560-06-012 ISMRM Abstract

A Deep Learning Model Based on Intratumoral DCE-MRI Kinetic Subregions for Predicting Response to Neoadjuvant Endocrine Thera

Primary: Body - Breast

Secondary: Analysis Methods - Classification and Prediction

560-06-012 · AI Image Processing · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row A

Keywords: Breast cancer Deep learning DCE-MRI Intratumoral heterogeneity Neoadjuvant endocrine therapy

Accepted

Lizhi Xie ¹, Haonan Guan¹, Liang You², Yingshi Sun²

¹MRI Research, GE Healthcare, Beijing, China

²China Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China

Presenting Author: Lizhi Xie

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.

References

1. Shang Y, Wang Y, Guo Y, et al. The Clinical Study of Intratumoral and Peritumoral Radiomics Based on DCE-MRI for HER-2 Positive and Low Expression Prediction in Breast Cancer. Breast Cancer (Dove Med Press). 2024;16:957-972. Published 2024 Dec 14. doi:10.2147/BCTT.S497770 [doi]

2. Osuala R, Joshi S, Tsirikoglou A, et al. Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks. J Med Imaging (Bellingham). 2025;12(Suppl 2):S22014. doi:10.1117/1.JMI.12.S2.S22014 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-06-012