In Hepatobiliary & pancreatic diseases international : HBPD INT
BACKGROUND : Although transarterial chemoembolization (TACE) is the first-line therapy for intermediate-stage hepatocellular carcinoma (HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice.
METHODS : A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting (XGBoost) with 5-fold cross-validation. The Shapley additive explanations (SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model's performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups.
RESULTS : A third of the patients (81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 0.759, 0.885, 0.906 [95% confidence interval (CI): 0.859-0.953] in the training and 0.826, 0.776, and 0.894 (95% CI: 0.815-0.972) in the testing cohort, respectively.
CONCLUSIONS : The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment.
Wang Dan-Dan, Zhang Jin-Feng, Zhang Lin-Han, Niu Meng, Jiang Hui-Jie, Jia Fu-Cang, Feng Shi-Ting
2022-Nov-22
Hepatocellular carcinoma, Machine learning, Prediction, Radiomics, Transarterial chemoembolization
