A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

ObjectiveThis study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).Methods370 patients with complete clinical, pathological, and CT image dat...

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Autores principales:	Yingjie Xv, Fajin Lv, Haoming Guo, Zhaojun Liu, Di Luo, Jing Liu, Xin Gou, Weiyang He, Mingzhao Xiao, Yineng Zheng
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	radiomics nomogram computed tomography clear cell renal cell carcinoma WHO/ISUP grading prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/4e0313e519bd4cefbbcec6f4d3136cd8
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spelling	oai:doaj.org-article:4e0313e519bd4cefbbcec6f4d3136cd82021-12-03T04:31:50ZA CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma2234-943X10.3389/fonc.2021.712554https://doaj.org/article/4e0313e519bd4cefbbcec6f4d3136cd82021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.712554/fullhttps://doaj.org/toc/2234-943XObjectiveThis study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).Methods370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsUsing multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.ConclusionThe CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.Yingjie XvYingjie XvFajin LvHaoming GuoZhaojun LiuDi LuoDi LuoJing LiuXin GouWeiyang HeMingzhao XiaoYineng ZhengFrontiers Media S.A.articleradiomics nomogramcomputed tomographyclear cell renal cell carcinomaWHO/ISUP gradingprediction modelNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radiomics nomogram computed tomography clear cell renal cell carcinoma WHO/ISUP grading prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	radiomics nomogram computed tomography clear cell renal cell carcinoma WHO/ISUP grading prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Yingjie Xv Yingjie Xv Fajin Lv Haoming Guo Zhaojun Liu Di Luo Di Luo Jing Liu Xin Gou Weiyang He Mingzhao Xiao Yineng Zheng A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma
description	ObjectiveThis study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).Methods370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsUsing multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.ConclusionThe CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.
format	article
author	Yingjie Xv Yingjie Xv Fajin Lv Haoming Guo Zhaojun Liu Di Luo Di Luo Jing Liu Xin Gou Weiyang He Mingzhao Xiao Yineng Zheng
author_facet	Yingjie Xv Yingjie Xv Fajin Lv Haoming Guo Zhaojun Liu Di Luo Di Luo Jing Liu Xin Gou Weiyang He Mingzhao Xiao Yineng Zheng
author_sort	Yingjie Xv
title	A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma
title_short	A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma
title_full	A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma
title_fullStr	A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma
title_full_unstemmed	A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma
title_sort	ct-based radiomics nomogram integrated with clinic-radiological features for preoperatively predicting who/isup grade of clear cell renal cell carcinoma
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/4e0313e519bd4cefbbcec6f4d3136cd8
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_version_	1718373928873754624

A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

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