Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
660-01-004 ISMRM Abstract

Deep learning-assisted prediction of lymph node metastasis in breast cancer after neoadjuvant chemotherapy using MRI

Primary: Body - Breast
Secondary: Analysis Methods - Radiomics
660-01-004 · A Tour of Body Oncology · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row A
Keywords: Breast cancer Lymph node metastasis Deep learning Neoadjuvant Chemotherapy
Accepted
Tingxi Wu 1, Yunjun Yang1, Zhifeng Xu2, Hai Zhao2
1Department of Radiology, The First People's Hospital of Foshan (Foshan Hospital Affiliated to Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, China
2Department of Radiolofy, The First People's Hospital of Foshan (Foshan Hospital Affiliated to Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, China
Presenting Author: Tingxi Wu

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. 1.Pedersen RN, Esen B, Mellemkjær L, et al. The incidence of breast cancer recurrence 10-32 years after primary diagnosis. J Natl Cancer Inst. 2022;114:391-399.doi:10.1093/jnci/djab202. PMID: 34747484 [doi] [pmid]
2. 2.GBD 2023 Cancer Collaboration. The global, regional, and national burden of cancer, 1990-2023, with forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. 2025;S0140-6736(25)01635-6.doi:10.1016/S0140-6736(25)01635-6.PMID: 41015051 [doi] [pmid]
3. 3.Pilewskie M, Morrow M. Axillary nodal management following neoadjuvant chemotherapy: a review. JAMA Oncol. 2017;3:549-555.doi:10.1001/jamaoncol.2016.4163.PMID: 27918753 [doi] [pmid]
4. 4.Gradishar WJ, Anderson BO, Abraham J, et al. Breast cancer, version 3.2020, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2020;18:452-478.doi:10.6004/jnccn.2020.0016.PMID: 32259783 [doi] [pmid]
5. 5.Hayward JH, Linden OE, Lewin AA, et al. ACR Appropriateness Criteria® monitoring response to neoadjuvant systemic therapy for breast cancer: 2022 update. J Am Coll Radiol. 2023;20:S125-S145.doi:10.1016/j.jacr.2023.02.016.PMID: 37236739 [doi] [pmid]
6. 6.Javid S, Segara D, Lotfi P, Raza S, Golshan M. Can breast MRI predict axillary lymph node metastasis in women undergoing neoadjuvant chemotherapy? Ann Surg Oncol. 2010;17:1841-1846.doi:10.1245/s10434-010-0934-2.PMID: 20143266 [doi] [pmid]
7. 7.Bansal GJ, Jaipal A, Wu GKC, Syed A. Diagnostic accuracy of magnetic resonance imaging to evaluate axillary lymph node status in breast cancer patients receiving neoadjuvant chemotherapy. Br J Radiol. 2023;96:20220904.doi:10.1259/bjr.20220904.PMID: 36607272 [doi] [pmid]
8. 8.Fu Y, Lei YT, Huang YH, et al. Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Eur Radiol. 2024;34:7080-7089.doi:10.1007/s00330-024-10786-5.PMID: 38724768 [doi] [pmid]
9. 9.Chen Y, Wang L, Dong X, et al. Deep learning radiomics of preoperative breast MRI for prediction of axillary lymph node metastasis in breast cancer. J Digit Imaging. 2023;36:1323-1331.doi:10.1007/s10278-023-00818-9.PMID: 36973631 [doi] [pmid]
10. 10.Elsholtz FHJ, Asbach P, Haas M, et al. Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer. Eur Radiol. 2021;31:6116-6124.doi:10.1007/s00330-020-07572-4.PMID: 33585994 [doi] [pmid]
11. 11.Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2020;128:336-359.doi: 10.1109/ICCV.2017.74 [doi]
12. 12.Rodríguez-Pérez R, Bajorath J. Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. J Med Chem. 2020;63:8761-8777.doi:10.1021/acs.jmedchem.9b01101.PMID: 31512867 [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/660-01-004