Menglin Chen1,2, Fu Yin3, Yuanmeng Yu4, Haijie Zhang5, Ge Wen6. 1. Medical Imaging teaching and research office, Nanfang hospital, Southern Medical University, No.1838 Guangzhoudadao Avenue north, Guangzhou, 510515, Guangdong, China. 2. Radiology department, The second affiliated hospital of Kunming medical university, No. 374 Dianmian Road, Kunming, 650032, Yunnan, China. 3. College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518068, China. 4. Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, No. 157 Jinbi Road, Kunming, 650032, Yunnan, China. 5. Department of Radiology, Shenzhen Second People's Hospital, No.3002, West Sungang Road, Futian District, Shenzhen, 518052, China. zhanghaijie1984@163.com. 6. Medical Imaging teaching and research office, Nanfang hospital, Southern Medical University, No.1838 Guangzhoudadao Avenue north, Guangzhou, 510515, Guangdong, China. m13360022166@163.com.
Abstract
BACKGROUND: The aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs). METHODS: A total of 197 pathologically proven renal tumors were divided into ccRCC(n = 143) and non-ccRCC (n = 54) groups. The 43 non-texture features and 296 texture features that extracted from the 3D volume tumor tissue were assessed for each tumor at both Non-contrast Phase, NCP; Corticomedullary Phase, CMP; Nephrographic Phase, NP and Excretory Phase, EP. Texture-score were calculated by the Least Absolute Shrinkage and Selection Operator (LASSO) to screen the most valuable texture features. Model 1 contains the three most distinctive non-texture features with p < 0.001, Model 2 contains texture scores, and Model 3 contains the above two types of features. RESULTS: The three models shown good discrimination of the ccRCC from non-ccRCC in NCP, CMP, NP, and EP. The area under receiver operating characteristic curve (AUC)values of the Model 1, Model 2, and Model 3 in differentiating the two groups were 0.748-0.823, 0.776-0.887 and 0.864-0.900, respectively. The difference in AUC between every two of the three Models was statistically significant (p < 0.001). CONCLUSIONS: The predictive efficacy of ccRCC was significantly improved by combining non-texture features and texture features to construct a combined diagnostic model, which could provide a reliable basis for clinical treatment options.
BACKGROUND: The aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs). METHODS: A total of 197 pathologically proven renal tumors were divided into ccRCC(n = 143) and non-ccRCC (n = 54) groups. The 43 non-texture features and 296 texture features that extracted from the 3D volume tumor tissue were assessed for each tumor at both Non-contrast Phase, NCP; Corticomedullary Phase, CMP; Nephrographic Phase, NP and Excretory Phase, EP. Texture-score were calculated by the Least Absolute Shrinkage and Selection Operator (LASSO) to screen the most valuable texture features. Model 1 contains the three most distinctive non-texture features with p < 0.001, Model 2 contains texture scores, and Model 3 contains the above two types of features. RESULTS: The three models shown good discrimination of the ccRCC from non-ccRCC in NCP, CMP, NP, and EP. The area under receiver operating characteristic curve (AUC)values of the Model 1, Model 2, and Model 3 in differentiating the two groups were 0.748-0.823, 0.776-0.887 and 0.864-0.900, respectively. The difference in AUC between every two of the three Models was statistically significant (p < 0.001). CONCLUSIONS: The predictive efficacy of ccRCC was significantly improved by combining non-texture features and texture features to construct a combined diagnostic model, which could provide a reliable basis for clinical treatment options.
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