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

被引:0
作者
Jia-Wei Zhong
Dan-Dan Nie
Ji-Lan Huang
Rong-Guang Luo
Qing-He Cheng
Qiao-Ting Du
Gui-Hai Guo
Liang-Liang Bai
Xue-Yun Guo
Yan Chen
Si-Hai Chen
机构
[1] The First Affiliated Hospital of Nanchang University,Department of Gastroenterology, Digestive Disease Hospital
[2] The First Affiliated Hospital of Nanchang University,Medical Imaging Department
[3] The First Affiliated Hospital of Nanchang University,Department of Interventional Medicine
[4] Fengcheng People’s Hospital,Department of Gastroenterology
[5] The First Affiliated Hospital of Nanchang University,Postdoctoral Innovation Practice Base
来源
Discover Oncology | / 14卷
关键词
Hepatocellular carcinoma; Transarterial chemoembolization; First response; Individual prediction;
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摘要
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.
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