Vries, H.S.; van Praagh, G.D.; Nienhuis, P.H.; Alic, L.; Slart, R.H.J.A.
A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [18F]FDG-PET Images During Follow-Up. Diagnostics 2025, 15, 367.
https://doi.org/10.3390/diagnostics15030367
AMA Style
Vries HS, van Praagh GD, Nienhuis PH, Alic L, Slart RHJA.
A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [18F]FDG-PET Images During Follow-Up. Diagnostics. 2025; 15(3):367.
https://doi.org/10.3390/diagnostics15030367
Chicago/Turabian Style
Vries, Hanne S., Gijs D. van Praagh, Pieter H. Nienhuis, Lejla Alic, and Riemer H. J. A. Slart.
2025. "A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [18F]FDG-PET Images During Follow-Up" Diagnostics 15, no. 3: 367.
https://doi.org/10.3390/diagnostics15030367
APA Style
Vries, H. S., van Praagh, G. D., Nienhuis, P. H., Alic, L., & Slart, R. H. J. A.
(2025). A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [18F]FDG-PET Images During Follow-Up. Diagnostics, 15(3), 367.
https://doi.org/10.3390/diagnostics15030367