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Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction

BACKGROUND: This study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images. METH...

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Autores principales: Zhang, Zhen, Jiang, Hanyu, Chen, Jie, Wei, Yi, Cao, Likun, Ye, Zheng, Li, Xin, Ma, Ling, Song, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518803/
https://www.ncbi.nlm.nih.gov/pubmed/31088553
http://dx.doi.org/10.1186/s40644-019-0209-5
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author Zhang, Zhen
Jiang, Hanyu
Chen, Jie
Wei, Yi
Cao, Likun
Ye, Zheng
Li, Xin
Ma, Ling
Song, Bin
author_facet Zhang, Zhen
Jiang, Hanyu
Chen, Jie
Wei, Yi
Cao, Likun
Ye, Zheng
Li, Xin
Ma, Ling
Song, Bin
author_sort Zhang, Zhen
collection PubMed
description BACKGROUND: This study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images. METHODS: In total, 155 patients (training cohort: n = 108; validation cohort: n = 47) with surgically confirmed HCC were enrolled in this IRB-approved prospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumour margins on multi-sequence MR images. Radiomics features were generated and selected to build a radiomics score using the least absolute shrinkage and selection operator (LASSO) method. Clinical characteristics and qualitative imaging features were identified by two independent radiologists and combined to establish a clinical-radiological nomogram. A radiomics nomogram comprising the radiomics score and clinical-radiological risk factors was constructed based on multivariable logistic regression analysis. Diagnostic performance and clinical usefulness were measured by receiver operation characteristic (ROC) and decision curves. RESULTS: In total, 14 radiomics features were selected to construct the radiomics score. For the clinical-radiological nomogram, the alpha-fetoprotein (AFP) level, gross vascular invasion and non-smooth tumour margin were included. The radiomics nomogram integrating the radiomics score with clinical-radiological risk factors showed better discriminative performance (AUC = 0.844, 95%CI, 0.769 to 0.919) than the clinical-radiological nomogram (AUC = 0.796, 95%CI, 0.712 to 0.881; P = 0.045), with increased clinical usefulness confirmed using a decision curve analysis. CONCLUSIONS: Incorporating multiple predictive factors, the radiomics nomogram demonstrated great potential in the preoperative prediction of early HCC recurrence after surgery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40644-019-0209-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-65188032019-05-21 Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction Zhang, Zhen Jiang, Hanyu Chen, Jie Wei, Yi Cao, Likun Ye, Zheng Li, Xin Ma, Ling Song, Bin Cancer Imaging Research Article BACKGROUND: This study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images. METHODS: In total, 155 patients (training cohort: n = 108; validation cohort: n = 47) with surgically confirmed HCC were enrolled in this IRB-approved prospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumour margins on multi-sequence MR images. Radiomics features were generated and selected to build a radiomics score using the least absolute shrinkage and selection operator (LASSO) method. Clinical characteristics and qualitative imaging features were identified by two independent radiologists and combined to establish a clinical-radiological nomogram. A radiomics nomogram comprising the radiomics score and clinical-radiological risk factors was constructed based on multivariable logistic regression analysis. Diagnostic performance and clinical usefulness were measured by receiver operation characteristic (ROC) and decision curves. RESULTS: In total, 14 radiomics features were selected to construct the radiomics score. For the clinical-radiological nomogram, the alpha-fetoprotein (AFP) level, gross vascular invasion and non-smooth tumour margin were included. The radiomics nomogram integrating the radiomics score with clinical-radiological risk factors showed better discriminative performance (AUC = 0.844, 95%CI, 0.769 to 0.919) than the clinical-radiological nomogram (AUC = 0.796, 95%CI, 0.712 to 0.881; P = 0.045), with increased clinical usefulness confirmed using a decision curve analysis. CONCLUSIONS: Incorporating multiple predictive factors, the radiomics nomogram demonstrated great potential in the preoperative prediction of early HCC recurrence after surgery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40644-019-0209-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-14 /pmc/articles/PMC6518803/ /pubmed/31088553 http://dx.doi.org/10.1186/s40644-019-0209-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Zhang, Zhen
Jiang, Hanyu
Chen, Jie
Wei, Yi
Cao, Likun
Ye, Zheng
Li, Xin
Ma, Ling
Song, Bin
Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction
title Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction
title_full Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction
title_fullStr Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction
title_full_unstemmed Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction
title_short Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction
title_sort hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced mr imaging for early postoperative recurrence prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518803/
https://www.ncbi.nlm.nih.gov/pubmed/31088553
http://dx.doi.org/10.1186/s40644-019-0209-5
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