Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients
Accordingly, machine learning methods could support clinicians in discriminating between endometrial carcinosarcoma patients at low-risk or high-risk of recurrence, in a non-invasive and inexpensive way. To the best of our knowledge, this is the first study proposing a preliminary approach addressing this task.
Authors
- Vera Loizzi (ORCID: https://orcid.org/0000-0002-4006-6421)
- Gerardo Cazzato (ORCID: https://orcid.org/0000-0003-0325-4316)
- Maria Colomba Comes (ORCID: https://orcid.org/0000-0003-2903-4389)
- Erica Silvestris (ORCID: https://orcid.org/0000-0001-7415-5986)
- Annarita Fanizzi (ORCID: https://orcid.org/0000-0002-2729-9896)
- Emanuele Naglieri
- Giuseppe Accogli (ORCID: https://orcid.org/0000-0003-4351-7395)
- Alessia Cafforio
- Giorgio De Nunzio (ORCID: https://orcid.org/0000-0002-1998-0286)
- Francesca Arezzo (ORCID: https://orcid.org/0000-0002-8914-9594)
- Elsa Vitale (ORCID: https://orcid.org/0000-0002-9738-3479)
- Raffaella Massafra (ORCID: https://orcid.org/0000-0001-8588-261X)
- Samantha Bove (ORCID: https://orcid.org/0000-0001-7772-7513)
- Brigida Anna Maiorano (ORCID: https://orcid.org/0000-0001-5423-3158)
- Gennaro Cormio (ORCID: https://orcid.org/0000-0001-5745-372X)
- Antonio Lupo
Institutions
- University of Salento (IT)
- Casa Sollievo della Sofferenza (IT)
- Istituto Tumori Bari (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- University of Bari Aldo Moro (IT)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2024-12-06
- DOI
- https://doi.org/10.3389/frai.2024.1388188
- Citations
- 1
- Primary Topic
- Endometrial and Cervical Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.58
Funders
- Ministero della Salute