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Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI

Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by (18)F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI. Materials and methods: Ninety-seven patients with HCC who received the examinations of...

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Autores principales: Shi, Huazheng, Duan, Ying, Shi, Jie, Zhang, Wenrui, Liu, Weiran, Shen, Bixia, Liu, Fufu, Mei, Xin, Li, Xiaoxiao, Yuan, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412047/
https://www.ncbi.nlm.nih.gov/pubmed/36035488
http://dx.doi.org/10.3389/fphys.2022.928969
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author Shi, Huazheng
Duan, Ying
Shi, Jie
Zhang, Wenrui
Liu, Weiran
Shen, Bixia
Liu, Fufu
Mei, Xin
Li, Xiaoxiao
Yuan, Zheng
author_facet Shi, Huazheng
Duan, Ying
Shi, Jie
Zhang, Wenrui
Liu, Weiran
Shen, Bixia
Liu, Fufu
Mei, Xin
Li, Xiaoxiao
Yuan, Zheng
author_sort Shi, Huazheng
collection PubMed
description Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by (18)F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI. Materials and methods: Ninety-seven patients with HCC who received the examinations of MRI and (18)F-FDG PET/CT were retrospectively included in this study and were randomized into training and testing cohorts. The lesion image texture features of (18)F-FDG PET were extracted using MaZda software. The optimal predictive texture features of mVI were selected, and the classification procedure was conducted. The predictive performance of mVI by radiomics classier in training and testing cohorts was respectively recorded. Next, the hybrid model was developed by integrating the (18)F-FDG PET image texture, metabolic parameters, and MRI parameters to predict mVI through logistic regression. Furthermore, the diagnostic performance of each time was recorded. Results: The (18)F-FDG PET image radiomics classier showed good predicted performance in both training and testing cohorts to discriminate HCC with/without mVI, with an AUC of 0.917 (95% CI: 0.824–0.970) and 0.771 (95% CI: 0.578, 0.905). The hybrid model, which combines radiomics classier, SUVmax, ADC, hypovascular arterial phase enhancement pattern on contrast-enhanced MRI, and non-smooth tumor margin, also yielded better predictive performance with an AUC of 0.996 (95% CI: 0.939, 1.000) and 0.953 (95% CI: 0.883, 1.000). The differences in AUCs between radiomics classier and hybrid classier were significant in both training and testing cohorts (DeLong test, both p < 0.05). Conclusion: The radiomics classier based on (18)F-FDG PET image texture and the hybrid classier incorporating (18)F-FDG PET/CT and MRI yielded good predictive performance, which might provide a precise prediction of HCC mVI preoperatively.
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spelling pubmed-94120472022-08-27 Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI Shi, Huazheng Duan, Ying Shi, Jie Zhang, Wenrui Liu, Weiran Shen, Bixia Liu, Fufu Mei, Xin Li, Xiaoxiao Yuan, Zheng Front Physiol Physiology Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by (18)F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI. Materials and methods: Ninety-seven patients with HCC who received the examinations of MRI and (18)F-FDG PET/CT were retrospectively included in this study and were randomized into training and testing cohorts. The lesion image texture features of (18)F-FDG PET were extracted using MaZda software. The optimal predictive texture features of mVI were selected, and the classification procedure was conducted. The predictive performance of mVI by radiomics classier in training and testing cohorts was respectively recorded. Next, the hybrid model was developed by integrating the (18)F-FDG PET image texture, metabolic parameters, and MRI parameters to predict mVI through logistic regression. Furthermore, the diagnostic performance of each time was recorded. Results: The (18)F-FDG PET image radiomics classier showed good predicted performance in both training and testing cohorts to discriminate HCC with/without mVI, with an AUC of 0.917 (95% CI: 0.824–0.970) and 0.771 (95% CI: 0.578, 0.905). The hybrid model, which combines radiomics classier, SUVmax, ADC, hypovascular arterial phase enhancement pattern on contrast-enhanced MRI, and non-smooth tumor margin, also yielded better predictive performance with an AUC of 0.996 (95% CI: 0.939, 1.000) and 0.953 (95% CI: 0.883, 1.000). The differences in AUCs between radiomics classier and hybrid classier were significant in both training and testing cohorts (DeLong test, both p < 0.05). Conclusion: The radiomics classier based on (18)F-FDG PET image texture and the hybrid classier incorporating (18)F-FDG PET/CT and MRI yielded good predictive performance, which might provide a precise prediction of HCC mVI preoperatively. Frontiers Media S.A. 2022-08-12 /pmc/articles/PMC9412047/ /pubmed/36035488 http://dx.doi.org/10.3389/fphys.2022.928969 Text en Copyright © 2022 Shi, Duan, Shi, Zhang, Liu, Shen, Liu, Mei, Li and Yuan. 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 Physiology
Shi, Huazheng
Duan, Ying
Shi, Jie
Zhang, Wenrui
Liu, Weiran
Shen, Bixia
Liu, Fufu
Mei, Xin
Li, Xiaoxiao
Yuan, Zheng
Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI
title Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI
title_full Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI
title_fullStr Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI
title_full_unstemmed Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI
title_short Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI
title_sort role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of fdg pet image: a comparison of quantitative metabolic parameters and mri
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412047/
https://www.ncbi.nlm.nih.gov/pubmed/36035488
http://dx.doi.org/10.3389/fphys.2022.928969
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