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Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma

OBJECTIVES: To validate a new nomogram based on magnetic resonance imaging (MRI) for pre-operative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma (IMCC). METHODS: A total of 78 patients with clinicopathologically confirmed IMCC who underwent pre-operative gadoli...

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Autores principales: Chen, Xiang, Zhu, Jingfen, Zou, Zigui, Du, Mingzhan, Xie, Junjian, Ye, Yujie, Zhang, Ling, Li, Yonggang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902416/
https://www.ncbi.nlm.nih.gov/pubmed/36401626
http://dx.doi.org/10.1007/s00261-022-03719-7
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author Chen, Xiang
Zhu, Jingfen
Zou, Zigui
Du, Mingzhan
Xie, Junjian
Ye, Yujie
Zhang, Ling
Li, Yonggang
author_facet Chen, Xiang
Zhu, Jingfen
Zou, Zigui
Du, Mingzhan
Xie, Junjian
Ye, Yujie
Zhang, Ling
Li, Yonggang
author_sort Chen, Xiang
collection PubMed
description OBJECTIVES: To validate a new nomogram based on magnetic resonance imaging (MRI) for pre-operative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma (IMCC). METHODS: A total of 78 patients with clinicopathologically confirmed IMCC who underwent pre-operative gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid enhanced MRI between 2016 and 2022 were enrolled in the training and validation group (53 patients and 25 patients, respectively). Images including qualitative, quantitative MRI features and clinical data were evaluated. Univariate analysis and multivariate logistic regression were used to select the independent predictors and establish different predictive models. The predictive performance was validated by operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). The validation cohort was used to test the predictive performance of the optimal model. The nomogram was constructed with the optimal model. RESULTS: In the training cohort, independent predictors obtained from the combined model were DWI (OR 1822.741; 95% CI 6.189, 536,781.805; P = 0.01) and HBP enhancement pattern (OR 14.270; 95% CI 1.044, 195.039; P = 0.046). The combined model showed the good performance (AUC 0.981; 95% CI 0.952, 1.000) for predicting Ki-67 expression. In the validation cohort, The combined model (AUC 0.909; 95% CI 0.787, 1.000)showed the best performance compared to the clinical model (AUC 0.448; 95% CI 0.196, 0.700) and MRI model (AUC 0.770; 95% CI 0.570, 0.970). CONCLUSION: This new nomogram has a good performance in predicting Ki-67 expression in patients with IMCC, which could help the decision-making of the patients’ therapy strategies. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00261-022-03719-7.
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spelling pubmed-99024162023-02-08 Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma Chen, Xiang Zhu, Jingfen Zou, Zigui Du, Mingzhan Xie, Junjian Ye, Yujie Zhang, Ling Li, Yonggang Abdom Radiol (NY) Hepatobiliary OBJECTIVES: To validate a new nomogram based on magnetic resonance imaging (MRI) for pre-operative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma (IMCC). METHODS: A total of 78 patients with clinicopathologically confirmed IMCC who underwent pre-operative gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid enhanced MRI between 2016 and 2022 were enrolled in the training and validation group (53 patients and 25 patients, respectively). Images including qualitative, quantitative MRI features and clinical data were evaluated. Univariate analysis and multivariate logistic regression were used to select the independent predictors and establish different predictive models. The predictive performance was validated by operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). The validation cohort was used to test the predictive performance of the optimal model. The nomogram was constructed with the optimal model. RESULTS: In the training cohort, independent predictors obtained from the combined model were DWI (OR 1822.741; 95% CI 6.189, 536,781.805; P = 0.01) and HBP enhancement pattern (OR 14.270; 95% CI 1.044, 195.039; P = 0.046). The combined model showed the good performance (AUC 0.981; 95% CI 0.952, 1.000) for predicting Ki-67 expression. In the validation cohort, The combined model (AUC 0.909; 95% CI 0.787, 1.000)showed the best performance compared to the clinical model (AUC 0.448; 95% CI 0.196, 0.700) and MRI model (AUC 0.770; 95% CI 0.570, 0.970). CONCLUSION: This new nomogram has a good performance in predicting Ki-67 expression in patients with IMCC, which could help the decision-making of the patients’ therapy strategies. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00261-022-03719-7. Springer US 2022-11-19 2023 /pmc/articles/PMC9902416/ /pubmed/36401626 http://dx.doi.org/10.1007/s00261-022-03719-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) .
spellingShingle Hepatobiliary
Chen, Xiang
Zhu, Jingfen
Zou, Zigui
Du, Mingzhan
Xie, Junjian
Ye, Yujie
Zhang, Ling
Li, Yonggang
Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma
title Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma
title_full Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma
title_fullStr Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma
title_full_unstemmed Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma
title_short Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma
title_sort nomogram based on mri for preoperative prediction of ki-67 expression in patients with intrahepatic mass cholangiocarcinoma
topic Hepatobiliary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902416/
https://www.ncbi.nlm.nih.gov/pubmed/36401626
http://dx.doi.org/10.1007/s00261-022-03719-7
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