An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients
Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.
Authors
- Gerardo Cazzato (ORCID: https://orcid.org/0000-0003-0325-4316)
- Maria Colomba Comes (ORCID: https://orcid.org/0000-0003-2903-4389)
- Annarita Fanizzi (ORCID: https://orcid.org/0000-0002-2729-9896)
- F. Mele (ORCID: https://orcid.org/0000-0001-6777-4682)
- Michele Guida (ORCID: https://orcid.org/0000-0001-8411-0319)
- Ivana De Risi (ORCID: https://orcid.org/0000-0002-6330-797X)
- Livia Fucci (ORCID: https://orcid.org/0000-0002-4096-7211)
- Concetta Saponaro (ORCID: https://orcid.org/0000-0002-8196-3583)
- Maurizio Ressa
- Raffaella Massafra (ORCID: https://orcid.org/0000-0001-8588-261X)
- Sabino Strippoli (ORCID: https://orcid.org/0000-0001-7982-7788)
- Samantha Bove (ORCID: https://orcid.org/0000-0001-7772-7513)
- Clara Soranno
- Carmen Colangiuli
- Ileana De Roma
- Rosita Tinelli
- Alfredo Zito
Institutions
- Istituto Tumori Bari (IT)
- University of Bari Aldo Moro (IT)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2024-09-12
- DOI
- https://doi.org/10.1186/s12967-024-05629-2
- Citations
- 6
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.70
Funders
- Ministero della Salute