Integrating CT Radiomics and Clinical Features to Optimize TACE Technique Decision-Making in Hepatocellular Carcinoma
Integrating CT radiomics with clinical variables may help identify the optimal TACE technique for individual HCC patients. This approach holds promise for a more personalized therapy selection and improved response rates beyond standard clinical decision-making.
Authors
- Haluk Morgül (ORCID: https://orcid.org/0000-0001-8789-0277)
- Gesa Helen Pöhler (ORCID: https://orcid.org/0000-0002-3994-1495)
- Jonel Trebicka (ORCID: https://orcid.org/0000-0002-7028-3881)
- Max Masthoff (ORCID: https://orcid.org/0000-0001-5338-9193)
- Moritz Wildgruber (ORCID: https://orcid.org/0000-0002-7228-6963)
- Philipp Schindler (ORCID: https://orcid.org/0000-0002-8098-9853)
- Florian Rennebaum
- Daniel Kaldewey
- Michael Köhler
- Maximilian Irle
Institutions
- University of Münster (DE)
- LMU Klinikum (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Cancers
- Published
- 2025-03-05
- DOI
- https://doi.org/10.3390/cancers17050893
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16