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A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma

Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC). Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC pati...

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Autores principales: Jiang, Yi, Li, Wuchao, Huang, Chencui, Tian, Chong, Chen, Qi, Zeng, Xianchun, Cao, Yin, Chen, Yi, Yang, Yintong, Liu, Heng, Bo, Yonghua, Luo, Chenggong, Li, Yiming, Zhang, Tijiang, Wang, Rongping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272670/
https://www.ncbi.nlm.nih.gov/pubmed/32547934
http://dx.doi.org/10.3389/fonc.2020.00592
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author Jiang, Yi
Li, Wuchao
Huang, Chencui
Tian, Chong
Chen, Qi
Zeng, Xianchun
Cao, Yin
Chen, Yi
Yang, Yintong
Liu, Heng
Bo, Yonghua
Luo, Chenggong
Li, Yiming
Zhang, Tijiang
Wang, Rongping
author_facet Jiang, Yi
Li, Wuchao
Huang, Chencui
Tian, Chong
Chen, Qi
Zeng, Xianchun
Cao, Yin
Chen, Yi
Yang, Yintong
Liu, Heng
Bo, Yonghua
Luo, Chenggong
Li, Yiming
Zhang, Tijiang
Wang, Rongping
author_sort Jiang, Yi
collection PubMed
description Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC). Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis. Results: Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87–0.96]) and validation (AUC 0.87 [95% CI 0.79–0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models. Conclusion: The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC.
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spelling pubmed-72726702020-06-15 A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma Jiang, Yi Li, Wuchao Huang, Chencui Tian, Chong Chen, Qi Zeng, Xianchun Cao, Yin Chen, Yi Yang, Yintong Liu, Heng Bo, Yonghua Luo, Chenggong Li, Yiming Zhang, Tijiang Wang, Rongping Front Oncol Oncology Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC). Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis. Results: Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87–0.96]) and validation (AUC 0.87 [95% CI 0.79–0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models. Conclusion: The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC. Frontiers Media S.A. 2020-05-29 /pmc/articles/PMC7272670/ /pubmed/32547934 http://dx.doi.org/10.3389/fonc.2020.00592 Text en Copyright © 2020 Jiang, Li, Huang, Tian, Chen, Zeng, Cao, Chen, Yang, Liu, Bo, Luo, Li, Zhang and Wang. http://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
Jiang, Yi
Li, Wuchao
Huang, Chencui
Tian, Chong
Chen, Qi
Zeng, Xianchun
Cao, Yin
Chen, Yi
Yang, Yintong
Liu, Heng
Bo, Yonghua
Luo, Chenggong
Li, Yiming
Zhang, Tijiang
Wang, Rongping
A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma
title A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma
title_full A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma
title_fullStr A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma
title_full_unstemmed A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma
title_short A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma
title_sort computed tomography-based radiomics nomogram to preoperatively predict tumor necrosis in patients with clear cell renal cell carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272670/
https://www.ncbi.nlm.nih.gov/pubmed/32547934
http://dx.doi.org/10.3389/fonc.2020.00592
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