MRI-Based Model for Personalizing Neoadjuvant Treatment in Breast Cancer
Predictive models developed using imaging biomarkers, alongside clinically validated probability thresholds, can be incorporated into decision-making for precision oncology.
Authors
- Angela M. DeMichele (ORCID: https://orcid.org/0000-0003-1297-4251)
- Denise M. Wolf (ORCID: https://orcid.org/0000-0002-3676-653X)
- Laura van ‘t Veer (ORCID: https://orcid.org/0000-0002-9838-8298)
- Barbara LeStage
- Bonnie N. Joe (ORCID: https://orcid.org/0000-0001-9333-1463)
- Jessica Gibbs (ORCID: https://orcid.org/0000-0003-1785-0899)
- Natsuko Onishi (ORCID: https://orcid.org/0000-0003-4495-9187)
- John Kornak (ORCID: https://orcid.org/0000-0002-0089-0619)
- Lisa J. Wilmes
- Nola M. Hylton (ORCID: https://orcid.org/0000-0002-6747-1662)
- Christina Yau (ORCID: https://orcid.org/0000-0002-1937-0859)
- Mark Jesus M. Magbanua (ORCID: https://orcid.org/0000-0003-2113-3593)
- Laura J. Esserman (ORCID: https://orcid.org/0000-0001-9202-4568)
- Nu N. Le
- Elissa R. Price (ORCID: https://orcid.org/0000-0001-9572-7795)
- Pouya Metanat (ORCID: https://orcid.org/0000-0003-2905-4080)
- Wen Li (ORCID: https://orcid.org/0000-0001-6584-363X)
Institutions
- University of California, San Francisco (US)
- Children's Advocacy Center (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Tomography
- Published
- 2025-02-27
- DOI
- https://doi.org/10.3390/tomography11030026
- Citations
- 4
- Primary Topic
- Breast Cancer Treatment Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.81
Funders
- National Institutes of Health