Intelligent ensemble learning-enhanced finite element modeling for precision thermal ablation in cancer therapy
Combining FEM simulations with supervised learning provides a scalable, data-driven framework for precision MWA. This approach improves predictive reliability, accelerates treatment planning, and enables adaptive, optimized thermal therapies, offering potential for enhanced clinical outcomes in minimally invasive oncological interventions.
Authors
- Frank Hübner
- Ayesha Sohail (ORCID: https://orcid.org/0000-0001-6835-6212)
- Peter Kim (ORCID: https://orcid.org/0000-0002-1492-4744)
- Hongfei Qiao
- Thomas Josef Vogl
Institutions
- Goethe University Frankfurt (DE)
- The University of Sydney (AU)
- University Hospital Frankfurt (DE)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-11-15
- DOI
- https://doi.org/10.1016/j.cmpb.2025.109157
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00