Literature DB >> 26663389

Angiomyolipoma with minimal fat: differentiation of morphological and enhancement features from renal cell carcinoma at CT imaging.

Chang Kyu Sung1, See Hyung Kim2, Sungmin Woo3, Min Hoan Moon1, Sang Youn Kim4, Seung Hyup Kim4, Jeong Yeon Cho5.   

Abstract

BACKGROUND: Angiomyolipoma (AML) with minimal fat may mimic renal cell carcinoma (RCC) and is difficult to distinguish from RCC with imaging studies alone. Precise diagnostic strategies have been explored to discern AML with minimal fat from RCC.
PURPOSE: To compare the morphological and enhancement features of AML with minimal fat with those of size-matched RCC on computed tomography (CT).
MATERIAL AND METHODS: Our study included 143 pathologically proved renal tumors (29 AML with minimal fat: mean diameter, 2.5 cm; range, 1.2-4 cm; 114 RCC: mean diameter, 2.8 cm; range, 1.3-4 cm). All patients underwent biphasic helical CTs. Two radiologists retrospectively evaluated the morphological (i.e. non-round and round appearances, with or without capsule) and enhancement features (i.e., wash-out, gradual, or prolonged). For the parameters that had statistically significance between the two groups, we calculated the positive and negative predictive values by using the univariate χ(2) test. P < 0.05 indicated a significant difference.
RESULTS: AML with minimal fat showed a non-round appearance without a capsule (n = 24, 83%) and prolonged enhancement (n = 20, 69%). The positive and negative predictive values of the non-round appearance without capsule for differentiating AML with minimal fat from RCC were 82.8% and 95.6%, respectively. The positive and negative predictive values of prolonged enhancement were 62.5% and 90.8%, respectively. These features were valuable predictors for AML with minimal fat from RCC.
CONCLUSION: CT images with non-round shape without capsule and prolonged enhancements may be used to differentiate AML with minimal fat from RCC. © The Foundation Acta Radiologica 2015.

Entities:  

Keywords:  Angiomyolipoma (AML); computed tomography (CT); renal cell carcinoma (RCC)

Mesh:

Substances:

Year:  2015        PMID: 26663389     DOI: 10.1177/0284185115618547

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  10 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.  External validation of a nomogram including the computed tomography imaging score to predict indolent renal masses.

Authors:  X Chen; B Wan; D Yang; H Zhao; W Tan
Journal:  Int Urol Nephrol       Date:  2017-04-17       Impact factor: 2.370

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.  MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study.

Authors:  Lian Jian; Yan Liu; Yu Xie; Shusuan Jiang; Mingji Ye; Huashan Lin
Journal:  Front Oncol       Date:  2022-05-26       Impact factor: 5.738

5.  Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors.

Authors:  Zhiwei Han; Yuanqiang Zhu; Jingji Xu; Didi Wen; Yuwei Xia; Minwen Zheng; Tao Yan; Mengqi Wei
Journal:  Dis Markers       Date:  2022-05-28       Impact factor: 3.464

6.  A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features.

Authors:  Xiao-Jie Wang; Bai-Qiang Qu; Jia-Ping Zhou; Qiao-Mei Zhou; Yuan-Fei Lu; Yao Pan; Jian-Xia Xu; You-You Miu; Hong-Qing Wang; Ri-Sheng Yu
Journal:  Front Oncol       Date:  2021-04-23       Impact factor: 6.244

7.  Magnetic resonance imaging features of minimal-fat angiomyolipoma and causes of preoperative misdiagnosis.

Authors:  Xiao-Long Li; Li-Xin Shi; Qi-Cong Du; Wei Wang; Li-Wei Shao; Ying-Wei Wang
Journal:  World J Clin Cases       Date:  2020-06-26       Impact factor: 1.337

8.  Multi-Phase Multiple Detector Computed Tomography (MDCT) Enhancement Patterns and Morphological Features of Chromophobe Renal Cell Carcinoma: An Analysis of 67 Cases.

Authors:  Min Luo; Yuting Zhu; Shaobin Chen; Qilin Huang; Wei Zhang; Mingping Ma; Yongbao Wei
Journal:  Med Sci Monit       Date:  2021-04-28

9.  Differential diagnosis and prognosis of small renal masses: association with collateral vessels detected using contrast-enhanced computed tomography.

Authors:  Masato Yanagi; Tomonari Kiriyama; Jun Akatsuka; Yuki Endo; Hayato Takeda; Akifumi Katsu; Yuichiro Honda; Kyota Suzuki; Yoshihiro Nishikawa; Shunsuke Ikuma; Hikaru Mikami; Yuka Toyama; Go Kimura; Yukihiro Kondo
Journal:  BMC Cancer       Date:  2022-08-05       Impact factor: 4.638

10.  Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma.

Authors:  Guangjie Yang; Aidi Gong; Pei Nie; Lei Yan; Wenjie Miao; Yujun Zhao; Jie Wu; Jingjing Cui; Yan Jia; Zhenguang Wang
Journal:  Mol Imaging       Date:  2019 Jan-Dec       Impact factor: 4.488

  10 in total

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