu1, Jing Zhang 1, Yuhui Xiong2, Mingsong Tang1, fei jia
1The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China
2GE HealthCare MR Research, Beijing, China
Presenting Author: Jing Zhang

Synopsis

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References

1. 1.Joshi S, Garlapati C, Bhattarai S, et al. Exosomal metabolic signatures are associated with differential response to neoadjuvant chemotherapy in patients with breast cancer. Int J Mol Sci 2022;23(10):5324.
2. 2.Gradishar WJ, Anderson BO, Balassanian R, et al. NCCN guidelines insights: breast cancer, version 1.2016. J Natl Compr Canc Netw 2015;13(12):1475-1485.
3. 3.Yang L, Wu S, Ma C, et al. RNA m6A methylation regulators subclassify luminal subtype in breast cancer. Front Oncol 2020;10:611191.
4. 4.Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2016;2(12):1636-1642.
5. 5.Wu J, Tha KK, Xing L, Li R. Radiomics and radiogenomics for precision radiotherapy. J Radiat Res 2018;59(suppl_1):i25-i31.
6. 6.Xu S, Zhang J, Liu J, et al. Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy. Cancer Imaging 2025;25:108.
7. 7.Zhang X, Su GH, Chen Y, Gu YJ, You C. Decoding intratumoral heterogeneity: clinical potential of habitat imaging based on radiomics. Radiology 2023;309(2):e232047.
8. 8.Chen H, Wang Z, Tang M, 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 2024;26:160.
9. 9.Wu PQ, Li Y, Chen X, et al. Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: a retrospective study. Quant Imaging Med Surg 2024;14(10):8211-8224.
10. 10.Li QY, Zhao W, Wang X, et al. MRI-based habitat analysis for intratumoral heterogeneity quantification combined with deep learning for HER2 status prediction in breast cancer. Magn Reson Imaging 2025;122:110429.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/660-01-011