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A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy
BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive malignant tumor with a poor prognosis. This study aimed to establish a novel clinical-radiomics model for predicting the prognosis of ICC after radical hepatectomy. METHODS: A clinical-radiomics model was established for 82 cas...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8639693/ https://www.ncbi.nlm.nih.gov/pubmed/34868941 http://dx.doi.org/10.3389/fonc.2021.744311 |
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author | Deng, Liming Chen, Bo Zhan, Chenyi Yu, Haitao Zheng, Jiuyi Bao, Wenming Deng, Tuo Zheng, Chongming Wu, Lijun Yang, Yunjun Yu, Zhengping Wang, Yi Chen, Gang |
author_facet | Deng, Liming Chen, Bo Zhan, Chenyi Yu, Haitao Zheng, Jiuyi Bao, Wenming Deng, Tuo Zheng, Chongming Wu, Lijun Yang, Yunjun Yu, Zhengping Wang, Yi Chen, Gang |
author_sort | Deng, Liming |
collection | PubMed |
description | BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive malignant tumor with a poor prognosis. This study aimed to establish a novel clinical-radiomics model for predicting the prognosis of ICC after radical hepatectomy. METHODS: A clinical-radiomics model was established for 82 cases of ICC treated with radical hepatectomy in our hospital from May 2011 to December 2020. Radiomics features were extracted from venous-phase and arterial-phase images of computed tomography. Kaplan-Meier survival analysis was generated to compare overall survival (OS) between different groups. The independent factors were identified by univariate and multivariate Cox regression analyses. Nomogram performance was evaluated regarding discrimination, calibration, and clinical utility. C-index and area under the curve (AUC) were utilized to compare the predictive performance between the clinical-radiomics model and conventional staging systems. RESULTS: The radiomics model included five features. The AUC of the radiomics model was 0.817 in the training cohort, and 0.684 in the validation cohort. The clinical-radiomics model included psoas muscle index, radiomics score, hepatolithiasis, carcinoembryonic antigen, and neutrophil/lymphocyte ratio. The reliable C-index of the model was 0.768, which was higher than that of other models. The AUC of the model for predicting OS at 1, and 3 years was 0.809 and 0.886, which was significantly higher than that of the American Joint Committee on Cancer 8(th) staging system (0.594 and 0.619), radiomics model (0.743 and 0.770), and tumor differentiation (0.645 and 0.628). After stratification according to the constructed model, the median OS was 59.8 months for low-risk ICC patients and 10.1 months for high-risk patients (p < 0.0001). CONCLUSION: The clinical-radiomics model integrating sarcopenia, clinical features, and radiomics score was accurate for prognostic prediction for mass-forming ICC patients. It provided an individualized prognostic evaluation in patients with mass-forming ICC and could helped surgeons with clinical decision-making. |
format | Online Article Text |
id | pubmed-8639693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86396932021-12-04 A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy Deng, Liming Chen, Bo Zhan, Chenyi Yu, Haitao Zheng, Jiuyi Bao, Wenming Deng, Tuo Zheng, Chongming Wu, Lijun Yang, Yunjun Yu, Zhengping Wang, Yi Chen, Gang Front Oncol Oncology BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive malignant tumor with a poor prognosis. This study aimed to establish a novel clinical-radiomics model for predicting the prognosis of ICC after radical hepatectomy. METHODS: A clinical-radiomics model was established for 82 cases of ICC treated with radical hepatectomy in our hospital from May 2011 to December 2020. Radiomics features were extracted from venous-phase and arterial-phase images of computed tomography. Kaplan-Meier survival analysis was generated to compare overall survival (OS) between different groups. The independent factors were identified by univariate and multivariate Cox regression analyses. Nomogram performance was evaluated regarding discrimination, calibration, and clinical utility. C-index and area under the curve (AUC) were utilized to compare the predictive performance between the clinical-radiomics model and conventional staging systems. RESULTS: The radiomics model included five features. The AUC of the radiomics model was 0.817 in the training cohort, and 0.684 in the validation cohort. The clinical-radiomics model included psoas muscle index, radiomics score, hepatolithiasis, carcinoembryonic antigen, and neutrophil/lymphocyte ratio. The reliable C-index of the model was 0.768, which was higher than that of other models. The AUC of the model for predicting OS at 1, and 3 years was 0.809 and 0.886, which was significantly higher than that of the American Joint Committee on Cancer 8(th) staging system (0.594 and 0.619), radiomics model (0.743 and 0.770), and tumor differentiation (0.645 and 0.628). After stratification according to the constructed model, the median OS was 59.8 months for low-risk ICC patients and 10.1 months for high-risk patients (p < 0.0001). CONCLUSION: The clinical-radiomics model integrating sarcopenia, clinical features, and radiomics score was accurate for prognostic prediction for mass-forming ICC patients. It provided an individualized prognostic evaluation in patients with mass-forming ICC and could helped surgeons with clinical decision-making. Frontiers Media S.A. 2021-11-19 /pmc/articles/PMC8639693/ /pubmed/34868941 http://dx.doi.org/10.3389/fonc.2021.744311 Text en Copyright © 2021 Deng, Chen, Zhan, Yu, Zheng, Bao, Deng, Zheng, Wu, Yang, Yu, Wang and Chen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Deng, Liming Chen, Bo Zhan, Chenyi Yu, Haitao Zheng, Jiuyi Bao, Wenming Deng, Tuo Zheng, Chongming Wu, Lijun Yang, Yunjun Yu, Zhengping Wang, Yi Chen, Gang A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy |
title | A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy |
title_full | A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy |
title_fullStr | A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy |
title_full_unstemmed | A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy |
title_short | A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy |
title_sort | novel clinical-radiomics model based on sarcopenia and radiomics for predicting the prognosis of intrahepatic cholangiocarcinoma after radical hepatectomy |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8639693/ https://www.ncbi.nlm.nih.gov/pubmed/34868941 http://dx.doi.org/10.3389/fonc.2021.744311 |
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