Value of CT texture analysis in the preoperative prediction of Fuhrman grade of clear cell renal cell carcinoma
10.3760/cma.j.issn.1005-1201.2018.08.009
- VernacularTitle: CT纹理分析术前预测肾脏透明细胞癌Fuhrman分级的价值
- Author:
Jiule DING
1
;
Zhaoyu XING
;
Zhen CHEN
;
Shengnan YU
;
Jun SUN
;
Jie CHEN
;
Jianguo QIU
;
Wei XING
Author Information
1. Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou 213003, China
- Publication Type:Journal Article
- Keywords:
Renal neoplasms;
Tomography,X-ray computed;
Texture analysis;
Grade
- From:
Chinese Journal of Radiology
2018;52(8):614-618
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To detect the values of CT texture features in the preoperative prediction of Fuhrman grade of clear cell renal cell carcinoma (ccRCC).
Methods:The CT data of 206 patients with ccRCC admitted to the Third Affiliated Hospital of Soochow University from January 2011 to December 2016 were retrospectively analyzed, and the ccRCC cases were graded using Fuhrman grading system, including 38 cases of Grade Ⅰ, 107 cases of Grade Ⅱ, 50 cases of Grade Ⅲ and 11 cases of Grade Ⅳ. All subjects undergone plain and enhancement CT scans. There were two methods used for the extraction of texture features, including histogram (2 features: Kurtosis and Skewness) and gray-level co-occurrence matrix (6 features: Contrast, Correlation, Energy, Entropy, Homogeneity and Variance). Each texture feature during Grade Ⅰ to Ⅳ was compared using a one-way analysis of variance following the log-ratio transformation, and a Newman-Keuls test was performed for all pairwise comparisons. An independent sample t test was used to find the differences of each texture feature between low (Grade Ⅰ+Ⅱ) and high grade (Grade Ⅲ+Ⅳ) ccRCC. A Spearman Rank test was performed to quantify the correlation of each texture feature with Fuhrman grade in ccRCC. Receiver operating characteristic curve (ROC) was employed to compare the diagnostic performance of the texture features to differentiate the low grade from high grade ccRCC.
Results:Six texture features, including Contrast, Correlation, Entropy, Homogeneity, Variance and Kurtosis, were different during Grade Ⅰ to Ⅳ (all P<0.05) with the exception of the two features of Energy and Skewness (all P>0.05). Furthermore, five textures, such as Correlation, Entropy, Homogeneity, Variance and Kurtosis, were not significantly different between Grade Ⅲ and Ⅳ ccRCC. There was no clinical application value for the features of Correlation, Energy, Entropy, Variance and Skewness with the absolute coefficients of<0.3, in contrast, the correlation coefficients were -0.54, 0.39 and 0.32 for the features of Contrast, Homogeneity and Kurtosis, respectively (all P<0.05). Compared with that in the low grade ccRCC, the values of Contrast and Variance reduced in the high grade ccRCC (all P<0.05), while the values of Kurtosis, Correlation and Homogeneity increased significantly in the high grade ccRCC (all P<0.05), and no difference was found for the features of Skewness, Energy and Entropy between the low and high grade ccRCC (all P>0.05). When those features were used to differentiate the high from low grade ccRCC, the Contrast exhibited the biggest area under ROC of 0.806 (P<0.05), followed by the Correlation of 0.641, Homogeneity of 0.687, Kurtosis of 0.668 and Variance of 0.659.
Conclusion:CT texture features can preoperatively predict the Fuhrman grade of ccRCC, and the Contrast will likely be the potential imaging biomarker for the clinical application.
