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Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics

PURPOSE: Incidence and mortality of intrahepatic cholangiocarcinoma (ICC) have been increasing over the past few decades, and Ki67 is an adverse prognostic predictor and an attractive therapeutic target for ICC patients. Thus, we aim to develop and validate a combined Ki67 prediction model for ICC p...

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Autores principales: Qian, Xianling, Zhou, Changwu, Wang, Fang, Lu, Xin, Zhang, Yunfei, Chen, Lei, Zeng, Mengsu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020304/
https://www.ncbi.nlm.nih.gov/pubmed/36773271
http://dx.doi.org/10.1007/s11547-023-01597-7
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author Qian, Xianling
Zhou, Changwu
Wang, Fang
Lu, Xin
Zhang, Yunfei
Chen, Lei
Zeng, Mengsu
author_facet Qian, Xianling
Zhou, Changwu
Wang, Fang
Lu, Xin
Zhang, Yunfei
Chen, Lei
Zeng, Mengsu
author_sort Qian, Xianling
collection PubMed
description PURPOSE: Incidence and mortality of intrahepatic cholangiocarcinoma (ICC) have been increasing over the past few decades, and Ki67 is an adverse prognostic predictor and an attractive therapeutic target for ICC patients. Thus, we aim to develop and validate a combined Ki67 prediction model for ICC patients. MATERIALS AND METHODS: Preoperative contrast-enhanced MR images were collected from 178 patients with postoperative pathologically confirmed ICC, and randomly divided into training and validation cohorts in a ratio of 7:3 (124:54). A time-independent test cohort of 49 ICC patients was used for validation. Independent clinicoradiological features of Ki67 status were determined by multivariate analysis. Optimal radiomics features were selected by least absolute shrinkage and selection operator logistic regression and linear discriminant analysis was used to construct combined models. The prediction efficacy of combined model was assessed by receiver operating characteristics curve, and verified by its calibration, decision and clinical impact curves. RESULTS: HBV (p = 0.022), arterial rim enhancement (p = 0.006) and enhancement pattern (p = 0.012) are independent clinicoradiological features. The radiomics model achieves good prediction efficacy in the training cohort (AUC = 0.860) and validation cohort (AUC = 0.843). The combined Ki67 prediction model incorporates clinicoradiological and radiomics features, and it yields desirable predictive efficiency in test cohort (AUC = 0.815). Decision curves and clinical impact curves further validate that the combined Ki67 prediction model can achieve net benefits in clinical work. CONCLUSION: The combined Ki67 model incorporating HBV, arterial rim enhancement, enhancement pattern and radiomics features is a potential biomarker in Ki67 prediction and stratification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11547-023-01597-7.
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spelling pubmed-100203042023-03-18 Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics Qian, Xianling Zhou, Changwu Wang, Fang Lu, Xin Zhang, Yunfei Chen, Lei Zeng, Mengsu Radiol Med Abdominal Radiology PURPOSE: Incidence and mortality of intrahepatic cholangiocarcinoma (ICC) have been increasing over the past few decades, and Ki67 is an adverse prognostic predictor and an attractive therapeutic target for ICC patients. Thus, we aim to develop and validate a combined Ki67 prediction model for ICC patients. MATERIALS AND METHODS: Preoperative contrast-enhanced MR images were collected from 178 patients with postoperative pathologically confirmed ICC, and randomly divided into training and validation cohorts in a ratio of 7:3 (124:54). A time-independent test cohort of 49 ICC patients was used for validation. Independent clinicoradiological features of Ki67 status were determined by multivariate analysis. Optimal radiomics features were selected by least absolute shrinkage and selection operator logistic regression and linear discriminant analysis was used to construct combined models. The prediction efficacy of combined model was assessed by receiver operating characteristics curve, and verified by its calibration, decision and clinical impact curves. RESULTS: HBV (p = 0.022), arterial rim enhancement (p = 0.006) and enhancement pattern (p = 0.012) are independent clinicoradiological features. The radiomics model achieves good prediction efficacy in the training cohort (AUC = 0.860) and validation cohort (AUC = 0.843). The combined Ki67 prediction model incorporates clinicoradiological and radiomics features, and it yields desirable predictive efficiency in test cohort (AUC = 0.815). Decision curves and clinical impact curves further validate that the combined Ki67 prediction model can achieve net benefits in clinical work. CONCLUSION: The combined Ki67 model incorporating HBV, arterial rim enhancement, enhancement pattern and radiomics features is a potential biomarker in Ki67 prediction and stratification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11547-023-01597-7. Springer Milan 2023-02-11 2023 /pmc/articles/PMC10020304/ /pubmed/36773271 http://dx.doi.org/10.1007/s11547-023-01597-7 Text en © The Author(s) 2023 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 Abdominal Radiology
Qian, Xianling
Zhou, Changwu
Wang, Fang
Lu, Xin
Zhang, Yunfei
Chen, Lei
Zeng, Mengsu
Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics
title Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics
title_full Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics
title_fullStr Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics
title_full_unstemmed Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics
title_short Development and validation of combined Ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and MRI radiomics
title_sort development and validation of combined ki67 status prediction model for intrahepatic cholangiocarcinoma based on clinicoradiological features and mri radiomics
topic Abdominal Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020304/
https://www.ncbi.nlm.nih.gov/pubmed/36773271
http://dx.doi.org/10.1007/s11547-023-01597-7
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