Abstract

To evaluate the value of preoperative MRI features and laboratory indicators in predicting the early response of hepatocellular carcinoma (HCC) to transcatheter arterial chemoembolization (TACE) combined with high-intensity focused ultrasound (HIFU) treatment and to establish a preoperative prediction model. A total of 188 patients with 223 tumors who underwent TACE/HIFU treatment from January 2011 to June 2017 were included. Tumors were divided into three groups (< 2 cm, 2 - 5 cm,> 5 cm) and classified as non-complete response (NCR) and complete response (CR) cohorts according to the Response Evaluation Criteria in Cancer of the Liver (RECICL) 2015 revised version. Univariate analysis and multivariate logistic regression analysis were used to determine independent predictors, and receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic power of each predictor. The prediction model was derived on the β coefficient of the multivariate regression analysis of the predictors. Irregular margins in the 2 - 5 cm group were closely related to early NCR. Irregular margins, arterial peritumoral enhancement and abnormal alpha-fetoprotein (AFP) were independent predictors of early NCR in the > 5 cm group. The prediction model of this group suggests that irregular margins combined with arterial peritumoral enhancement and abnormal AFP combined with irregular margins and arterial peritumoral enhancement predict an increased risk of early NCR. Irregular margins of 2 - 5 cm tumors and irregular margins, arterial peritumoral enhancement, and abnormal AFP of tumors > 5 cm can be applied to predict the early response of HCC to TACE/HIFU treatment. TACE combined with HIFU treatment may be able to significantly improve survival in patients with advanced HCC. Conventional MRI features and laboratory indicators are readily available without complex post-processing. It is feasible to predict the response of HCC after TACE/HIFU treatment based on preoperative conventional MRI features and laboratory indicators, the combination of multiple features predicts high-risk of non-complete response.

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