Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score

Abstract

Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model's efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE.

Keywords: First response; Hepatocellular carcinoma; Individual prediction; Transarterial chemoembolization.

Fig. 1

Flow chart of the selection process. DSA digital subtraction angiography, HCC hepatocellular carcinoma, TACE transarterial chemoembolization

Fig. 2

Texture feature selection using LASSO regression. A, B Showed the shrinkage process of coefficients. LASSO least absolute shrinkage and selection operator, RBC red blood count, WBC white blood cell count, PLT lymphocyte count, platelet; ALB albumin, GLB globulin, TBIL total bilirubin, DBIL direct bilirubin, AST aspartate aminotransferase, ALT alanine aminotransferase, K⁺ potassium, PT prothrombin time, L lymphocyte count, N neutrophil count, INR international normalized ratio, AFP a-fetoprotein, HAVF hepatic arteriovenous fistula, PVTT portal vein tumor thrombus, Number numbers of tumor, Size maximum diameter of tumor

Fig. 3

ROC curve analyses of candidate models and comparing the discrimination ability of BCLC staging, C–P class and ALBI grading in training group (A) and validation group (B). ROC receiver operating characteristic curve, BCLC Barcelona Clinic Liver Cancer, ALBI albumin–bilirubin, C–P Child–Pugh

Fig. 4

Calibration plot of candidate models, including model1 (A, B), model2 (C, D) and model3 (E, F), ALBI grading (G, H), BCLC staging (I, J) and C–P class (K, L) in training group and validation group, respectively. BCLC Barcelona Clinic Liver Cancer, ALBI albumin–bilirubin, C–P Child–Pugh

Fig 5

The nomogram of model3 (A) and the DCA curves of candidate models, BCLC staging, C–P class and ALBI grading in training group (B) and validation group (C). BCLC Barcelona Clinic Liver Cancer, ALBI albumin–bilirubin, C–P Child–Pugh