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Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices

Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically availabl...

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Autores principales: Zhao, Jianhong, Wu, Jiangpeng, Wei, Jinyan, Su, Xiaolu, Chai, Yanjun, Li, Shuyan, Wang, Zhiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873977/
https://www.ncbi.nlm.nih.gov/pubmed/33585225
http://dx.doi.org/10.3389/fonc.2020.605769
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author Zhao, Jianhong
Wu, Jiangpeng
Wei, Jinyan
Su, Xiaolu
Chai, Yanjun
Li, Shuyan
Wang, Zhiping
author_facet Zhao, Jianhong
Wu, Jiangpeng
Wei, Jinyan
Su, Xiaolu
Chai, Yanjun
Li, Shuyan
Wang, Zhiping
author_sort Zhao, Jianhong
collection PubMed
description Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC.
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spelling pubmed-78739772021-02-11 Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices Zhao, Jianhong Wu, Jiangpeng Wei, Jinyan Su, Xiaolu Chai, Yanjun Li, Shuyan Wang, Zhiping Front Oncol Oncology Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC. Frontiers Media S.A. 2021-01-06 /pmc/articles/PMC7873977/ /pubmed/33585225 http://dx.doi.org/10.3389/fonc.2020.605769 Text en Copyright © 2021 Zhao, Wu, Wei, Su, Chai, Li 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
Zhao, Jianhong
Wu, Jiangpeng
Wei, Jinyan
Su, Xiaolu
Chai, Yanjun
Li, Shuyan
Wang, Zhiping
Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices
title Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices
title_full Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices
title_fullStr Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices
title_full_unstemmed Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices
title_short Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices
title_sort liq_ccrcc: identification of clear cell renal cell carcinoma based on the integration of clinical liquid indices
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873977/
https://www.ncbi.nlm.nih.gov/pubmed/33585225
http://dx.doi.org/10.3389/fonc.2020.605769
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