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A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma

PURPOSE: To develop and validate a clinical-radiomic nomogram for the preoperative prediction of the aldosterone-producing adenoma (APA) risk in patients with unilateral adrenal adenoma. PATIENTS AND METHODS: Ninety consecutive primary aldosteronism (PA) patients with unilateral adrenal adenoma who...

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Autores principales: He, Keng, Zhang, Zhao-Tao, Wang, Zhen-Hua, Wang, Yu, Wang, Yi-Xi, Zhang, Hong-Zhou, Dong, Yi-Fei, Xiao, Xin-Lan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8300014/
https://www.ncbi.nlm.nih.gov/pubmed/34307119
http://dx.doi.org/10.3389/fonc.2021.634879
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author He, Keng
Zhang, Zhao-Tao
Wang, Zhen-Hua
Wang, Yu
Wang, Yi-Xi
Zhang, Hong-Zhou
Dong, Yi-Fei
Xiao, Xin-Lan
author_facet He, Keng
Zhang, Zhao-Tao
Wang, Zhen-Hua
Wang, Yu
Wang, Yi-Xi
Zhang, Hong-Zhou
Dong, Yi-Fei
Xiao, Xin-Lan
author_sort He, Keng
collection PubMed
description PURPOSE: To develop and validate a clinical-radiomic nomogram for the preoperative prediction of the aldosterone-producing adenoma (APA) risk in patients with unilateral adrenal adenoma. PATIENTS AND METHODS: Ninety consecutive primary aldosteronism (PA) patients with unilateral adrenal adenoma who underwent adrenal venous sampling (AVS) were randomly separated into training (n = 62) and validation cohorts (n = 28) (7:3 ratio) by a computer algorithm. Data were collected from October 2017 to June 2020. The prediction model was developed in the training cohort. Radiomic features were extracted from unenhanced computed tomography (CT) images of unilateral adrenal adenoma. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensions, select features, and establish a radiomic signature. Multivariable logistic regression analysis was used for the predictive model development, the radiomic signature and clinical risk factors integration, and the model was displayed as a clinical-radiomic nomogram. The nomogram performance was evaluated by its calibration, discrimination, and clinical practicability. Internal validation was performed. RESULTS: Six potential predictors were selected from 358 texture features by using the LASSO regression model. These features were included in the Radscore. The predictors included in the individualized prediction nomogram were the Radscore, age, sex, serum potassium level, and aldosterone-to-renin ratio (ARR). The model showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.900 [95% confidence interval (CI), 0.807 to 0.993], and good calibration. The nomogram still showed good discrimination [AUC, 0.912 (95% CI, 0.761 to 1.000)] and good calibration in the validation cohort. Decision curve analysis presented that the nomogram was useful in clinical practice. CONCLUSIONS: A clinical-radiomic nomogram was constructed by integrating a radiomic signature and clinical factors. The nomogram facilitated accurate prediction of the probability of APA in patients with unilateral adrenal nodules and could be helpful for clinical decision making.
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spelling pubmed-83000142021-07-24 A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma He, Keng Zhang, Zhao-Tao Wang, Zhen-Hua Wang, Yu Wang, Yi-Xi Zhang, Hong-Zhou Dong, Yi-Fei Xiao, Xin-Lan Front Oncol Oncology PURPOSE: To develop and validate a clinical-radiomic nomogram for the preoperative prediction of the aldosterone-producing adenoma (APA) risk in patients with unilateral adrenal adenoma. PATIENTS AND METHODS: Ninety consecutive primary aldosteronism (PA) patients with unilateral adrenal adenoma who underwent adrenal venous sampling (AVS) were randomly separated into training (n = 62) and validation cohorts (n = 28) (7:3 ratio) by a computer algorithm. Data were collected from October 2017 to June 2020. The prediction model was developed in the training cohort. Radiomic features were extracted from unenhanced computed tomography (CT) images of unilateral adrenal adenoma. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensions, select features, and establish a radiomic signature. Multivariable logistic regression analysis was used for the predictive model development, the radiomic signature and clinical risk factors integration, and the model was displayed as a clinical-radiomic nomogram. The nomogram performance was evaluated by its calibration, discrimination, and clinical practicability. Internal validation was performed. RESULTS: Six potential predictors were selected from 358 texture features by using the LASSO regression model. These features were included in the Radscore. The predictors included in the individualized prediction nomogram were the Radscore, age, sex, serum potassium level, and aldosterone-to-renin ratio (ARR). The model showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.900 [95% confidence interval (CI), 0.807 to 0.993], and good calibration. The nomogram still showed good discrimination [AUC, 0.912 (95% CI, 0.761 to 1.000)] and good calibration in the validation cohort. Decision curve analysis presented that the nomogram was useful in clinical practice. CONCLUSIONS: A clinical-radiomic nomogram was constructed by integrating a radiomic signature and clinical factors. The nomogram facilitated accurate prediction of the probability of APA in patients with unilateral adrenal nodules and could be helpful for clinical decision making. Frontiers Media S.A. 2021-07-09 /pmc/articles/PMC8300014/ /pubmed/34307119 http://dx.doi.org/10.3389/fonc.2021.634879 Text en Copyright © 2021 He, Zhang, Wang, Wang, Wang, Zhang, Dong and Xiao 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
He, Keng
Zhang, Zhao-Tao
Wang, Zhen-Hua
Wang, Yu
Wang, Yi-Xi
Zhang, Hong-Zhou
Dong, Yi-Fei
Xiao, Xin-Lan
A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma
title A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma
title_full A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma
title_fullStr A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma
title_full_unstemmed A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma
title_short A Clinical-Radiomic Nomogram Based on Unenhanced Computed Tomography for Predicting the Risk of Aldosterone-Producing Adenoma
title_sort clinical-radiomic nomogram based on unenhanced computed tomography for predicting the risk of aldosterone-producing adenoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8300014/
https://www.ncbi.nlm.nih.gov/pubmed/34307119
http://dx.doi.org/10.3389/fonc.2021.634879
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