Novel tumor localization model and prediction of ablation zone using an intertwined helical antenna for the treatment of hepatocellular carcinoma

• Published in: International journal for numerical methods in biomedical engineering Vol. 39; no. 4; pp. e3686 - n/a
• Main Authors: Singh, Suyash Kumar, Yadav, Amar Nath
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2023; Wiley Subscription Services, Inc
• Subjects: Ablation; Ablation Techniques - methods; ablation zone; Algorithms; Artificial neural networks; Carcinoma, Hepatocellular - surgery; Curve fitting; Electrical conductivity; Electrical resistivity; finite element analysis; Finite element method; Helical antennas; Hepatocellular carcinoma; Humans; Liver - surgery; Liver cancer; Liver Neoplasms - surgery; Localization; Mathematical models; microwave ablation antenna; Models, Theoretical; Neural networks; numerical simulation; Parameters; Permittivity; Polynomials; Reflectance; Resonant frequencies; Temperature; Temperature dependence; Thermal conductivity; Thermal properties; Thermodynamic properties; tumor detection; Tumors; ablation zone; microwave ablation antenna; tumor detection; finite element analysis; numerical simulation
• ISSN: 2040-7939; 2040-7947
• DOI: 10.1002/cnm.3686

Hepatocellular carcinoma has been the leading cause of death in recent centuries and with the advent of newer technologies, several thermal and cryo‐ablation techniques have been introduced in the recent past. In this regard, microwave ablation has developed into a promising method for thermal ablation technique. However, due to clinical obligations, in‐vivo analysis is not feasible and ex‐vivo analysis is inaccurate due to changes in the electrical and thermal properties of the tissue. Therefore, in this study, temperature‐dependent permittivity, electrical conductivity, and thermal conductivity along with phase change effect due to temperature reaching above 100°C are incorporated using finite element method model. Further, using an intertwined normal mode helical antenna ablation probe, a change in resonant frequency (Δf) and reflection coefficient (ΔS11) from the actual value (antenna parameter in the air at 5 GHz) is modeled using second‐order polynomial curve fitting to predict the surrounding permittivity in the range of 30–70. A maximum deviation of 0.8 value in permittivity from the actual value is observed. However, to obtain a generalized methodology, XG Boost and CAT Boost algorithms are used. Further, since ablation diameter plays a crucial role in achieving optimal tumor ablation, an artificial neural network (ANN) algorithm with three different optimizers is incorporated to predict ablation diameter using five critical parameters. Such an ANN algorithm which can predict the transversal and axial ablation zone may provide optimal ablation outcomes.