Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-05-006 ISMRM Abstract

Automated Radiomic Analysis of Breast Tumor in Diffusion-Weighted MRI for Early Prediction of Neoadjuvant Therapy Response

Primary: Body - Breast
Secondary: Analysis Methods - Radiomics
360-05-006 · Acquisition, Reconstruction, and Analysis · Monday, 11 May, 5:05 PM–6:00 PM · Digital Posters Row A
Keywords: Treatment Response Radiomics Diffusion-weighted MRI Breast MRI Machine learning (ML)
Accepted
Nu N Le1, Wen Li1, Lisa Wilmes1, Natsuko Onishi 1, Bonnie Joe1, John Kornak2, Dariya Malyarenko3, Thomas L Chenevert3, Patrick J Bolan4, Savannah Partridge5, Nola Hylton1
1Department of Radiology and Biomedical Imaging, University Of California, San Francisco (UCSF), United States of America
2Department of Epidemiology & Biostatistics, University Of California, San Francisco (UCSF), United States of America
3Department of Radiology, University of Michigan, Ann Arbor, United States of America
4Center for Magnetic Resonance Research, Dept. of Radiology, University of Minnesota, Minneapolis, United States of America
5Department of Radiology, University of Washington, Seattle, United States of America
Presenting Author: Natsuko Onishi

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. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/breast-cancer. 2025. Breast cancer.
2. Partridge SC, Zhang Z, Newitt DC, Gibbs JE, Chenevert TL, Rosen MA, et al. Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment  of Breast Cancer: The ACRIN 6698 Multicenter Trial. Radiology. 2018 Dec;289(3):618–27.
3. Le NN, Newitt DC, Li W, Hathi D. ISMRM 2023. 2023. Automated breast tumor segmentation in DWI using multi-modality image registration: a feasibility study using multi-center data.
4. Li W, Partridge SC, Newitt DC, Steingrimsson J, Marques HS, Bolan PJ, et al. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge. Radiol Imaging Cancer. 2024;6(1).
5. van Griethuysen J, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017 Nov 1;77:e104–7.
6. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M. Benchmark for filter methods for feature selection in high-dimensional classification data. Comput Stat Data Anal. 2020;143.
7. De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, Haibe-Kains B. MRMRe: An R package for parallelized mRMR ensemble feature selection. Bioinformatics. 2013;29(18).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-05-006