Cargando…

MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018

Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Haiping, Guo, Dajing, Liu, Huan, He, Xiaojing, Qiao, Xiaofeng, Liu, Xinjie, Liu, Yangyang, Zhou, Jun, Zhou, Zhiming, Liu, Xi, Fang, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139717/
https://www.ncbi.nlm.nih.gov/pubmed/35626199
http://dx.doi.org/10.3390/diagnostics12051043
_version_ 1784714923560927232
author Zhang, Haiping
Guo, Dajing
Liu, Huan
He, Xiaojing
Qiao, Xiaofeng
Liu, Xinjie
Liu, Yangyang
Zhou, Jun
Zhou, Zhiming
Liu, Xi
Fang, Zheng
author_facet Zhang, Haiping
Guo, Dajing
Liu, Huan
He, Xiaojing
Qiao, Xiaofeng
Liu, Xinjie
Liu, Yangyang
Zhou, Jun
Zhou, Zhiming
Liu, Xi
Fang, Zheng
author_sort Zhang, Haiping
collection PubMed
description Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification.
format Online
Article
Text
id pubmed-9139717
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91397172022-05-28 MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018 Zhang, Haiping Guo, Dajing Liu, Huan He, Xiaojing Qiao, Xiaofeng Liu, Xinjie Liu, Yangyang Zhou, Jun Zhou, Zhiming Liu, Xi Fang, Zheng Diagnostics (Basel) Article Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification. MDPI 2022-04-21 /pmc/articles/PMC9139717/ /pubmed/35626199 http://dx.doi.org/10.3390/diagnostics12051043 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Haiping
Guo, Dajing
Liu, Huan
He, Xiaojing
Qiao, Xiaofeng
Liu, Xinjie
Liu, Yangyang
Zhou, Jun
Zhou, Zhiming
Liu, Xi
Fang, Zheng
MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018
title MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018
title_full MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018
title_fullStr MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018
title_full_unstemmed MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018
title_short MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018
title_sort mri-based radiomics models to discriminate hepatocellular carcinoma and non-hepatocellular carcinoma in lr-m according to li-rads version 2018
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139717/
https://www.ncbi.nlm.nih.gov/pubmed/35626199
http://dx.doi.org/10.3390/diagnostics12051043
work_keys_str_mv AT zhanghaiping mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT guodajing mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT liuhuan mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT hexiaojing mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT qiaoxiaofeng mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT liuxinjie mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT liuyangyang mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT zhoujun mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT zhouzhiming mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT liuxi mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018
AT fangzheng mribasedradiomicsmodelstodiscriminatehepatocellularcarcinomaandnonhepatocellularcarcinomainlrmaccordingtoliradsversion2018