Development of an MRI Radiomic Machine-Learning Model to Predict Triple-Negative Breast Cancer Based on Fibroglandular Tissue of the Contralateral Unaffected Breast in Breast Cancer Patients
TNBC can be predicted based on radiomic features extracted from the FGT of the contralateral unaffected breast of patients, suggesting the potential for risk prediction specific to TNBC.
Authors
- Roberto Lo Gullo (ORCID: https://orcid.org/0000-0001-6887-292X)
- Katja Pinker (ORCID: https://orcid.org/0000-0002-2722-7331)
- Sarah Eskreis‐Winkler (ORCID: https://orcid.org/0000-0003-2427-3532)
- Keitha Varela (ORCID: https://orcid.org/0009-0000-7007-9226)
- Mark E. Robson (ORCID: https://orcid.org/0000-0002-3109-1692)
- Sunitha B. Thakur (ORCID: https://orcid.org/0000-0001-8090-3696)
- Maxine S. Jochelson (ORCID: https://orcid.org/0000-0002-4012-2470)
- R. Elena Ochoa‐Albiztegui (ORCID: https://orcid.org/0000-0002-5443-6702)
- Jayasree Chakraborty (ORCID: https://orcid.org/0000-0003-4434-8861)
- Daphne Resch
Institutions
- Sigmund Freud Privatuniversität Wien (AT)
- Memorial Sloan Kettering Cancer Center (US)
- CUNY School of Law (US)
- Columbia University (US)
Publication Details
- Journal
- Cancers
- Published
- 2024-10-14
- DOI
- https://doi.org/10.3390/cancers16203480
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Breast Cancer Research Foundation
- Siemens Healthineers
- Genentech