Prediction of the efficacy of first transarterial chemoembolization for advanced hepatocellular carcinoma a clinical-radiomics model.

BACKGROUND

Hepatocellular carcinoma (HCC) is a common tumor with a poor prognosis. Early intervention is essential; thus, good prognostic markers to identify patients who benefit from first transarterial chemoembolization (TACE) are needed.

AIM

To investigate the efficacy of computed tomography (CT) radiomics in predicting the success of the first TACE in patients with advanced HCC and to develop an early prediction model based on clinical radiomics features.

METHODS

Data from 122 patients with advanced HCC treated with TACE were analyzed. Intratumoral and peritumoral areas on arterial and venous CT images were selected to extract radiomic features, which were screened in the training cohort using the minimum redundancy maximum correlation. Then, support vector machines were used to construct the model. To construct a receiver operating characteristic curve, the predictive efficacy of each model was evaluated on the basis of the area under the curve (AUC).

RESULTS

Among the 122 patients, 72 patients were effectively treated TACE, and in 50 patients, this treatment was ineffective. In the radiomics model, the areas under the curve of the venous phase model were 0.867 (95%CI: 0.790-0.940) in the training cohort and 0.755 (0.600-0.910) in the validation cohort, indicating good predictive efficacy. The multivariate logistic regression results indicated that preoperative alpha-fetoprotein levels ( = 0.01) were a risk factor for TACE. The screened clinical features were combined with the radiomic features to construct a combined model. This combined model had an AUC of 0.92 (0.87-0.95) in the training cohort and 0.815 (0.67-0.95) in the validation cohort.

CONCLUSION

CT radiomics has good value in predicting the efficacy of the first TACE treatment in patients with HCC. The combined model was a better tool for predicting the first TACE efficacy in patients with advanced HCC and could provide an efficient predictive tool to help with the selection of patients for TACE.