Academic Journal
A machine learning surrogate model for fast approximation of simulated microwave ablation zones.
| Τίτλος: | A machine learning surrogate model for fast approximation of simulated microwave ablation zones. |
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| Συγγραφείς: | Karkanis N; Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece., Samaras T; Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece. |
| Πηγή: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2026 Jun 01; Vol. 12 (3). Date of Electronic Publication: 2026 Jun 01. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing Ltd., [2015]- |
| Ιατρικοί όροι (MeSH): | Microwaves*/therapeutic use , Ablation Techniques*/methods , Machine Learning* , Computer Simulation*, Finite Element Analysis ; Humans ; Liver Neoplasms ; Neural Networks, Computer ; Algorithms |
| Περίληψη: | Microwave ablation (MWA) is a minimally invasive therapy for liver, lung, and kidney tumors. Computational modeling using finite element methods (FEMs) can simulate ablation zones but is computationally expensive and unsuitable for interactive use. This study develops and evaluates a machine learning surrogate model to rapidly approximate 50 °C isothermal contours from FEM simulations of MWA, enabling faster computational exploration. A feedforward neural network was trained on 2625 FEM simulations incorporating tumor geometry and dielectric properties. Predictions were compared with FEM-derived ablation zones, demonstrating high accuracy and strong correlation (R> 0.95), with error distributions centered near zero. The proposed framework enables rapid approximation of electrothermal simulation outputs, significantly reducing computational cost while maintaining accuracy within the simulated parameter space. However, the model is trained and evaluated exclusively on simulation data and therefore reflects interpolation within this predefined domain. This study represents a simulation-based proof-of-concept, and the proposed model is intended to approximate the outputs of a simplified computational framework rather than directly predict clinical outcomes. Further validation using experimental and clinical data is required before practical applicability can be established. The proposed approach should be interpreted as a surrogate model of the underlying simulation framework rather than a direct predictor of clinical outcomes. (© 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Contributed Indexing: | Keywords: finite element method; machine learning; microwave ablation; neural network; thermal ablation; treatment planning |
| Entry Date(s): | Date Created: 20260519 Date Completed: 20260716 Latest Revision: 20260716 |
| Update Code: | 20260717 |
| DOI: | 10.1088/2057-1976/ae6fc0 |
| PMID: | 42155488 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2057-1976 |
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| DOI: | 10.1088/2057-1976/ae6fc0 |