Development of two machine learning models to predict conversion from primary HER2-0 breast cancer to HER2-low metastases: a proof-of-concept study
This work represents one of the first proof-of-concept applications of machine learning models to predict a highly relevant phenomenon for drug access in modern BC oncology. Starting with an explainable model and subsequently integrating it with an ensemble approach enabled us to enhance performance while maintaining transparency, explainability, and intelligibility.
Authors
- A. Vingiani
- Federica Miglietta (ORCID: https://orcid.org/0000-0001-6203-874X)
- Fabio Girardi (ORCID: https://orcid.org/0000-0001-7094-7986)
- Angelo Paolo Dei Tos (ORCID: https://orcid.org/0000-0002-1228-8940)
- Andrea Botticelli (ORCID: https://orcid.org/0000-0002-6425-9893)
- Valentina Guarneri (ORCID: https://orcid.org/0000-0002-2375-8397)
- Filippo Zanghi
- Matteo Fassan (ORCID: https://orcid.org/0000-0001-6515-5482)
- Marco Marino (ORCID: https://orcid.org/0000-0002-7470-7493)
- Tommaso Giarratano (ORCID: https://orcid.org/0000-0002-6746-8877)
- G. Griguolo
- G. Pruneri
- C. Vernieri
- Maria Vittoria Dieci (ORCID: https://orcid.org/0000-0002-3967-9861)
- Carlo Alberto Giorgi (ORCID: https://orcid.org/0000-0001-5406-0357)
- Giuseppe Fotia
- D. Massa
- Federico Piacentini (ORCID: https://orcid.org/0000-0001-8969-638X)
- A. Collesei
- M. Cacciatore
Institutions
- University of Modena and Reggio Emilia (IT)
- AULSS 2 Marca Trevigiana (IT)
Publication Details
- Journal
- ESMO Open
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1016/j.esmoop.2024.104087
- Citations
- 7
- Primary Topic
- HER2/EGFR in Cancer Research
- Type
- article
- Field-Weighted Citation Impact
- 1.98
Funders
- Ministero della Salute