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.
Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma
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
Received: 11 September 2020 / Revised: 5 October 2020 / Accepted: 4 November 2020 / Published: 1 December 2020
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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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 StyleZhang, Yuhan; Li, Xu; Lv, Yang; Gu, Xinquan. 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
Find Other Styles
