Machine Learning approach in optimal localization of tumor using a novel SIW - Based antenna for improvement of ablation zone in Hepatocellular carcinoma

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Singh, Suyash Kumar, Yadav, Amar Nath
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Ablation Zone; Algorithms; Antennas; Applicators; Cancer; Cat Boost; Clinical diagnosis; Conductors; Finite element method; Heating; Helical antennas; Impedance; Liver cancer; Machine learning; Microwave Ablation Antenna; Microwave antennas; Necrosis; Radio frequency; Substrate integrated waveguides; Support Vector Regression; Tumors; XG Boost
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3257869

In recent years, hepatocellular carcinoma has been the leading cause of cancer-related mortality and thermal ablation procedures such as MWA (microwave ablation) and RFA (Radiofrequency ablation) provide a viable alternative to radiation and surgical excision. Due to uneven ablation and tissue charring in RFA, MWA offers faster and uniform heating as a result of its higher operating frequency. In MWA, the antenna probe heats the tissue to the point of cell necrosis using electromagnetic heating. In this aspect, a substrate-integrated waveguide (SIW) antenna is designed to work at 2.45 GHz. Further, a finite element method (FEM) is employed to model the probe and the liver tissue environment. Within 10 minutes of application time, a maximum ablation diameter of 32 mm (transversal-T) and 25 mm (axial-A) is achieved at 20W of power. Since the position of the applicator probe is of utmost importance, the relationship between the percentage of ablation, probe tip position, and tumor diameter is evaluated using ML- algorithm to estimate the ideal probe location for maximal tumor ablation and thus may improve clinical outcome.