Literature DB >> 24259298

Angiomyolipoma with minimal fat and non-clear cell renal cell carcinoma: differentiation on MDCT using classification and regression tree analysis-based algorithm.

Sungmin Woo1, Jeong Yeon Cho2, Seung Hyup Kim3, Sang Youn Kim1.   

Abstract

BACKGROUND: Differentiation between angiomyolipoma with minimal fat (AMLmf) and non-clear cell renal cell carcinoma (nccRCC) may be difficult owing to lack of macroscopic fat in AMLmf. However, the differential points between AMLmf and nccRCC has not been well established in the literature.
PURPOSE: To evaluate quantitative triphasic multidetector computed tomography (MDCT) features that differentiate between small AMLmf and nccRCC, and to integrate them to develop a simple and easy diagnostic algorithm.
MATERIAL AND METHODS: This study was approved by the Institutional Review Board; informed consent was waived. Triphasic MDCT images of pathologically-proven AMLmfs (n = 24) and nccRCCs (n = 55) of 79 patients were retrospectively evaluated. Age, sex, size, long-to-short axis ratio (LSR), attenuation and enhancement degree in all phases, unenhanced tumor-kidney attenuation difference (UTKAD) in Hounsfield units (HU) were compared with Chi-square analysis, independent-samples t-test, and receiver-operating characteristic (ROC) curves. A criterion was formulated with classification and regression tree analysis (CART). Thereafter, CART-based algorithm was tested with additional interpretations from two radiologists. Intra- and inter-observer variability was analyzed with Bland-Altman analysis.
RESULTS: LSR was greater in AMLmf than nccRCC (P < 0.001). AMLmf showed higher attenuation (all phases), CMP enhancement, and wash-out than nccRCC (P ≤ 0.001). UTKAD was greater in AMLmf than nccRCC (P < 0.001). ROC curve analysis yielded area under the curves of 0.936, 0.888, and 0.853 using UTKAD, unenhanced attenuation, and LSR. CART-based algorithm (UTKAD > 7.5 HU, LSR > 1.23) predicted AMLmf with sensitivity, specificity, PPV, and NPV of 87.5%, 96.4%, 91.3%, and 94.6%. Mean intra- and inter-observer difference was -0.1/0.03 HU and -1.0/0.09 HU for UTKAD/LSR, respectively. These interpretations changed the final diagnosis in 1.3% (1/79) and 5.1% (4/79) patients for radiologists 1 and 2.
CONCLUSION: Triphasic MDCT was useful for differentiating AMLmf and nccRCC. CART-based algorithm using UTKAD > 7.5 and LSR > 1.23 was simple and accurate in predicting AMLmf. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Entities:  

Keywords:  CT; Kidney; angiomyolipoma with minimal fat; classification and regression tree analysis; neoplasms – primary; non-clear cell renal cell carcinoma

Mesh:

Substances:

Year:  2013        PMID: 24259298     DOI: 10.1177/0284185113513887

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


  7 in total

Review 1.  Renal angiomyolipoma without visible fat: Can we make the diagnosis using CT and MRI?

Authors:  Robert S Lim; Trevor A Flood; Matthew D F McInnes; Luke T Lavallee; Nicola Schieda
Journal:  Eur Radiol       Date:  2017-08-04       Impact factor: 5.315

2.  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

3.  Advances of multidetector computed tomography in the characterization and staging of renal cell carcinoma.

Authors:  Athina C Tsili; Maria I Argyropoulou
Journal:  World J Radiol       Date:  2015-06-28

Review 4.  Renal angiomyolipoma: preoperative identification of atypical fat-poor AML.

Authors:  Crystal Farrell; Sabrina L Noyes; Mouafak Tourojman; Brian R Lane
Journal:  Curr Urol Rep       Date:  2015-03       Impact factor: 3.092

5.  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

Review 6.  Imaging findings of common benign renal tumors in the era of small renal masses: differential diagnosis from small renal cell carcinoma: current status and future perspectives.

Authors:  Sungmin Woo; Jeong Yeon Cho
Journal:  Korean J Radiol       Date:  2015-01-09       Impact factor: 3.500

7.  Dynamic Contrast-enhanced MRI in Renal Tumors: Common Subtype Differentiation using Pharmacokinetics.

Authors:  Hai-Yi Wang; Zi-Hua Su; Xiao Xu; Ning Huang; Zhi-Peng Sun; Ying-Wei Wang; Lu Li; Ai-Tao Guo; Xin Chen; Xin Ma; Lin Ma; Hui-Yi Ye
Journal:  Sci Rep       Date:  2017-06-08       Impact factor: 4.379

  7 in total

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