Tumor Morphology for Prediction of Poor Responses Early in Neoadjuvant Chemotherapy for Breast Cancer: A Multicenter Retrospective Study
Tumor morphologic features can improve the prediction of RCB-III compared to using FTV only at the early treatment time point.
Authors
- Rohan Nadkarni
- Efstathios D. Gennatas (ORCID: https://orcid.org/0000-0001-9280-3609)
- 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)
- Rita A. Mukhtar (ORCID: https://orcid.org/0000-0001-8079-7846)
- Barbara LeStage
- 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)
- Wen Li (ORCID: https://orcid.org/0000-0001-6584-363X)
- Laura J. van’t Veer
Institutions
- University of California, San Francisco (US)
- Children's Advocacy Center (US)
Publication Details
- Journal
- Tomography
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3390/tomography10110134
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81