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Development and validation of prognostic nomograms for large hepatocellular carcinoma after HAIC
BACKGROUND AND AIMS: Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for large hepatocellular carcinoma (HCC). However, post-HAIC prognosis can vary in different patients due to tumor heterogeneity. Herein, w...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126656/ https://www.ncbi.nlm.nih.gov/pubmed/37113732 http://dx.doi.org/10.1177/17588359231163845 |
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author | Yao, Wang Wei, Ran Jia, Jia Li, Wang Zuo, Mengxuan Zhuo, Shuqing Shi, Ge Wu, Peihong An, Chao |
author_facet | Yao, Wang Wei, Ran Jia, Jia Li, Wang Zuo, Mengxuan Zhuo, Shuqing Shi, Ge Wu, Peihong An, Chao |
author_sort | Yao, Wang |
collection | PubMed |
description | BACKGROUND AND AIMS: Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for large hepatocellular carcinoma (HCC). However, post-HAIC prognosis can vary in different patients due to tumor heterogeneity. Herein, we established two nomogram models to assess the survival prognosis of patients after HAIC combination therapy. METHODS: A total of 1082 HCC patients who underwent initial HAIC were enrolled between February 2014 and December 2021. We built two nomogram models for survival prediction: the preoperative nomogram (pre-HAICN) using preoperative clinical data and the postoperative nomogram (post-HAICN) based on pre-HAICN and combination therapy. The two nomogram models were internally validated in one hospital and externally validated in four hospitals. A multivariate Cox proportional hazards model was used to identify risk factors for overall survival (OS). The performance outcomes of all models were compared by area under the receiver operating characteristic curve (AUC) analysis with the DeLong test. RESULTS: Multivariable analysis identified larger tumor size, vascular invasion, metastasis, high albumin–bilirubin grade, and high alpha-fetoprotein as indicators for poor prognosis. With these variables, the pre-HAICN provided three risk strata for OS in the training cohort: low risk (5-year OS, 44.9%), middle risk (5-year OS, 20.6%), and high risk (5-year OS, 4.9%). The discrimination of the three strata was improved significantly in the post-HAICN, which included the above-mentioned factors and number of sessions, combination with immune checkpoint inhibitors, tyrosine kinase inhibitors, and local therapy (AUC, 0.802 versus 0.811, p < 0.001). CONCLUSIONS: The nomogram models are essential to identify patients with large HCC suitable for treatment with HAIC combination therapy and may potentially benefit personalized decision-making. LAY SUMMARY: Hepatic arterial infusion chemotherapy (HAIC) provides sustained higher concentrations of chemotherapy agents in large hepatocellular carcinoma (HCC) by hepatic intra-arterial, result in better objective response outperformed the intravenous administration. HAIC is significantly correlated with favorable survival outcome and obtains extensive support in the effective and safe treatment of intermediate advanced-stage HCC. In view of the high heterogeneity of HCC, there is no consensus regarding the optimal tool for risk stratification before HAIC alone or HAIC combined with tyrosine kinase inhibitors or immune checkpoint inhibitors treatment in HCC. In this large collaboration, we established two nomogram models to estimate the prognosis and evaluate the survival benefits with different HAIC combination therapy. It could help physicians in decision-making before HAIC and comprehensive treatment for large HCC patients in clinical practice and future trials. |
format | Online Article Text |
id | pubmed-10126656 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101266562023-04-26 Development and validation of prognostic nomograms for large hepatocellular carcinoma after HAIC Yao, Wang Wei, Ran Jia, Jia Li, Wang Zuo, Mengxuan Zhuo, Shuqing Shi, Ge Wu, Peihong An, Chao Ther Adv Med Oncol Artificial intelligence and radiomics in oncology: building the evidence base and implementing into practice BACKGROUND AND AIMS: Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for large hepatocellular carcinoma (HCC). However, post-HAIC prognosis can vary in different patients due to tumor heterogeneity. Herein, we established two nomogram models to assess the survival prognosis of patients after HAIC combination therapy. METHODS: A total of 1082 HCC patients who underwent initial HAIC were enrolled between February 2014 and December 2021. We built two nomogram models for survival prediction: the preoperative nomogram (pre-HAICN) using preoperative clinical data and the postoperative nomogram (post-HAICN) based on pre-HAICN and combination therapy. The two nomogram models were internally validated in one hospital and externally validated in four hospitals. A multivariate Cox proportional hazards model was used to identify risk factors for overall survival (OS). The performance outcomes of all models were compared by area under the receiver operating characteristic curve (AUC) analysis with the DeLong test. RESULTS: Multivariable analysis identified larger tumor size, vascular invasion, metastasis, high albumin–bilirubin grade, and high alpha-fetoprotein as indicators for poor prognosis. With these variables, the pre-HAICN provided three risk strata for OS in the training cohort: low risk (5-year OS, 44.9%), middle risk (5-year OS, 20.6%), and high risk (5-year OS, 4.9%). The discrimination of the three strata was improved significantly in the post-HAICN, which included the above-mentioned factors and number of sessions, combination with immune checkpoint inhibitors, tyrosine kinase inhibitors, and local therapy (AUC, 0.802 versus 0.811, p < 0.001). CONCLUSIONS: The nomogram models are essential to identify patients with large HCC suitable for treatment with HAIC combination therapy and may potentially benefit personalized decision-making. LAY SUMMARY: Hepatic arterial infusion chemotherapy (HAIC) provides sustained higher concentrations of chemotherapy agents in large hepatocellular carcinoma (HCC) by hepatic intra-arterial, result in better objective response outperformed the intravenous administration. HAIC is significantly correlated with favorable survival outcome and obtains extensive support in the effective and safe treatment of intermediate advanced-stage HCC. In view of the high heterogeneity of HCC, there is no consensus regarding the optimal tool for risk stratification before HAIC alone or HAIC combined with tyrosine kinase inhibitors or immune checkpoint inhibitors treatment in HCC. In this large collaboration, we established two nomogram models to estimate the prognosis and evaluate the survival benefits with different HAIC combination therapy. It could help physicians in decision-making before HAIC and comprehensive treatment for large HCC patients in clinical practice and future trials. SAGE Publications 2023-04-17 /pmc/articles/PMC10126656/ /pubmed/37113732 http://dx.doi.org/10.1177/17588359231163845 Text en © The Author(s), 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Artificial intelligence and radiomics in oncology: building the evidence base and implementing into practice Yao, Wang Wei, Ran Jia, Jia Li, Wang Zuo, Mengxuan Zhuo, Shuqing Shi, Ge Wu, Peihong An, Chao Development and validation of prognostic nomograms for large hepatocellular carcinoma after HAIC |
title | Development and validation of prognostic nomograms for large
hepatocellular carcinoma after HAIC |
title_full | Development and validation of prognostic nomograms for large
hepatocellular carcinoma after HAIC |
title_fullStr | Development and validation of prognostic nomograms for large
hepatocellular carcinoma after HAIC |
title_full_unstemmed | Development and validation of prognostic nomograms for large
hepatocellular carcinoma after HAIC |
title_short | Development and validation of prognostic nomograms for large
hepatocellular carcinoma after HAIC |
title_sort | development and validation of prognostic nomograms for large
hepatocellular carcinoma after haic |
topic | Artificial intelligence and radiomics in oncology: building the evidence base and implementing into practice |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126656/ https://www.ncbi.nlm.nih.gov/pubmed/37113732 http://dx.doi.org/10.1177/17588359231163845 |
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