Machine Learning–Based Survival Prediction Tool for Adrenocortical Carcinoma
S-GRAS parameters can efficiently predict outcome in patients with ACC, even using a robust ML model approach. Our web app instantly estimates the mortality and disease progression for patients with ACC, representing an accessible tool to drive personalized management decisions in clinical practice.
Authors
- Cristina L. Ronchi (ORCID: https://orcid.org/0000-0001-5020-2071)
- Yasir Elhassan (ORCID: https://orcid.org/0000-0002-2735-6053)
- Barbara Altieri (ORCID: https://orcid.org/0000-0003-2616-3249)
- Alessandro Prete (ORCID: https://orcid.org/0000-0002-4821-0336)
- Emre Sedar Saygılı (ORCID: https://orcid.org/0000-0003-0022-5704)
- Juliane Lippert
Institutions
- Çanakkale Onsekiz Mart Üniversitesi (TR)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- University of Würzburg (DE)
- Queen Elizabeth Hospital Birmingham (GB)
- Institute of Human Genetics (PL)
- Universitätsklinikum Würzburg (DE)
- NIHR Birmingham Biomedical Research Centre (GB)
- HealthPartners (US)
- University of Birmingham (GB)
Publication Details
- Journal
- The Journal of Clinical Endocrinology & Metabolism
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1210/clinem/dgaf096
- Citations
- 5
- Primary Topic
- Adrenal and Paraganglionic Tumors
- Type
- article
- Field-Weighted Citation Impact
- 10.03