Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
460-03-008 ISMRM Abstract

Improved 3D Neural Network for Prediction of Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer Acquire

Primary: Body - Breast
Secondary: Analysis Methods - Classification and Prediction
460-03-008 · Image Classification for Breast Cancer · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row A
Keywords: Breast Predictive model Neural Network Quantitative synthetic MRI
Accepted
Ken-Pin Hwang 1, Zhan Xu2, Huiqin Chen3, Xiaofei Huo4, Beatriz E Adrada5, Clinton Yam6, Peng Wei3, Wei Yang5, Jingfei Ma1, Gaiane Maia Rauch4
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, United States of America
2Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, United States of America
3Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, United States of America
4Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, United States of America
5Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, United States of America
6Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, United States of America
Presenting Author: Ken-Pin Hwang

Synopsis

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References

1. Sharma P. Biology and management of patients with triple-negative breast cancer. Oncologist 2016;21(9):1050–1062
2. Warntjes JBM, et al. Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magn Reason Med 2008;60(2):320–329.
3. Hwang KP, Fujita S. Synthetic MR: Physical principles, clinical implementation, and new developments. Med Phys. 2022 Jul;49(7):4861-4874.
4. Hwang KP, et al. A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer. 2023 Jul;5(4):e230009.
5. Hwang KP, et al. Prediction of Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer Using a 3D Neural Network on Synthetic MR Images. Proc. ISMRM, 2024.
6. He K, et al. Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2016:770-778.
7. Ebrahimi A, et al. “Convolutional Neural Networks for Alzheimer’s Disease Detection on MRI Images.” Journal of Medical Imaging, vol. 8, no. 02, SPIE-Intl Soc Optical Eng, Apr. 2021

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/460-03-008