Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
460-03-009 ISMRM Abstract

Longitudinal DCE-MRI and Biomedical Foundation Model for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer

Primary: Analysis Methods - Classification and Prediction
Secondary: Analysis Methods - Foundation Models
460-03-009 · Image Classification for Breast Cancer · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row A
Keywords: Breast cancer Foundation model Longitudinal MRI Neoadjuvant Chemotherapy
Accepted
Haiwei Lin1, Meng Wang2, Ya Ren2, Lin Li2, Shuluan Chen2, Jie Wen2, wei cui3, Zhou Liu 2, Bingsheng Huang1
1Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China
2Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, shenzhen, China
3MRI Research, GE Healthcare, Beijing, China
Presenting Author: Zhou Liu

Synopsis

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References

1. Siegel, R.L., et al., Cancer statistics, 2022. CA Cancer J Clin, 2022. 72(1): p. 7-33.
2. Gradishar, W.J., et al., Breast Cancer, Version 4.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2018. 16(3): p. 310-320.
3. Shi, Z., et al., MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology, 2023. 308(1): p. e222830.
4. Gao, Y., et al., An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer. Nat Commun, 2024. 15(1): p. 9613.
5. Zhang, S., et al., A multimodal biomedical foundation model trained from fifteen million image–text pairs. NEJM AI, 2025. 2(1): p. AIoa2400640.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/460-03-009