Prediction of the degree of differentiation of hepatocellular carcinoma before surgery based on clinical data and MRI image features / 中华肝胆外科杂志

ABSTRACT

Objective:To explore the value of clinical data and MRI image features in predicting and analyzing the degree of differentiation of hepatocellular carcinoma (HCC).Methods:The clinical and imaging data of 180 patients with surgical outcomes of HCC from March 2015 to June 2019 in the General Hospital of Ningxia Medical University were retrospectively analyzed. Alpha-fetoprotein (AFP)、aspartate aminotransferase (AST)、D-dimer、clinical stage、tumor length、apparent diffusion coefficient(ADC)、enhancement types and so on the clinical and imaging data of the poorly differentiated and non-differentiated HCC were compared and analyzed. Multivariate logistic regression was used to predict independent risk factors for poorly differentiated HCC.Results:Of the 180 HCC patients, 121 were moderately and highly differentiated, and 59 were poorly differentiated. Univariate analysis showed that the patient’s age, gender, AFP, AST, D-dimer level, clinical stage, Child-Pugh score, tumor length, whether the capsule was complete, tumor apparent diffusion coefficient, the maximum level ADC value, enhancement type with HCC differentiation degree were correlated(all P<0.05). Multivariate logistic regression analysis showed that the patients' gender ( OR=4.524, P<0.05), clinical stage ( OR=5.598, P<0.05), D-dimer ( OR=8.576, P<0.05), HCC diameter ( OR=0.498, P<0.05), enhancement types ( OR=2.988, P<0.05), tumour ADC value ( OR=0.059, P<0.05) were independent of poorly differentiated HCC risk factor. Conclusion:MRI image features can be used as an effective indicator to predict the degree of HCC differentiation before surgery. It is more valuable to accurately predict the degree of HCC combined with D-dimer and AFP value.