Patient-specific biophysical AI-powered modeling for predicting RFA volumes in HCC.
- DOI
- 10.1186/s41747-026-00839-3
- Published
- 2026 Nov 3
- Container
- European radiology experimental
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
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Cite this work
BibTeX
@article{allodium:10.1186/s41747-026-00839-3,
title = {Patient-specific biophysical AI-powered modeling for predicting RFA volumes in HCC.},
author = {Audigier C and Akkari FG and Meister F and Tonglet A and Maréchal B and Frings O and Duran R},
year = {2026},
journal = {European radiology experimental},
doi = {10.1186/s41747-026-00839-3},
url = {https://doi.org/10.1186/s41747-026-00839-3}
}RIS
TY - JOUR TI - Patient-specific biophysical AI-powered modeling for predicting RFA volumes in HCC. AU - Audigier C AU - Akkari FG AU - Meister F AU - Tonglet A AU - Maréchal B AU - Frings O AU - Duran R PY - 2026 JO - European radiology experimental DO - 10.1186/s41747-026-00839-3 UR - https://doi.org/10.1186/s41747-026-00839-3 ER -
APA
C, A., FG, A., F, M., A, T., B, M., O, F., & R, D. (2026). Patient-specific biophysical AI-powered modeling for predicting RFA volumes in HCC.. European radiology experimental. https://doi.org/10.1186/s41747-026-00839-3