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Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score
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 lackin...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10581972/ https://www.ncbi.nlm.nih.gov/pubmed/37847433 http://dx.doi.org/10.1007/s12672-023-00803-2 |
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author | Zhong, Jia-Wei Nie, Dan-Dan Huang, Ji-Lan Luo, Rong-Guang Cheng, Qing-He Du, Qiao-Ting Guo, Gui-Hai Bai, Liang-Liang Guo, Xue-Yun Chen, Yan Chen, Si-Hai |
author_facet | Zhong, Jia-Wei Nie, Dan-Dan Huang, Ji-Lan Luo, Rong-Guang Cheng, Qing-He Du, Qiao-Ting Guo, Gui-Hai Bai, Liang-Liang Guo, Xue-Yun Chen, Yan Chen, Si-Hai |
author_sort | Zhong, Jia-Wei |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00803-2. |
format | Online Article Text |
id | pubmed-10581972 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-105819722023-10-19 Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score Zhong, Jia-Wei Nie, Dan-Dan Huang, Ji-Lan Luo, Rong-Guang Cheng, Qing-He Du, Qiao-Ting Guo, Gui-Hai Bai, Liang-Liang Guo, Xue-Yun Chen, Yan Chen, Si-Hai Discov Oncol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00803-2. Springer US 2023-10-17 /pmc/articles/PMC10581972/ /pubmed/37847433 http://dx.doi.org/10.1007/s12672-023-00803-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Zhong, Jia-Wei Nie, Dan-Dan Huang, Ji-Lan Luo, Rong-Guang Cheng, Qing-He Du, Qiao-Ting Guo, Gui-Hai Bai, Liang-Liang Guo, Xue-Yun Chen, Yan Chen, Si-Hai Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
title | Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
title_full | Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
title_fullStr | Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
title_full_unstemmed | Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
title_short | Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
title_sort | prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: tacf score |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10581972/ https://www.ncbi.nlm.nih.gov/pubmed/37847433 http://dx.doi.org/10.1007/s12672-023-00803-2 |
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