Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas

Abstract This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC...

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Autores principales:	Ping Wang, Xu Pei, Xiao-Ping Yin, Jia-Liang Ren, Yun Wang, Lu-Yao Ma, Xiao-Guang Du, Bu-Lang Gao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/acf1a865aae440a3b0aee58e4b605312
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spelling	oai:doaj.org-article:acf1a865aae440a3b0aee58e4b6053122021-12-02T18:18:33ZRadiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas10.1038/s41598-021-93069-z2045-2322https://doaj.org/article/acf1a865aae440a3b0aee58e4b6053122021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93069-zhttps://doaj.org/toc/2045-2322Abstract This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC confirmed by pathology were retrospectively analyzed, with the patients being randomly divided into two groups, including the training set and testing set according to the ratio of 7:3. A total of 396 radiomic features were computationally obtained and analyzed with the Correlation between features, Univariate Logistics and Multivariate Logistics. Finally, 4 features were selected, and three machine models (Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR)) were established to discriminate RCC subtypes. The radiomics performance was compared with that of radiologist diagnosis. In the testing set, the RF model had an area under the curve (AUC) value of 0.909, a sensitivity of 0.956, and a specificity of 0.538. The SVM model had an AUC value of 0.841, a sensitivity of 1.0, and a specificity of 0.231, in the testing set. The LR model had an AUC value of 0.906, a sensitivity of 0.956, and a specificity of 0.692, in the testing set. The sensitivity and specificity of radiologist diagnosis to differentiate ccRCC from non-ccRCC were 0.850 and 0.581, respectively, with the AUC value of the radiologist diagnosis as 0.69. In conclusion, radiomics models based on CT imaging data show promise for augmenting radiological diagnosis in renal cancer, especially for differentiating ccRCC from non-ccRCC.Ping WangXu PeiXiao-Ping YinJia-Liang RenYun WangLu-Yao MaXiao-Guang DuBu-Lang GaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ping Wang Xu Pei Xiao-Ping Yin Jia-Liang Ren Yun Wang Lu-Yao Ma Xiao-Guang Du Bu-Lang Gao Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
description	Abstract This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC confirmed by pathology were retrospectively analyzed, with the patients being randomly divided into two groups, including the training set and testing set according to the ratio of 7:3. A total of 396 radiomic features were computationally obtained and analyzed with the Correlation between features, Univariate Logistics and Multivariate Logistics. Finally, 4 features were selected, and three machine models (Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR)) were established to discriminate RCC subtypes. The radiomics performance was compared with that of radiologist diagnosis. In the testing set, the RF model had an area under the curve (AUC) value of 0.909, a sensitivity of 0.956, and a specificity of 0.538. The SVM model had an AUC value of 0.841, a sensitivity of 1.0, and a specificity of 0.231, in the testing set. The LR model had an AUC value of 0.906, a sensitivity of 0.956, and a specificity of 0.692, in the testing set. The sensitivity and specificity of radiologist diagnosis to differentiate ccRCC from non-ccRCC were 0.850 and 0.581, respectively, with the AUC value of the radiologist diagnosis as 0.69. In conclusion, radiomics models based on CT imaging data show promise for augmenting radiological diagnosis in renal cancer, especially for differentiating ccRCC from non-ccRCC.
format	article
author	Ping Wang Xu Pei Xiao-Ping Yin Jia-Liang Ren Yun Wang Lu-Yao Ma Xiao-Guang Du Bu-Lang Gao
author_facet	Ping Wang Xu Pei Xiao-Ping Yin Jia-Liang Ren Yun Wang Lu-Yao Ma Xiao-Guang Du Bu-Lang Gao
author_sort	Ping Wang
title	Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
title_short	Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
title_full	Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
title_fullStr	Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
title_full_unstemmed	Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
title_sort	radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/acf1a865aae440a3b0aee58e4b605312
work_keys_str_mv	AT pingwang radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas AT xupei radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas AT xiaopingyin radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas AT jialiangren radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas AT yunwang radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas AT luyaoma radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas AT xiaoguangdu radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas AT bulanggao radiomicsmodelsbasedonenhancedcomputedtomographytodistinguishclearcellfromnonclearcellrenalcellcarcinomas
_version_	1718378235284160512

Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas

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