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Predictive value of infiltrating tumor border configuration of rectal cancer on MRI

BACKGROUND: Infiltrating tumor border configuration (iTBC) is assessed by postoperative pathological examination, thus, is not helpful for preoperative treatment strategies. The study aimed to detect iTBC by magnetic resonance imaging (MRI) and evaluate its predictive value. MATERIALS AND METHODS: A...

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Autores principales: Lv, Baohua, Yuan, Leilei, Li, Jizheng, Kong, Xue, Cheng, Yanling, Shang, Kai, Jin, Erhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571450/
https://www.ncbi.nlm.nih.gov/pubmed/37828450
http://dx.doi.org/10.1186/s12880-023-01118-y
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author Lv, Baohua
Yuan, Leilei
Li, Jizheng
Kong, Xue
Cheng, Yanling
Shang, Kai
Jin, Erhu
author_facet Lv, Baohua
Yuan, Leilei
Li, Jizheng
Kong, Xue
Cheng, Yanling
Shang, Kai
Jin, Erhu
author_sort Lv, Baohua
collection PubMed
description BACKGROUND: Infiltrating tumor border configuration (iTBC) is assessed by postoperative pathological examination, thus, is not helpful for preoperative treatment strategies. The study aimed to detect iTBC by magnetic resonance imaging (MRI) and evaluate its predictive value. MATERIALS AND METHODS: A total of 153 patients with rectal cancer were retrospectively analyzed. Clinicopathological and MRI data mainly including tumor border configuration (TBC) on MRI, MRI-detected extramural vascular invasion (MEMVI), tumor length, tumor growth pattern, maximal extramural depth, pathology-proven lymph node metastasis (PLN) and pathology-proven extramural vascular invasion (PEMVI) were analyzed. The correlation of MRI factors with PEMVI and PLN was analyzed by univariate and multivariate logistic regression analyses. The nomograms were established based on multivariate logistic regression analysis and were confirmed by Bootstrap self-sampling. The receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to evaluate the diagnostic efficiency. RESULTS: Fifty cases of PEMVI and 48 cases of PLN were found. Forty cases of PEMVI and 34 cases of PLN in 62 cases of iTBC were also found. iTBC, MEMVI and maximal extramural depth were significantly associated with PEMVI and PLN (P < 0.05). iTBC (odds ratio = 3.84 and 3.02) and MEMVI (odds ratio = 7.27 and 3.22) were independent risk factors for PEMVI and PLN. The C-indices of the two nomograms for predicting PEMVI and PLN were 0.863 and 0.752, respectively. The calibration curves and ROC curves of the two nomograms showed that the correlation between the predicted and the actual incidence of PEMVI and PLN was good. The AUCs of iTBC for predicting PEMVI and PLN were 0.793 (95% CI: 0.714–0.872) and 0.721 (95% CI: 0.632–0.810), respectively. The DeLong test showed that the predictive efficiency of the nomogram in predicting PEMVI was better than that of iTBC (P = 0.0009) and MEMVI (P = 0.0095). CONCLUSION: iTBC and MEMVI are risk factors for PEMVI and pelvic lymph node metastasis. The nomograms based on iTBC show a good performance in predicting PEMVI and pelvic lymph node metastasis, possessing a certain clinical reference value. TRIAL REGISTRATION: This study was approved by the Ethics Committee of Beijing Friendship Hospital, and individual consent was waived for this retrospective analysis.
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spelling pubmed-105714502023-10-14 Predictive value of infiltrating tumor border configuration of rectal cancer on MRI Lv, Baohua Yuan, Leilei Li, Jizheng Kong, Xue Cheng, Yanling Shang, Kai Jin, Erhu BMC Med Imaging Research BACKGROUND: Infiltrating tumor border configuration (iTBC) is assessed by postoperative pathological examination, thus, is not helpful for preoperative treatment strategies. The study aimed to detect iTBC by magnetic resonance imaging (MRI) and evaluate its predictive value. MATERIALS AND METHODS: A total of 153 patients with rectal cancer were retrospectively analyzed. Clinicopathological and MRI data mainly including tumor border configuration (TBC) on MRI, MRI-detected extramural vascular invasion (MEMVI), tumor length, tumor growth pattern, maximal extramural depth, pathology-proven lymph node metastasis (PLN) and pathology-proven extramural vascular invasion (PEMVI) were analyzed. The correlation of MRI factors with PEMVI and PLN was analyzed by univariate and multivariate logistic regression analyses. The nomograms were established based on multivariate logistic regression analysis and were confirmed by Bootstrap self-sampling. The receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to evaluate the diagnostic efficiency. RESULTS: Fifty cases of PEMVI and 48 cases of PLN were found. Forty cases of PEMVI and 34 cases of PLN in 62 cases of iTBC were also found. iTBC, MEMVI and maximal extramural depth were significantly associated with PEMVI and PLN (P < 0.05). iTBC (odds ratio = 3.84 and 3.02) and MEMVI (odds ratio = 7.27 and 3.22) were independent risk factors for PEMVI and PLN. The C-indices of the two nomograms for predicting PEMVI and PLN were 0.863 and 0.752, respectively. The calibration curves and ROC curves of the two nomograms showed that the correlation between the predicted and the actual incidence of PEMVI and PLN was good. The AUCs of iTBC for predicting PEMVI and PLN were 0.793 (95% CI: 0.714–0.872) and 0.721 (95% CI: 0.632–0.810), respectively. The DeLong test showed that the predictive efficiency of the nomogram in predicting PEMVI was better than that of iTBC (P = 0.0009) and MEMVI (P = 0.0095). CONCLUSION: iTBC and MEMVI are risk factors for PEMVI and pelvic lymph node metastasis. The nomograms based on iTBC show a good performance in predicting PEMVI and pelvic lymph node metastasis, possessing a certain clinical reference value. TRIAL REGISTRATION: This study was approved by the Ethics Committee of Beijing Friendship Hospital, and individual consent was waived for this retrospective analysis. BioMed Central 2023-10-12 /pmc/articles/PMC10571450/ /pubmed/37828450 http://dx.doi.org/10.1186/s12880-023-01118-y 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
Lv, Baohua
Yuan, Leilei
Li, Jizheng
Kong, Xue
Cheng, Yanling
Shang, Kai
Jin, Erhu
Predictive value of infiltrating tumor border configuration of rectal cancer on MRI
title Predictive value of infiltrating tumor border configuration of rectal cancer on MRI
title_full Predictive value of infiltrating tumor border configuration of rectal cancer on MRI
title_fullStr Predictive value of infiltrating tumor border configuration of rectal cancer on MRI
title_full_unstemmed Predictive value of infiltrating tumor border configuration of rectal cancer on MRI
title_short Predictive value of infiltrating tumor border configuration of rectal cancer on MRI
title_sort predictive value of infiltrating tumor border configuration of rectal cancer on mri
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571450/
https://www.ncbi.nlm.nih.gov/pubmed/37828450
http://dx.doi.org/10.1186/s12880-023-01118-y
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