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Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma

OBJECTIVE: Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predic...

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Autores principales: Ma, Xijuan, Qian, Xianling, Wang, Qing, Zhang, Yunfei, Zong, Ruilong, Zhang, Jia, Qian, Baoxin, Yang, Chun, Lu, Xin, Shi, Yibing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620280/
https://www.ncbi.nlm.nih.gov/pubmed/37679641
http://dx.doi.org/10.1007/s11547-023-01704-8
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author Ma, Xijuan
Qian, Xianling
Wang, Qing
Zhang, Yunfei
Zong, Ruilong
Zhang, Jia
Qian, Baoxin
Yang, Chun
Lu, Xin
Shi, Yibing
author_facet Ma, Xijuan
Qian, Xianling
Wang, Qing
Zhang, Yunfei
Zong, Ruilong
Zhang, Jia
Qian, Baoxin
Yang, Chun
Lu, Xin
Shi, Yibing
author_sort Ma, Xijuan
collection PubMed
description OBJECTIVE: Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predicting MVI in ICC tumors. METHODS: 160 single ICC lesions with MRI scanning confirmed by postoperative pathology were randomly separated into training and validation cohorts (TC and VC). Multivariate analysis identified independent clinical and imaging MVI predictors. Radiomics features were obtained from images of 6 MRI sequences at 4 different VOIs. The least absolute shrinkage and selection operator algorithm was performed to enable the derivation of robust and effective radiomics features. Then, the best three sequences and the optimal VOI were obtained through comparison. The MVI prediction nomogram combined the independent predictors and optimal radiomics features, and its performance was evaluated via the receiver operating characteristics, calibration, and decision curves. RESULTS: Tumor size and intrahepatic ductal dilatation are independent MVI predictors. Radiomics features extracted from the best three sequences (T1WI-D, T1WI, DWI) with VOI(10mm) (including tumor and 10 mm peritumoral region) showed the best predictive performance, with AUC(TC) = 0.987 and AUC(VC) = 0.859. The MVI prediction nomogram obtained excellent prediction efficacy in both TC (AUC = 0.995, 95%CI 0.987–1.000) and VC (AUC = 0.867, 95%CI 0.798–0.921) and its clinical significance was further confirmed by the decision curves. CONCLUSION: A nomogram combining tumor size, intrahepatic ductal dilatation, and the radiomics model of MRI multi-sequence fusion at VOI(10mm) may be a predictor of preoperative MVI status in ICC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11547-023-01704-8.
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spelling pubmed-106202802023-11-03 Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma Ma, Xijuan Qian, Xianling Wang, Qing Zhang, Yunfei Zong, Ruilong Zhang, Jia Qian, Baoxin Yang, Chun Lu, Xin Shi, Yibing Radiol Med Abdominal Radiology OBJECTIVE: Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predicting MVI in ICC tumors. METHODS: 160 single ICC lesions with MRI scanning confirmed by postoperative pathology were randomly separated into training and validation cohorts (TC and VC). Multivariate analysis identified independent clinical and imaging MVI predictors. Radiomics features were obtained from images of 6 MRI sequences at 4 different VOIs. The least absolute shrinkage and selection operator algorithm was performed to enable the derivation of robust and effective radiomics features. Then, the best three sequences and the optimal VOI were obtained through comparison. The MVI prediction nomogram combined the independent predictors and optimal radiomics features, and its performance was evaluated via the receiver operating characteristics, calibration, and decision curves. RESULTS: Tumor size and intrahepatic ductal dilatation are independent MVI predictors. Radiomics features extracted from the best three sequences (T1WI-D, T1WI, DWI) with VOI(10mm) (including tumor and 10 mm peritumoral region) showed the best predictive performance, with AUC(TC) = 0.987 and AUC(VC) = 0.859. The MVI prediction nomogram obtained excellent prediction efficacy in both TC (AUC = 0.995, 95%CI 0.987–1.000) and VC (AUC = 0.867, 95%CI 0.798–0.921) and its clinical significance was further confirmed by the decision curves. CONCLUSION: A nomogram combining tumor size, intrahepatic ductal dilatation, and the radiomics model of MRI multi-sequence fusion at VOI(10mm) may be a predictor of preoperative MVI status in ICC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11547-023-01704-8. Springer Milan 2023-09-07 2023 /pmc/articles/PMC10620280/ /pubmed/37679641 http://dx.doi.org/10.1007/s11547-023-01704-8 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/) .
spellingShingle Abdominal Radiology
Ma, Xijuan
Qian, Xianling
Wang, Qing
Zhang, Yunfei
Zong, Ruilong
Zhang, Jia
Qian, Baoxin
Yang, Chun
Lu, Xin
Shi, Yibing
Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
title Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
title_full Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
title_fullStr Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
title_full_unstemmed Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
title_short Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
title_sort radiomics nomogram based on optimal voi of multi-sequence mri for predicting microvascular invasion in intrahepatic cholangiocarcinoma
topic Abdominal Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620280/
https://www.ncbi.nlm.nih.gov/pubmed/37679641
http://dx.doi.org/10.1007/s11547-023-01704-8
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