Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound

PURPOSE: This study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: A retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Di, Wei, Qi, Wu, Ge-Ge, Zhang, Xian-Ya, Lu, Wen-Wu, Lv, Wen-Zhi, Liao, Jin-Tang, Cui, Xin-Wu, Ni, Xue-Jun, Dietrich, Christoph F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453164/
https://www.ncbi.nlm.nih.gov/pubmed/34557410
http://dx.doi.org/10.3389/fonc.2021.709339
_version_ 1784570224798859264
author Zhang, Di
Wei, Qi
Wu, Ge-Ge
Zhang, Xian-Ya
Lu, Wen-Wu
Lv, Wen-Zhi
Liao, Jin-Tang
Cui, Xin-Wu
Ni, Xue-Jun
Dietrich, Christoph F.
author_facet Zhang, Di
Wei, Qi
Wu, Ge-Ge
Zhang, Xian-Ya
Lu, Wen-Wu
Lv, Wen-Zhi
Liao, Jin-Tang
Cui, Xin-Wu
Ni, Xue-Jun
Dietrich, Christoph F.
author_sort Zhang, Di
collection PubMed
description PURPOSE: This study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: A retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness. RESULTS: Multivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC. CONCLUSION: The CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning.
format Online
Article
Text
id pubmed-8453164
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-84531642021-09-22 Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound Zhang, Di Wei, Qi Wu, Ge-Ge Zhang, Xian-Ya Lu, Wen-Wu Lv, Wen-Zhi Liao, Jin-Tang Cui, Xin-Wu Ni, Xue-Jun Dietrich, Christoph F. Front Oncol Oncology PURPOSE: This study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: A retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness. RESULTS: Multivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC. CONCLUSION: The CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning. Frontiers Media S.A. 2021-09-07 /pmc/articles/PMC8453164/ /pubmed/34557410 http://dx.doi.org/10.3389/fonc.2021.709339 Text en Copyright © 2021 Zhang, Wei, Wu, Zhang, Lu, Lv, Liao, Cui, Ni and Dietrich 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
Zhang, Di
Wei, Qi
Wu, Ge-Ge
Zhang, Xian-Ya
Lu, Wen-Wu
Lv, Wen-Zhi
Liao, Jin-Tang
Cui, Xin-Wu
Ni, Xue-Jun
Dietrich, Christoph F.
Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
title Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
title_full Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
title_fullStr Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
title_full_unstemmed Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
title_short Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound
title_sort preoperative prediction of microvascular invasion in patients with hepatocellular carcinoma based on radiomics nomogram using contrast-enhanced ultrasound
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453164/
https://www.ncbi.nlm.nih.gov/pubmed/34557410
http://dx.doi.org/10.3389/fonc.2021.709339
work_keys_str_mv AT zhangdi preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT weiqi preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT wugege preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT zhangxianya preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT luwenwu preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT lvwenzhi preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT liaojintang preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT cuixinwu preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT nixuejun preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound
AT dietrichchristophf preoperativepredictionofmicrovascularinvasioninpatientswithhepatocellularcarcinomabasedonradiomicsnomogramusingcontrastenhancedultrasound