Published online Aug 16, 2025. doi: 10.12998/wjcc.v13.i23.101742
Revised: March 9, 2025
Accepted: April 25, 2025
Published online: August 16, 2025
Processing time: 250 Days and 18.9 Hours
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.
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.
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).
Among the 122 patients, 72 patients were effectively treated via 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
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.
Core Tip: Hepatocellular carcinoma (HCC) is often diagnosed at advanced stages, where transarterial chemoembolization (TACE) serves as a key therapy. However, nearly 50% of patients show poor TACE response due to tumor heterogeneity. This study integrates preoperative computed tomography radiomics and clinical factors to build a predictive model for TACE efficacy. Radiomics noninvasively extracts quantitative imaging features reflecting tumor pathophysiology, enabling precise assessment of treatment response. The combined model stratifies patients by predicted risk, guiding timely transition to alternative therapies (e.g., targeted drugs or immunotherapy) for non-responders. This approach enhances personalized HCC management, optimizes resource allocation, and improves survival outcomes through data-driven clinical decision-making.