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Tomography
Volume 6
Issue 4
10.18383/j.tom.2020.00039
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Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
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Review

Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma

by
Yuhan Zhang
1,
Xu Li
1,
Yang Lv
2 and
Xinquan Gu
1,*
1
Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China
2
Department of Anesthesia, The Second Hospital of Jilin University, Changchun, China
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(4), 325-332; https://doi.org/10.18383/j.tom.2020.00039
Submission received: 11 September 2020 / Revised: 5 October 2020 / Accepted: 4 November 2020 / Published: 1 December 2020
Versions Notes

Abstract

The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.
Keywords: texture analysis; renal AML; RCC; machine learning texture analysis; renal AML; RCC; machine learning

Share and Cite

MDPI and ACS Style

Zhang, Y.; Li, X.; Lv, Y.; Gu, X. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography 2020, 6, 325-332. https://doi.org/10.18383/j.tom.2020.00039

AMA Style

Zhang Y, Li X, Lv Y, Gu X. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography. 2020; 6(4):325-332. https://doi.org/10.18383/j.tom.2020.00039

Chicago/Turabian Style

Zhang, Yuhan, Xu Li, Yang Lv, and Xinquan Gu. 2020. "Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma" Tomography 6, no. 4: 325-332. https://doi.org/10.18383/j.tom.2020.00039

APA Style

Zhang, Y., Li, X., Lv, Y., & Gu, X. (2020). Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography, 6(4), 325-332. https://doi.org/10.18383/j.tom.2020.00039

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