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Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients

BACKGROUND: GALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Ch...

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Autores principales: Chen, Ping, Song, Haolin, Xu, Wei, Guo, Jin, Wang, Jianfei, Zhou, Juhong, Kang, Xiang, Jin, Chaolei, Cai, Yubo, Feng, Zixuan, Gao, Hainv, Lu, Fengmin, Li, Lanjuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817025/
https://www.ncbi.nlm.nih.gov/pubmed/36620588
http://dx.doi.org/10.3389/fonc.2022.1037742
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author Chen, Ping
Song, Haolin
Xu, Wei
Guo, Jin
Wang, Jianfei
Zhou, Juhong
Kang, Xiang
Jin, Chaolei
Cai, Yubo
Feng, Zixuan
Gao, Hainv
Lu, Fengmin
Li, Lanjuan
author_facet Chen, Ping
Song, Haolin
Xu, Wei
Guo, Jin
Wang, Jianfei
Zhou, Juhong
Kang, Xiang
Jin, Chaolei
Cai, Yubo
Feng, Zixuan
Gao, Hainv
Lu, Fengmin
Li, Lanjuan
author_sort Chen, Ping
collection PubMed
description BACKGROUND: GALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations. METHODS: There are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model. RESULT: The GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888). CONCLUSIONS: The new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed.
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spelling pubmed-98170252023-01-07 Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients Chen, Ping Song, Haolin Xu, Wei Guo, Jin Wang, Jianfei Zhou, Juhong Kang, Xiang Jin, Chaolei Cai, Yubo Feng, Zixuan Gao, Hainv Lu, Fengmin Li, Lanjuan Front Oncol Oncology BACKGROUND: GALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations. METHODS: There are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model. RESULT: The GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888). CONCLUSIONS: The new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed. Frontiers Media S.A. 2022-12-23 /pmc/articles/PMC9817025/ /pubmed/36620588 http://dx.doi.org/10.3389/fonc.2022.1037742 Text en Copyright © 2022 Chen, Song, Xu, Guo, Wang, Zhou, Kang, Jin, Cai, Feng, Gao, Lu and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Chen, Ping
Song, Haolin
Xu, Wei
Guo, Jin
Wang, Jianfei
Zhou, Juhong
Kang, Xiang
Jin, Chaolei
Cai, Yubo
Feng, Zixuan
Gao, Hainv
Lu, Fengmin
Li, Lanjuan
Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients
title Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients
title_full Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients
title_fullStr Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients
title_full_unstemmed Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients
title_short Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients
title_sort validation of the galad model and establishment of a new model for hcc detection in chinese patients
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817025/
https://www.ncbi.nlm.nih.gov/pubmed/36620588
http://dx.doi.org/10.3389/fonc.2022.1037742
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