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Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study
BACKGROUND: The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). METHODS: We enrol...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403665/ https://www.ncbi.nlm.nih.gov/pubmed/32567245 http://dx.doi.org/10.1002/ctm2.111 |
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author | Zhang, Xiuming Ruan, Shijian Xiao, Wenbo Shao, Jiayuan Tian, Wuwei Liu, Weihai Zhang, Zhao Wan, Dalong Huang, Jiacheng Huang, Qiang Yang, Yunjun Yang, Hanjin Ding, Yong Liang, Wenjie Bai, Xueli Liang, Tingbo |
author_facet | Zhang, Xiuming Ruan, Shijian Xiao, Wenbo Shao, Jiayuan Tian, Wuwei Liu, Weihai Zhang, Zhao Wan, Dalong Huang, Jiacheng Huang, Qiang Yang, Yunjun Yang, Hanjin Ding, Yong Liang, Wenjie Bai, Xueli Liang, Tingbo |
author_sort | Zhang, Xiuming |
collection | PubMed |
description | BACKGROUND: The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). METHODS: We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. RESULTS: The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. CONCLUSIONS: Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool. |
format | Online Article Text |
id | pubmed-7403665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74036652020-08-06 Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study Zhang, Xiuming Ruan, Shijian Xiao, Wenbo Shao, Jiayuan Tian, Wuwei Liu, Weihai Zhang, Zhao Wan, Dalong Huang, Jiacheng Huang, Qiang Yang, Yunjun Yang, Hanjin Ding, Yong Liang, Wenjie Bai, Xueli Liang, Tingbo Clin Transl Med Research Articles BACKGROUND: The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). METHODS: We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. RESULTS: The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. CONCLUSIONS: Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool. John Wiley and Sons Inc. 2020-06-21 /pmc/articles/PMC7403665/ /pubmed/32567245 http://dx.doi.org/10.1002/ctm2.111 Text en © 2020 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zhang, Xiuming Ruan, Shijian Xiao, Wenbo Shao, Jiayuan Tian, Wuwei Liu, Weihai Zhang, Zhao Wan, Dalong Huang, Jiacheng Huang, Qiang Yang, Yunjun Yang, Hanjin Ding, Yong Liang, Wenjie Bai, Xueli Liang, Tingbo Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study |
title | Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study |
title_full | Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study |
title_fullStr | Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study |
title_full_unstemmed | Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study |
title_short | Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study |
title_sort | contrast‐enhanced ct radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: a two‐center study |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403665/ https://www.ncbi.nlm.nih.gov/pubmed/32567245 http://dx.doi.org/10.1002/ctm2.111 |
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