Literature DB >> 26998171

Lipid-poor renal angiomyolipoma: Differentiation from clear cell renal cell carcinoma using wash-in and washout characteristics on contrast-enhanced computed tomography.

Pingkun Xie1, Zhihui Yang2, Zheng Yuan3.   

Abstract

In the present study, a total of 82 patients (42 men and 40 women; age range, 24-84 years), including 34 patients with lipid-poor renal angiomyolipoma (AML) and 49 with clear cell renal cell carcinoma (RCC), who had undergone multiphase contrast-enhanced computed tomography (CT) (i.e., CT with unenhanced, corticomedullary, nephrographic and 5-min delay phase scanning) were evaluated. The peak enhancement attenuation value, net enhancement attenuation value, enhancement ratio, washout value and washout ratio were calculated to compare the enhancement characteristics between the two diseases. The results revealed that the lipid-poor AMLs had a significantly higher mean attenuation value compared with that of CCRCCs on unenhanced CT scans (37.8 vs. 30.9 HU; Mann-Whitney U test, P=0.003). In addition, significant differences were found between lipid-poor AMLs and CCRCCs with regard to wash-in (Mann-Whitney U test, P=0.001) and enhancement ratios (Mann-Whitney U test, P=0.010) on contrast-enhanced CT scans. A receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.722 using wash-in for differentiation between CCRCCs and lipid-poor AMLs. Lipid-poor AMLs exhibited a reduced washout of contrast enhancement (35.8±32.7 HU washout value; 29.4±0.187% washout ratio) compared with that of CCRCCs (48.3±28.4 HU washout value; 35.7±0.148% washout ratio; Mann-Whitney U test, P=0.037 and P=0.204, respectively). The ROC analysis result yielded an AUC of 0.639 for the use of washout to differentiate CCRCCs from lipid-poor AMLs. In summary, a larger wash-in and washout of contrast enhancement is a predictor that a lesion is CCRCC.

Entities:  

Keywords:  X-ray; clear cell renal cell carcinoma; computed tomography; lipid-poor; renal angiomyolipoma; washout

Year:  2016        PMID: 26998171      PMCID: PMC4774615          DOI: 10.3892/ol.2016.4214

Source DB:  PubMed          Journal:  Oncol Lett        ISSN: 1792-1074            Impact factor:   2.967


  16 in total

1.  Characterization of solitary pulmonary nodules: Use of washout characteristics at contrast-enhanced computed tomography.

Authors:  Xiao-Dan Ye; Jian-Ding Ye; Zheng Yuan; Sheng Dong; Xiang-Sheng Xiao
Journal:  Oncol Lett       Date:  2011-12-12       Impact factor: 2.967

2.  Imaging characteristics of minimal fat renal angiomyolipoma with histologic correlations.

Authors:  Jason Hafron; James D Fogarty; David M Hoenig; Maomi Li; Robert Berkenblit; Reza Ghavamian
Journal:  Urology       Date:  2005-12       Impact factor: 2.649

3.  Renal angiomyolipoma with minimal fat: differentiation from other neoplasms at double-echo chemical shift FLASH MR imaging.

Authors:  Jeong Kon Kim; Soo Hyun Kim; Yoon Jin Jang; Hanjong Ahn; Choung-Soo Kim; Hyungkeun Park; Jun Woo Lee; Suk Kim; Kyoung-Sik Cho
Journal:  Radiology       Date:  2006-02-28       Impact factor: 11.105

4.  Solitary pulmonary nodule: characterization with combined wash-in and washout features at dynamic multi-detector row CT.

Authors:  Yeon Joo Jeong; Kyung Soo Lee; Sun Young Jeong; Myung Jin Chung; Sung Shine Shim; Hojoong Kim; O Jung Kwon; Seonwoo Kim
Journal:  Radiology       Date:  2005-11       Impact factor: 11.105

5.  Characterization of indeterminate (lipid-poor) adrenal masses: use of washout characteristics at contrast-enhanced CT.

Authors:  C S Peña; G W Boland; P F Hahn; M J Lee; P R Mueller
Journal:  Radiology       Date:  2000-12       Impact factor: 11.105

6.  Diagnosis of angiomyolipoma using computed tomography-region of interest < or =-10 HU or 4 adjacent pixels < or =-10 HU are recommended as the diagnostic thresholds.

Authors:  E Simpson; U Patel
Journal:  Clin Radiol       Date:  2006-05       Impact factor: 2.350

7.  CT histogram analysis: differentiation of angiomyolipoma without visible fat from renal cell carcinoma at CT imaging.

Authors:  Ji Yeon Kim; Jeong Kon Kim; Namkug Kim; Kyoung-Sik Cho
Journal:  Radiology       Date:  2007-12-19       Impact factor: 11.105

8.  Pixel distribution analysis: can it be used to distinguish clear cell carcinomas from angiomyolipomas with minimal fat?

Authors:  Onofrio A Catalano; Anthony E Samir; Dushyant V Sahani; Peter F Hahn
Journal:  Radiology       Date:  2008-04-15       Impact factor: 11.105

Review 9.  Hereditary renal cancers.

Authors:  Peter L Choyke; Gladys M Glenn; McClellan M Walther; Berton Zbar; W Marston Linehan
Journal:  Radiology       Date:  2003-01       Impact factor: 11.105

10.  Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT.

Authors:  Jeong Kon Kim; Soo-Youn Park; Jeong-Hee Shon; Kyoung-Sik Cho
Journal:  Radiology       Date:  2004-03       Impact factor: 11.105

View more
  4 in total

1.  Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.

Authors:  Ruimeng Yang; Jialiang Wu; Lei Sun; Shengsheng Lai; Yikai Xu; Xilong Liu; Ying Ma; Xin Zhen
Journal:  Eur Radiol       Date:  2019-08-29       Impact factor: 5.315

2.  Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

Authors:  Zhichao Feng; Pengfei Rong; Peng Cao; Qingyu Zhou; Wenwei Zhu; Zhimin Yan; Qianyun Liu; Wei Wang
Journal:  Eur Radiol       Date:  2017-11-13       Impact factor: 5.315

3.  [A discrimination model for differentiation of renal cell carcinoma from renal angiomyolipoma without visible fat: based on hierarchical fusion framework of multi-classifier].

Authors:  T Mo; Y Wu; R Yang; X Zhen
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2022-08-20

4.  A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors.

Authors:  Mohamed Shehata; Ahmed Alksas; Rasha T Abouelkheir; Ahmed Elmahdy; Ahmed Shaffie; Ahmed Soliman; Mohammed Ghazal; Hadil Abu Khalifeh; Reem Salim; Ahmed Abdel Khalek Abdel Razek; Norah Saleh Alghamdi; Ayman El-Baz
Journal:  Sensors (Basel)       Date:  2021-07-20       Impact factor: 3.576

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.