A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death
A substantial proportion of patients with non-metastatic triple-negative breast cancer (TNBC) experience disease progression and death despite treatment. However, no tool currently exists to discriminate those at higher risk of death. To identify high-risk TNBC, we conducted a retrospective analysis of 749 patients from two independent cohorts. We built a prediction model that leverages breast magnetic resonance imaging (MRI) features to predict risk groups based on a 50-gene Transcriptomics Signature (TS). The TS distinguished patients with high-risk for death in multivariate survival analysis (Transcriptomic cohort: [HR] = 13.6, 95% confidence interval [CI] = 1.56-1, p = 0.02; SCAN-B cohort: HR = 1.45, CI 1.04-2.03, p = 0.02). The model identified a 20-feature radiomic signature derived from breast MRI that predicted the TS-based risk groups. This imaging-based classifier was applied to a validation cohort (log rank p = 0.013, accuracy 0.72, AUC 0.71, F1 0.74, precision 0.67, and recall 0.82), detecting a 25% absolute survival difference between high- and low-risk groups after 5 years.
Authors
- James M. Ford (ORCID: https://orcid.org/0000-0002-2513-7025)
- Vishal Joshi (ORCID: https://orcid.org/0000-0002-1457-4027)
- Debra M. Ikeda (ORCID: https://orcid.org/0000-0002-8663-8448)
- Satoko Okamoto (ORCID: https://orcid.org/0000-0001-6042-8269)
- Humaira Noor (ORCID: https://orcid.org/0000-0001-8887-8122)
- Andrew Kozlov (ORCID: https://orcid.org/0000-0002-3181-6470)
- Ryle Zhou
- Sarah A. Mattonen (ORCID: https://orcid.org/0000-0002-8172-1074)
- Melinda L. Telli (ORCID: https://orcid.org/0000-0001-7993-1235)
- A. Mantz (ORCID: https://orcid.org/0000-0002-8031-1217)
- Shu-Tian Chen (ORCID: https://orcid.org/0000-0003-2650-3338)
- George W. Sledge (ORCID: https://orcid.org/0000-0003-0297-0775)
- Allison W. Kurian (ORCID: https://orcid.org/0000-0002-6175-9470)
- Haruka Itakura (ORCID: https://orcid.org/0000-0003-4382-0183)
- Olivier Gevaert (ORCID: https://orcid.org/0000-0002-9965-5466)
- Yuanning Zheng (ORCID: https://orcid.org/0000-0002-0018-3252)
- Wendy B. DeMartini (ORCID: https://orcid.org/0000-0002-5600-6626)
- Kevin Lee (ORCID: https://orcid.org/0000-0002-2730-9150)
- Shaveta Vinayak (ORCID: https://orcid.org/0000-0001-9197-7316)
- Mina Satoyoshi
Institutions
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- npj Breast Cancer
- Published
- 2025-07-26
- DOI
- https://doi.org/10.1038/s41523-025-00790-3
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Breast Cancer Research Foundation
- California Department of Public Health
- University of Southern California
- Georgia Clinical and Translational Science Alliance
- Cancer Registry of Greater California
- Emerson Collective
- National Institutes of Health
- Centers for Disease Control and Prevention
- School of Medicine, Stanford University
- University of California, San Francisco
- National Cancer Institute
- Stanford Cancer Institute