Intelligent Algorithm-Based Ultrasound Images in Evaluation of Therapeutic Effects of Radiofrequency Ablation for Liver Tumor and Analysis on Risk Factors of Postoperative Infection

Abstract

This research aimed to explore the therapeutic effects of radiofrequency ablation (RFA) for liver tumors and to investigate the postoperative infection factors. Specifically, 80 patients with liver tumors undergoing ultrasound-guided FRA were selected as research subjects. They were diagnosed in the hospital. An intelligent fitting (IF) algorithm was compared with a genetic algorithm (GA) and applied to the RFA of the 80 patients. It was found that the running time of the IF algorithm was about 0.2 times than that of the GA, demonstrating better global searching capabilities. The mean diameter of single liver tumors was (3.45 ± 1.24) cm, and the complete ablation rate of tumors with diameters less than 3 cm was 87.88%, that of tumors with diameters of 3–5 cm was 72.92%, and that of tumors with a diameter of more than 5 cm was 63.33%. Posttreatment, the AST level decreased significantly and the ALB level increased significantly, and the difference was notable ( $P<\mathit{0.05}$ P<); the TBIL level (36.8 ± 9.7 umol/L) was lower than prior treatment (17.9 ± 8.5 umol/L) and the ALT level (45.2 ± 6.8 g/L) was lower than prior treatment (19.6 ± 5.7 g/L), showing a notable difference ( $P<\mathit{0.05}$ P<). The diameter, whether there was great vessel invasion, and TNM staging were associated with infection after RFA, and the difference was notable. The ultrasound images can effectively evaluate the therapeutic effects of RFA and the degree of inactivation of liver tumors. In addition, the tumor stage was an independent risk factor for postoperative infection.