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Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma

OBJECTIVES: The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators. METHODS: As a retrospective study, the s...

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Autores principales: Liu, Peijun, Li, Weiqiu, Qiu, Ganbin, Chen, Jincan, Liu, Yonghui, Wen, Zhongyan, Liang, Mei, Zhao, Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666788/
https://www.ncbi.nlm.nih.gov/pubmed/38023195
http://dx.doi.org/10.3389/fonc.2023.1142916
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author Liu, Peijun
Li, Weiqiu
Qiu, Ganbin
Chen, Jincan
Liu, Yonghui
Wen, Zhongyan
Liang, Mei
Zhao, Yue
author_facet Liu, Peijun
Li, Weiqiu
Qiu, Ganbin
Chen, Jincan
Liu, Yonghui
Wen, Zhongyan
Liang, Mei
Zhao, Yue
author_sort Liu, Peijun
collection PubMed
description OBJECTIVES: The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators. METHODS: As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility. RESULTS: Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75(th) percentile ADC value < 1.48 ×10(3) mm(2)/s and R2* value ≥ 38.6 sec(-1) were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness. CONCLUSIONS: The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients.
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spelling pubmed-106667882023-01-01 Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma Liu, Peijun Li, Weiqiu Qiu, Ganbin Chen, Jincan Liu, Yonghui Wen, Zhongyan Liang, Mei Zhao, Yue Front Oncol Oncology OBJECTIVES: The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators. METHODS: As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility. RESULTS: Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75(th) percentile ADC value < 1.48 ×10(3) mm(2)/s and R2* value ≥ 38.6 sec(-1) were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness. CONCLUSIONS: The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients. Frontiers Media S.A. 2023-11-09 /pmc/articles/PMC10666788/ /pubmed/38023195 http://dx.doi.org/10.3389/fonc.2023.1142916 Text en Copyright © 2023 Liu, Li, Qiu, Chen, Liu, Wen, Liang and Zhao 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
Liu, Peijun
Li, Weiqiu
Qiu, Ganbin
Chen, Jincan
Liu, Yonghui
Wen, Zhongyan
Liang, Mei
Zhao, Yue
Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma
title Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma
title_full Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma
title_fullStr Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma
title_full_unstemmed Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma
title_short Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma
title_sort multiparametric mri combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666788/
https://www.ncbi.nlm.nih.gov/pubmed/38023195
http://dx.doi.org/10.3389/fonc.2023.1142916
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