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Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging
Background: To compare the diagnostic performance of radiomics models with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters for the preoperative prediction of extramural venous invasion (EMVI) in rectal cancer patients and to develop a preoperative nomogram...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160694/ https://www.ncbi.nlm.nih.gov/pubmed/32328461 http://dx.doi.org/10.3389/fonc.2020.00459 |
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author | Yu, Xiangling Song, Wenlong Guo, Dajing Liu, Huan Zhang, Haiping He, Xiaojing Song, Junjie Zhou, Jun Liu, Xinjie |
author_facet | Yu, Xiangling Song, Wenlong Guo, Dajing Liu, Huan Zhang, Haiping He, Xiaojing Song, Junjie Zhou, Jun Liu, Xinjie |
author_sort | Yu, Xiangling |
collection | PubMed |
description | Background: To compare the diagnostic performance of radiomics models with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters for the preoperative prediction of extramural venous invasion (EMVI) in rectal cancer patients and to develop a preoperative nomogram for predicting the EMVI status. Methods: In total, 106 rectal cancer patients were enrolled in our study. All patients under went preoperative rectal high-resolution MRI and DCE-MRI. We built five models based on the perfusion parameters of DCE-MRI (quantitative model), the radiomics of T(2)-weighted (T(2)W) CUBE imaging (R(1) model), DCE-MRI (R(2) model), clinical features (clinical model), and clinical-radiomics features. The predictive efficacy of the radiomics signature was assessed and internally verified. The area under the receiver operating curve (AUC) was used to compare the diagnostic performance of different radiomics models and DCE-MRI quantitative parameters. The radiomics score and clinical-pathologic risk factors were incorporated into an easy-to-use nomogram. Results: The quantitative parameters K(trans) and Ve were significantly higher in the EMVI-positive group than in the EMVI-negative group (both P =0.02). K(trans) combined with Ve showed a fair degree of accuracy (AUC 0.680 in the training cohort and AUC 0.715 in the validation cohort) compared with K(trans) or Ve alone. The AUCs of the R(1) and R(2) models were 0.826, 0.715 and 0.872, 0.812 in the training and validation cohorts, respectively. In addition, the R(2)-C model yielded an AUC of 0.904 in the training cohort and 0.812 in the validation cohort. The nomogram was presented based on the clinical-radiomics model. The calibration curves showed good agreement. Conclusion: The radiomics nomogram that incorporates the radiomics score, histopathological grade and T stage demonstrated better diagnostic accuracy than the DCE-MRI quantitative parameters and may have significant clinical implications for the preoperative individualized prediction of EMVI in rectal cancer patients. |
format | Online Article Text |
id | pubmed-7160694 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71606942020-04-23 Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging Yu, Xiangling Song, Wenlong Guo, Dajing Liu, Huan Zhang, Haiping He, Xiaojing Song, Junjie Zhou, Jun Liu, Xinjie Front Oncol Oncology Background: To compare the diagnostic performance of radiomics models with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters for the preoperative prediction of extramural venous invasion (EMVI) in rectal cancer patients and to develop a preoperative nomogram for predicting the EMVI status. Methods: In total, 106 rectal cancer patients were enrolled in our study. All patients under went preoperative rectal high-resolution MRI and DCE-MRI. We built five models based on the perfusion parameters of DCE-MRI (quantitative model), the radiomics of T(2)-weighted (T(2)W) CUBE imaging (R(1) model), DCE-MRI (R(2) model), clinical features (clinical model), and clinical-radiomics features. The predictive efficacy of the radiomics signature was assessed and internally verified. The area under the receiver operating curve (AUC) was used to compare the diagnostic performance of different radiomics models and DCE-MRI quantitative parameters. The radiomics score and clinical-pathologic risk factors were incorporated into an easy-to-use nomogram. Results: The quantitative parameters K(trans) and Ve were significantly higher in the EMVI-positive group than in the EMVI-negative group (both P =0.02). K(trans) combined with Ve showed a fair degree of accuracy (AUC 0.680 in the training cohort and AUC 0.715 in the validation cohort) compared with K(trans) or Ve alone. The AUCs of the R(1) and R(2) models were 0.826, 0.715 and 0.872, 0.812 in the training and validation cohorts, respectively. In addition, the R(2)-C model yielded an AUC of 0.904 in the training cohort and 0.812 in the validation cohort. The nomogram was presented based on the clinical-radiomics model. The calibration curves showed good agreement. Conclusion: The radiomics nomogram that incorporates the radiomics score, histopathological grade and T stage demonstrated better diagnostic accuracy than the DCE-MRI quantitative parameters and may have significant clinical implications for the preoperative individualized prediction of EMVI in rectal cancer patients. Frontiers Media S.A. 2020-04-09 /pmc/articles/PMC7160694/ /pubmed/32328461 http://dx.doi.org/10.3389/fonc.2020.00459 Text en Copyright © 2020 Yu, Song, Guo, Liu, Zhang, He, Song, Zhou and Liu. http://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 Yu, Xiangling Song, Wenlong Guo, Dajing Liu, Huan Zhang, Haiping He, Xiaojing Song, Junjie Zhou, Jun Liu, Xinjie Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging |
title | Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging |
title_full | Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging |
title_fullStr | Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging |
title_full_unstemmed | Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging |
title_short | Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging |
title_sort | preoperative prediction of extramural venous invasion in rectal cancer: comparison of the diagnostic efficacy of radiomics models and quantitative dynamic contrast-enhanced magnetic resonance imaging |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160694/ https://www.ncbi.nlm.nih.gov/pubmed/32328461 http://dx.doi.org/10.3389/fonc.2020.00459 |
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