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Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer
BACKGROUND: To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. METHODS: This retrospective study included preoperative images of 194 rectal cancer...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249234/ https://www.ncbi.nlm.nih.gov/pubmed/37291527 http://dx.doi.org/10.1186/s12880-023-01027-0 |
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author | Wang, Ke-xin Yu, Jing Xu, Qing |
author_facet | Wang, Ke-xin Yu, Jing Xu, Qing |
author_sort | Wang, Ke-xin |
collection | PubMed |
description | BACKGROUND: To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. METHODS: This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (K(trans), K(ep) and V(e)) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive). RESULTS: The mean values of K(trans) and V(e) in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in K(trans) skewness, K(trans) entropy, K(trans) kurtosis, and V(e) maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The K(trans) kurtosis and K(trans) entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores. CONCLUSIONS: Histogram Analysis of DCE-MRI K(trans) maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01027-0. |
format | Online Article Text |
id | pubmed-10249234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102492342023-06-09 Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer Wang, Ke-xin Yu, Jing Xu, Qing BMC Med Imaging Research BACKGROUND: To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. METHODS: This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (K(trans), K(ep) and V(e)) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive). RESULTS: The mean values of K(trans) and V(e) in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in K(trans) skewness, K(trans) entropy, K(trans) kurtosis, and V(e) maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The K(trans) kurtosis and K(trans) entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores. CONCLUSIONS: Histogram Analysis of DCE-MRI K(trans) maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01027-0. BioMed Central 2023-06-08 /pmc/articles/PMC10249234/ /pubmed/37291527 http://dx.doi.org/10.1186/s12880-023-01027-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Wang, Ke-xin Yu, Jing Xu, Qing Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer |
title | Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer |
title_full | Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer |
title_fullStr | Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer |
title_full_unstemmed | Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer |
title_short | Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer |
title_sort | histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249234/ https://www.ncbi.nlm.nih.gov/pubmed/37291527 http://dx.doi.org/10.1186/s12880-023-01027-0 |
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