Cargando…
Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study
OBJECTIVE: To compare and evaluate radiomics models to preoperatively predict β-catenin mutation in patients with hepatocellular carcinoma (HCC). METHODS: Ninety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interes...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523364/ https://www.ncbi.nlm.nih.gov/pubmed/36185240 http://dx.doi.org/10.3389/fonc.2022.916126 |
_version_ | 1784800272424370176 |
---|---|
author | Zeng, Fengxia Dai, Hui Li, Xu Guo, Le Jia, Ningyang Yang, Jun Huang, Danping Zeng, Hui Chen, Weiguo Zhang, Ling Qin, Genggeng |
author_facet | Zeng, Fengxia Dai, Hui Li, Xu Guo, Le Jia, Ningyang Yang, Jun Huang, Danping Zeng, Hui Chen, Weiguo Zhang, Ling Qin, Genggeng |
author_sort | Zeng, Fengxia |
collection | PubMed |
description | OBJECTIVE: To compare and evaluate radiomics models to preoperatively predict β-catenin mutation in patients with hepatocellular carcinoma (HCC). METHODS: Ninety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interest were manually delineated on arterial phase, portal venous phase, delay phase, and hepatobiliary phase (HBP) images. Radiomics features extracted from different combinations of imaging phases were analyzed and validated. A linear support vector classifier was applied to develop different models. RESULTS: Among all 15 types of radiomics models, the model with the best performance was seen in the R(HBP) radiomics model. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity of the R(HBP) radiomics model in the training and validation cohorts were 0.86 (95% confidence interval [CI], 0.75–0.93), 0.75, 1.0, and 0.65 and 0.82 (95% CI, 0.63–0.93), 0.73, 0.67, and 0.76, respectively. The combined model integrated radiomics features in the R(HBP) radiomics model, and signatures in the clinical model did not improve further compared to the single HBP radiomics model with AUCs of 0.86 and 0.76. Good calibration for the best R(HBP) radiomics model was displayed in both cohorts; the decision curve showed that the net benefit could achieve 0.15. The most important radiomics features were low and high gray-level zone emphases based on gray-level size zone matrix with the same Shapley additive explanation values of 0.424. CONCLUSION: The R(HBP) radiomics model may be used as an effective model indicative of HCCs with β-catenin mutation preoperatively and thus could guide personalized medicine. |
format | Online Article Text |
id | pubmed-9523364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95233642022-10-01 Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study Zeng, Fengxia Dai, Hui Li, Xu Guo, Le Jia, Ningyang Yang, Jun Huang, Danping Zeng, Hui Chen, Weiguo Zhang, Ling Qin, Genggeng Front Oncol Oncology OBJECTIVE: To compare and evaluate radiomics models to preoperatively predict β-catenin mutation in patients with hepatocellular carcinoma (HCC). METHODS: Ninety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interest were manually delineated on arterial phase, portal venous phase, delay phase, and hepatobiliary phase (HBP) images. Radiomics features extracted from different combinations of imaging phases were analyzed and validated. A linear support vector classifier was applied to develop different models. RESULTS: Among all 15 types of radiomics models, the model with the best performance was seen in the R(HBP) radiomics model. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity of the R(HBP) radiomics model in the training and validation cohorts were 0.86 (95% confidence interval [CI], 0.75–0.93), 0.75, 1.0, and 0.65 and 0.82 (95% CI, 0.63–0.93), 0.73, 0.67, and 0.76, respectively. The combined model integrated radiomics features in the R(HBP) radiomics model, and signatures in the clinical model did not improve further compared to the single HBP radiomics model with AUCs of 0.86 and 0.76. Good calibration for the best R(HBP) radiomics model was displayed in both cohorts; the decision curve showed that the net benefit could achieve 0.15. The most important radiomics features were low and high gray-level zone emphases based on gray-level size zone matrix with the same Shapley additive explanation values of 0.424. CONCLUSION: The R(HBP) radiomics model may be used as an effective model indicative of HCCs with β-catenin mutation preoperatively and thus could guide personalized medicine. Frontiers Media S.A. 2022-09-16 /pmc/articles/PMC9523364/ /pubmed/36185240 http://dx.doi.org/10.3389/fonc.2022.916126 Text en Copyright © 2022 Zeng, Dai, Li, Guo, Jia, Yang, Huang, Zeng, Chen, Zhang and Qin 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 Zeng, Fengxia Dai, Hui Li, Xu Guo, Le Jia, Ningyang Yang, Jun Huang, Danping Zeng, Hui Chen, Weiguo Zhang, Ling Qin, Genggeng Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study |
title | Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study |
title_full | Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study |
title_fullStr | Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study |
title_full_unstemmed | Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study |
title_short | Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study |
title_sort | preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: a retrospective study |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523364/ https://www.ncbi.nlm.nih.gov/pubmed/36185240 http://dx.doi.org/10.3389/fonc.2022.916126 |
work_keys_str_mv | AT zengfengxia preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT daihui preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT lixu preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT guole preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT jianingyang preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT yangjun preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT huangdanping preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT zenghui preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT chenweiguo preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT zhangling preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy AT qingenggeng preoperativeradiomicsmodelusinggadobenatedimeglumineenhancedmagneticresonanceimagingforpredictingbcateninmutationinpatientswithhepatocellularcarcinomaaretrospectivestudy |