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Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures

Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 202...

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Autores principales: Zhu, Minyan, Tang, Lumin, Yang, Wenqi, Xu, Yao, Che, Xiajing, Zhou, Yin, Shao, Xinghua, Zhou, Wenyan, Zhang, Minfang, Li, Guanghan, Zheng, Min, Wang, Qin, Li, Hongli, Mou, Shan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689562/
https://www.ncbi.nlm.nih.gov/pubmed/36359519
http://dx.doi.org/10.3390/diagnostics12112678
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author Zhu, Minyan
Tang, Lumin
Yang, Wenqi
Xu, Yao
Che, Xiajing
Zhou, Yin
Shao, Xinghua
Zhou, Wenyan
Zhang, Minfang
Li, Guanghan
Zheng, Min
Wang, Qin
Li, Hongli
Mou, Shan
author_facet Zhu, Minyan
Tang, Lumin
Yang, Wenqi
Xu, Yao
Che, Xiajing
Zhou, Yin
Shao, Xinghua
Zhou, Wenyan
Zhang, Minfang
Li, Guanghan
Zheng, Min
Wang, Qin
Li, Hongli
Mou, Shan
author_sort Zhu, Minyan
collection PubMed
description Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 2020. The primary outcome was the initiation of renal replacement therapy or death, while the secondary outcome was eGFR < 60 mL/min/1.73 m(2). Ultrasound, biochemical, and biopsy examinations were performed on the same day. Radiomics signatures were extracted from the SWE images. Results: In total, 187 patients were included and followed up for 24.57 ± 5.52 months. The median SWE value of the left kidney cortex (L_C_median) is an independent risk factor for kidney prognosis for stage 3 or over (HR 0.890 (0.796–0.994), p < 0.05). The inclusion of 9 out of 2511 extracted radiomics signatures improved the prognostic performance of the Cox regression models containing the SWE and the traditional index (chi-square test, p < 0.001). The traditional Cox regression model had a c-index of 0.9051 (0.8460–0.9196), which was no worse than the machine learning models, Support Vector Machine (SVM), SurvivalTree, Random survival forest (RSF), Coxboost, and Deepsurv. Conclusions: SWE can predict kidney injury progression with an improved performance by radiomics and Cox regression modeling.
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spelling pubmed-96895622022-11-25 Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures Zhu, Minyan Tang, Lumin Yang, Wenqi Xu, Yao Che, Xiajing Zhou, Yin Shao, Xinghua Zhou, Wenyan Zhang, Minfang Li, Guanghan Zheng, Min Wang, Qin Li, Hongli Mou, Shan Diagnostics (Basel) Article Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 2020. The primary outcome was the initiation of renal replacement therapy or death, while the secondary outcome was eGFR < 60 mL/min/1.73 m(2). Ultrasound, biochemical, and biopsy examinations were performed on the same day. Radiomics signatures were extracted from the SWE images. Results: In total, 187 patients were included and followed up for 24.57 ± 5.52 months. The median SWE value of the left kidney cortex (L_C_median) is an independent risk factor for kidney prognosis for stage 3 or over (HR 0.890 (0.796–0.994), p < 0.05). The inclusion of 9 out of 2511 extracted radiomics signatures improved the prognostic performance of the Cox regression models containing the SWE and the traditional index (chi-square test, p < 0.001). The traditional Cox regression model had a c-index of 0.9051 (0.8460–0.9196), which was no worse than the machine learning models, Support Vector Machine (SVM), SurvivalTree, Random survival forest (RSF), Coxboost, and Deepsurv. Conclusions: SWE can predict kidney injury progression with an improved performance by radiomics and Cox regression modeling. MDPI 2022-11-03 /pmc/articles/PMC9689562/ /pubmed/36359519 http://dx.doi.org/10.3390/diagnostics12112678 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Minyan
Tang, Lumin
Yang, Wenqi
Xu, Yao
Che, Xiajing
Zhou, Yin
Shao, Xinghua
Zhou, Wenyan
Zhang, Minfang
Li, Guanghan
Zheng, Min
Wang, Qin
Li, Hongli
Mou, Shan
Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures
title Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures
title_full Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures
title_fullStr Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures
title_full_unstemmed Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures
title_short Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures
title_sort predicting progression of kidney injury based on elastography ultrasound and radiomics signatures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689562/
https://www.ncbi.nlm.nih.gov/pubmed/36359519
http://dx.doi.org/10.3390/diagnostics12112678
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