Abstract: Objective To study the application value of linear support vector machine (SMV) prediction model in judging the degree of pathological differentiation of primary hepatocellular carcinomaliver cancer (HCC). Methods A total of 78 patients who were diagnosed with HCC from June 2018 to June 2020 in Wuxi Fifth People's Hospital were selected. All patients underwent CT texture analysis, selected the characteristics of arterial phase, and used the SMV prediction model to judge the degree of HCC pathological differentiation. Taking postoperative pathological examination results as the gold standard, Kappa test was used to determine the consistency of the arterial phase SVM in predicting the degree of tumor differentiation in CT texture analysis. Results The degree of tumor differentiation of 78 HCC patients was poorly differentiated, moderately differentiated, and well differentiated, accounting for 25.64 %, 34.62 %, and 39.74 % of the total. The arterial phase SVM judged that the tumors of HCC patients were poorly differentiated, moderately differentiated, and highly differentiated, and the results of pathological examination were relatively consistent. The Kappa values were 0.835, 0.860, and 0.892, respectively. The sensitivity, specificity and accuracy of arterial SVM in predicting HCC differentiation were 85.71 %, 96.49 % and 93.59 %, respectively. The sensitivity, specificity and accuracy of in predicting HCC differentiation were 89.29 %, 96.00 % and 93.59 %, respectively, . The sensitivity, specificity and accuracy oinf predicting high differentiation of HCC were 96.55 %, 93.88 % and 94.87 %, respectively. Conclusion The SMV prediction model has a high value in judging the degree of HCC pathological differentiation.

Key words: CT texture analysis, lLinear support vector machine, pPrimary hepatocellular carcinoma liver cancer, tTumor differentiation