Deep-Learning-Based Prediction of t(11;14) in Multiple Myeloma H&E-Stained Samples
These findings suggest that this AI approach could facilitate rapid screening for FISH analysis, although further enhancements are necessary for its clinical application in MM management.
Authors
- Svetlana Trestman
- Tomer Ziv‐Baran (ORCID: https://orcid.org/0000-0002-2589-8473)
- Dov Hershkovitz (ORCID: https://orcid.org/0000-0002-7954-4486)
- Tamir Shragai (ORCID: https://orcid.org/0000-0002-3741-689X)
- Yael Chava Cohen (ORCID: https://orcid.org/0000-0002-9061-7287)
- Irit Avivi (ORCID: https://orcid.org/0000-0001-8663-444X)
- Nadav Kerner (ORCID: https://orcid.org/0009-0002-6288-3059)
- Hila Lederman Nachmias
Institutions
- Tel Aviv University (IL)
- Tel Aviv Sourasky Medical Center (IL)
Publication Details
- Journal
- Cancers
- Published
- 2025-05-22
- DOI
- https://doi.org/10.3390/cancers17111733
- Citations
- 2
- Primary Topic
- Multiple Myeloma Research and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 3.54