Deep learning [18F]-FDG-PET/CT‑based algorithm for tumor burden estimation in metastatic melanoma patients under immunotherapy
PARS shows potential for treatment decision support with moderate accuracy in lesion detection and lesion volume estimation, but demonstrates significant variability in TB estimation, highlighting the need for further model refinements before clinical adoption.
Authors
- Stephanie Tanadini‐Lang (ORCID: https://orcid.org/0000-0002-4387-1522)
- Matthias Gückenberger (ORCID: https://orcid.org/0000-0002-7146-9071)
- Daniel Abler (ORCID: https://orcid.org/0000-0003-1776-5985)
- Adrien Depeursinge (ORCID: https://orcid.org/0000-0002-2362-0304)
- Sebastian M. Christ (ORCID: https://orcid.org/0000-0001-5330-3820)
- Lorenzo Lo Faro (ORCID: https://orcid.org/0000-0003-3336-8090)
- Simon Burgermeister (ORCID: https://orcid.org/0000-0002-1284-5284)
- Panagiotis Balermpas (ORCID: https://orcid.org/0000-0001-5261-6446)
- Ciro Franzese (ORCID: https://orcid.org/0000-0001-6893-6284)
- Maiwand Ahmadsei (ORCID: https://orcid.org/0000-0002-0646-2762)
- Michel A. Cuendet (ORCID: https://orcid.org/0000-0002-0754-3425)
- Marta Scorsetti (ORCID: https://orcid.org/0000-0003-0320-559X)
- Hubert S. Gabryś (ORCID: https://orcid.org/0000-0001-5657-8432)
- Maksym Fristak
Institutions
- SIB Swiss Institute of Bioinformatics (CH)
- Humanitas University (IT)
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- Cornell University (US)
- University Hospital of Zurich (CH)
- University Hospital of Geneva (CH)
- IRCCS Humanitas Research Hospital (IT)
- Geneva College (US)
- University of Lausanne (CH)
Publication Details
- Journal
- Clinical and Translational Radiation Oncology
- Published
- 2025-10-28
- DOI
- https://doi.org/10.1016/j.ctro.2025.101063
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 0.00