Thermal Ablation Treatment Detection by means of Machine Learning

Thermal ablation refers to the destruction of tissue by extreme hyperthermia (elevated tissue temperatures) or hypothermia (depressed tissue temperatures). Radiofrequency ablation and microwave ablation are treatments that use image guidance to place a needle through the skin into a liver tumor. In radiofrequency ablation, high-frequency electrical currents are passed through an electrode in the needle, creating a small region of heat. In microwave ablation, microwaves are created from the needle to create a small region of heat. The heat destroys the liver cancer cells. Both the thermal ablation techniques are effective treatment options for patients who might have difficulty with surgery or those whose tumors are less than one and a half inches in diameter. In this paper, with the aim to help doctors in choosing of the best suitable thermal ablation treatment (i.e. with radiofrequency of micro waves), we propose a method exploiting machine learning to automatically predict the thermal ablation treatment starting from a set of features obtained from patients medical analysis.