Comparison of Effects of Radiofrequency Ablation of Liver Cancer Guided by CT Images Based on Deep Learning Algorithm
Objective. To investigate the paper radiofrequency ablation (RFA) CT-guided feasibility of hepatocellular carcinoma (primary liver cancer) treatment, safety, and clinical efficacy of the use of deep learning algorithms. Method. A total of 47 cases of primary liver cancer patients were included: 21 cases of CT-guided liver lesions in line with RFA (C-CT group) and, in the same period, 26 cases of spiral CT-guided liver lesions in line with RFA (S-CT group). Two groups of patients were recorded immediately after the total operation time and ablation time, the acceptable radiation dose was observed in the incidence of postoperative complications of 7d, and the postoperative hospital stay was recorded to evaluate the efficacy of the treatment of lesions in 1, 3, and 6 months after RFA. Results. All 47 patients were successful; two technical success rates were 100%. There was a significant difference ( P < 0.05) in the total operation time groups, ablation time, and acceptable radiation dose. And there was no significant difference ( P < 0.05) in postoperative complications of 7d groups, postoperative hospital stay, and local disease control. There was a significant difference ( P < 0.05) in the S-CT group, seven ablation residual or recurrent lesions during the follow-up ratio of 26.9%, and C-CT groups compared with only 14.3%. Conclusion. CT-guided RFA treatment of primary liver cancer patients is safe, effective, and superior to the conventional spiral CT-guided ablation lesion site-specific terms.