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A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature

OBJECTIVES: The objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation. METHODS: 202 patients with proven single HCC w...

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Autores principales: Fan, Tingting, Li, Shijie, Li, Kai, Xu, Jingxu, Zhao, Sheng, Li, Jinping, Zhou, Xinglu, Jiang, Huijie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013965/
https://www.ncbi.nlm.nih.gov/pubmed/35444942
http://dx.doi.org/10.3389/fonc.2022.857715
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author Fan, Tingting
Li, Shijie
Li, Kai
Xu, Jingxu
Zhao, Sheng
Li, Jinping
Zhou, Xinglu
Jiang, Huijie
author_facet Fan, Tingting
Li, Shijie
Li, Kai
Xu, Jingxu
Zhao, Sheng
Li, Jinping
Zhou, Xinglu
Jiang, Huijie
author_sort Fan, Tingting
collection PubMed
description OBJECTIVES: The objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation. METHODS: 202 patients with proven single HCC were enlisted and stochastically distributed into a training set (n = 142) and a test set (n = 60). Arterial phase, portal venous phase, balanced phase, delayed phase, and hepatobiliary phase images were used to radiomics features extraction. We retrieved 1906 radiomic features from each phase of every participant’s MRI images. The F-test was applied to choose the crucial features. A logistic regression model was adopted to generate a radiomics signature. By combining independent risk indicators from the fusion radiomics signature and clinico-radiological features, we developed a multivariable logistic regression model that could predict the VEGF status preoperatively through calculating the area under the curve (AUC). RESULTS: The entire group comprised 108 VEGF-positive individuals and 94 VEGF-negative patients. AUCs of 0.892 (95% confidence interval [CI]: 0.839 - 0.945) in the training dataset and 0.800 (95% CI: 0.682 - 0.918) in the test dataset were achieved by utilizing radiomics features from two phase images (8 features from the portal venous phase and 5 features from the hepatobiliary phase). Furthermore, the nomogram relying on a combined model that included the clinical factors α-fetoprotein (AFP), irregular tumor margin, and the fusion radiomics signature performed well in both the training (AUC = 0.936, 95% CI: 0.898-0.974) and test (AUC = 0.836, 95% CI: 0.728-0.944) datasets. CONCLUSIONS: The combined model acquired from two phase (portal venous and hepatobiliary phase) pictures of gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI could be considered as a credible prognostic marker for the level of VEGF in HCC.
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spelling pubmed-90139652022-04-19 A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature Fan, Tingting Li, Shijie Li, Kai Xu, Jingxu Zhao, Sheng Li, Jinping Zhou, Xinglu Jiang, Huijie Front Oncol Oncology OBJECTIVES: The objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation. METHODS: 202 patients with proven single HCC were enlisted and stochastically distributed into a training set (n = 142) and a test set (n = 60). Arterial phase, portal venous phase, balanced phase, delayed phase, and hepatobiliary phase images were used to radiomics features extraction. We retrieved 1906 radiomic features from each phase of every participant’s MRI images. The F-test was applied to choose the crucial features. A logistic regression model was adopted to generate a radiomics signature. By combining independent risk indicators from the fusion radiomics signature and clinico-radiological features, we developed a multivariable logistic regression model that could predict the VEGF status preoperatively through calculating the area under the curve (AUC). RESULTS: The entire group comprised 108 VEGF-positive individuals and 94 VEGF-negative patients. AUCs of 0.892 (95% confidence interval [CI]: 0.839 - 0.945) in the training dataset and 0.800 (95% CI: 0.682 - 0.918) in the test dataset were achieved by utilizing radiomics features from two phase images (8 features from the portal venous phase and 5 features from the hepatobiliary phase). Furthermore, the nomogram relying on a combined model that included the clinical factors α-fetoprotein (AFP), irregular tumor margin, and the fusion radiomics signature performed well in both the training (AUC = 0.936, 95% CI: 0.898-0.974) and test (AUC = 0.836, 95% CI: 0.728-0.944) datasets. CONCLUSIONS: The combined model acquired from two phase (portal venous and hepatobiliary phase) pictures of gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI could be considered as a credible prognostic marker for the level of VEGF in HCC. Frontiers Media S.A. 2022-04-04 /pmc/articles/PMC9013965/ /pubmed/35444942 http://dx.doi.org/10.3389/fonc.2022.857715 Text en Copyright © 2022 Fan, Li, Li, Xu, Zhao, Li, Zhou and Jiang 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
Fan, Tingting
Li, Shijie
Li, Kai
Xu, Jingxu
Zhao, Sheng
Li, Jinping
Zhou, Xinglu
Jiang, Huijie
A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature
title A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature
title_full A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature
title_fullStr A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature
title_full_unstemmed A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature
title_short A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature
title_sort potential prognostic marker for recognizing vegf-positive hepatocellular carcinoma based on magnetic resonance radiomics signature
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013965/
https://www.ncbi.nlm.nih.gov/pubmed/35444942
http://dx.doi.org/10.3389/fonc.2022.857715
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