Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools

doi:10.1186/s40644-021-00417-3

	Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools
	Wang, Xu 1,2; Song, Ge 1,2; Jiang, Haitao 1,2
刊名	CANCER IMAGING
	2021-07-05
卷号	21
ISSN号	1740-5025
DOI	10.1186/s40644-021-00417-3
通讯作者	Wang, Xu(wangxu@zjcc.org.cn)
英文摘要	BackgroundTo investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC).MethodsFour-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n=31) and ccRCC (n=74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated.ResultsAmong the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC=0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC=0.986).ConclusionThe combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.
WOS关键词	POOR ANGIOMYOLIPOMA ; MASSES DIFFERENTIATION ; IMAGING FEATURES ; TEXTURE ANALYSIS ; UNENHANCED CT ; DIAGNOSIS ; HETEROGENEITY ; CLASSIFICATION ; CRITERIA ; SUBTYPES
WOS研究方向	Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	BMC
WOS记录号	WOS:000672451700002
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/123586]
专题	中国科学院合肥物质科学研究院
通讯作者	Wang, Xu
作者单位	1.Chinese Acad Sci, Inst Canc & Basic Med, 1 Banshan East Rd, Hangzhou 310022, Zhejiang, Peoples R China 2.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Canc Hosp, Dept Radiol, 1 Banshan East Rd, Hangzhou 310022, Zhejiang, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xu,Song, Ge,Jiang, Haitao. Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools[J]. CANCER IMAGING,2021,21.
APA	Wang, Xu,Song, Ge,&Jiang, Haitao.(2021).Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools.CANCER IMAGING,21.
MLA	Wang, Xu,et al."Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools".CANCER IMAGING 21(2021).