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Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma

BACKGROUND: Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations. METHODS: This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and interna...

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Autores principales: Yu, Xinxin, Gao, Lin, Zhang, Shuai, Sun, Cong, Zhang, Juntao, Kang, Bing, Wang, Ximing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846314/
https://www.ncbi.nlm.nih.gov/pubmed/36686790
http://dx.doi.org/10.3389/fonc.2022.1016583
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author Yu, Xinxin
Gao, Lin
Zhang, Shuai
Sun, Cong
Zhang, Juntao
Kang, Bing
Wang, Ximing
author_facet Yu, Xinxin
Gao, Lin
Zhang, Shuai
Sun, Cong
Zhang, Juntao
Kang, Bing
Wang, Ximing
author_sort Yu, Xinxin
collection PubMed
description BACKGROUND: Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations. METHODS: This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed. RESULTS: Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness. CONCLUSIONS: The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.
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spelling pubmed-98463142023-01-19 Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma Yu, Xinxin Gao, Lin Zhang, Shuai Sun, Cong Zhang, Juntao Kang, Bing Wang, Ximing Front Oncol Oncology BACKGROUND: Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations. METHODS: This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed. RESULTS: Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness. CONCLUSIONS: The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9846314/ /pubmed/36686790 http://dx.doi.org/10.3389/fonc.2022.1016583 Text en Copyright © 2023 Yu, Gao, Zhang, Sun, Zhang, Kang and Wang 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
Yu, Xinxin
Gao, Lin
Zhang, Shuai
Sun, Cong
Zhang, Juntao
Kang, Bing
Wang, Ximing
Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma
title Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma
title_full Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma
title_fullStr Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma
title_full_unstemmed Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma
title_short Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma
title_sort development and validation of a ct-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846314/
https://www.ncbi.nlm.nih.gov/pubmed/36686790
http://dx.doi.org/10.3389/fonc.2022.1016583
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