Intelligent Algorithm-Based Computed Tomography Image Features in Diagnosis of the Effect of Radiofrequency Ablation in Liver Cancer

Abstract

To provide a reference for finding a reasonable evaluation method for treatment effect of radiofrequency ablation (RFA), computed tomography (CT) image optimized by the intelligent segmentation algorithm was utilized to evaluate the liver condition of hepatocellular carcinoma (HCC) patients after RFA and to estimate the patient’s prognosis. Eighty-eight patients with HCC who needed RFA surgery after diagnosis in our hospital were selected. The CT images before optimization were set as the control group; the CT images after optimization were set as the observation group. Comprehensive diagnosis was taken as the gold standard to compare the ablation range and residual lesions under CT scans before and after surgery. The results showed that the consistency of the two sets of CT images was compared with comprehensive diagnosis under different diameters of the lesion. The difference between the two groups was not statistically considerable when the diameter of the lesion was less than 50 mm ( $P>0.05$ ). For lesions larger than 50 mm in diameter, the consistency of the observation group (83%) was remarkably higher than that of the control group (40%), and the difference was substantial ( $P<0.05$ ). The kappa value of the observation group was 0.84 and that of the control group was 0.78. The kappa value of observation group was better than the control group, with considerable difference ( $P<0.05$ ). In conclusion, the diagnostic effect of CT image based on intelligent segmentation algorithm was superior to conventional diagnosis when the diameter of the lesion was larger than 50 mm. Moreover, the overall improvement rate of patients after RFA treatment was far greater than the recurrence rate, indicating that the clinical adoption of RFA was very meaningful.