Development and Validation of a Prediction Model for Hepatitis B Virus-Related Hepatocellular Carcinoma Patients Receiving Postoperative Adjuvant Transarterial Chemoembolization

• Published in: Journal of hepatocellular carcinoma Vol. 10; pp. 1881 - 1895
• Main Authors: Tu, Xinyue, Zhang, Jie, Li, Minjun, Lu, Fei, Wang, Ting, Gong, Wenfeng, Xiang, Bangde
• Format: Journal Article
• Language: English
• Published: Dove 01.01.2023; Dove Medical Press
• Subjects: hepatitis b virus; hepatocellular carcinoma; nomogram; Original Research; prognostic model; transarterial chemoembolization
• ISSN: 2253-5969; 2253-5969
• DOI: 10.2147/JHC.S422565

BackgroundHepatocellular carcinoma (HCC) patients who are at significant risk of tumor recurrence and mortality can benefit from postoperative adjuvant transarterial chemoembolization (PA-TACE). However, the benefits of PA-TACE remain unclear. Herein, we aimed to develop a model for predicting the prognosis of HBV-related patients who undergo PA-TACE and endeavored to guide individualized clinical treatment.MethodsWe included 432 HBV-related patients who underwent PA-TACE after curative resection were included. The dataset was divided into a training set (n=216) and an internal validation set (n=216). For identifying independent risk factors, the least absolute shrinkage and selection operator and univariate and multivariate Cox analyses were performed. We derived a prognostic model from the training set that was internally validated. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and risk stratification were used to evaluate the performance of the nomogram.ResultsPatients undergoing PA-TACE had significantly longer overall survival (OS) than those who did not undergo PA-TACE. Age, albumin levels, macrovascular invasion, tumor size, and, stages of Barcelona Clinic Liver Cancer were identified as independent risk variables and concluded into the nomogram to predict the OS of HBV-related patients who received PA-TACE. The nomogram's C-index values OS were 0.710 and 0.652 in the training and internal validation sets, respectively. Both time-dependent AUC and the calibration curve showed good discrimination and model fitness. The risk score -0.12 was kept as the cut-off value that would accurately divide patients into high-risk and low-risk groups; furthermore, the Kaplan-Meier curve showed a high discriminative ability of the model.ConclusionWe developed a predictive model. comprising a formula and nomogram to predict the OS and provide risk stratification for HBV-related patients undergoing PA-TACE, which could contribute to suitable treatment options for this patient population.