Automatic Generation of Liver Virtual Models with Artificial Intelligence
This automatic digital tool accurately predicts intraoperative LRC and paves the way for an innovative oncology surgery planning. This tool could help orient patients towards appropriate medical centers depending on the predicted LRC level.
Authors
- Chady Salloum (ORCID: https://orcid.org/0000-0001-9282-4418)
- Marc-Michel Rohé
- Éric Vibert (ORCID: https://orcid.org/0000-0003-3297-1947)
- Alexandre Bône (ORCID: https://orcid.org/0000-0003-4442-3876)
- Belkacem Acidi (ORCID: https://orcid.org/0000-0001-9436-5817)
- Omar Ali (ORCID: https://orcid.org/0000-0003-0286-9474)
- Caterina Accardo (ORCID: https://orcid.org/0000-0003-3497-683X)
- Irène Vignon-Clémentel (ORCID: https://orcid.org/0000-0001-9754-4386)
- A. Facque
- P Valleur
Institutions
- Guerbet (France) (FR)
- Center for Science in the Public Interest (United States) (US)
- Inserm (FR)
- Food Safety and Inspection Service (US)
- Centre Inria de Saclay (FR)
- Hôpital Paul-Brousse (FR)
- Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione (IT)
Publication Details
- Journal
- Annals of Surgery
- Published
- 2025-04-11
- DOI
- https://doi.org/10.1097/sla.0000000000006722
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00