Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study
Lisa Pilgram, Kai Yang, Ana-Alicia Beltran-Bless, Gregory R Pond, Lisa Vandermeer, John Hilton, Marie-France Savard, Andreanne LeBlanc, Lois Shepherd, Bingshu Chen, John MS Bartlett, Karen J Taylor, Jane Bayani, Sarah Barker, Melanie Spears, Cornelis JH van der Velde, Elma Meershoek-Klein Kranenbarg · 2026
Read Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study by Lisa Pilgram, Kai Yang, Ana-Alicia Beltran-Bless, Gregory R Pond, Lisa Vandermeer, John Hilton, Marie-France Savard, Andreanne LeBlanc, Lois Shepherd, Bingshu Chen, John MS Bartlett, Karen J Taylor, Jane Bayani, Sarah Barker, Melanie Spears, Cornelis JH van der Velde, Elma Meershoek-Klein Kranenbarg (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13125978.txt.
Description
BackgroundPrognostic information is essential for decision-making in breast cancer management. In recent years, trials and clinical practice have emphasized genomic prognostication tools, despite clinicopathological methods being more affordable and accessible. PREDICT v3 is one such tool with promising results across cohorts.
Catalog record
| CREATOR | Lisa Pilgram, Kai Yang, Ana-Alicia Beltran-Bless, Gregory R Pond, Lisa Vandermeer, John Hilton, Marie-France Savard, Andreanne LeBlanc, Lois Shepherd, Bingshu Chen, John MS Bartlett, Karen J Taylor, Jane Bayani, Sarah Barker, Melanie Spears, Cornelis JH van der Velde, Elma Meershoek-Klein Kranenbarg |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 69 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |