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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10666788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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