Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Hodgdon Taryn; McInnes Matthew D. F.; Schieda Nicola; Flood Trevor A.; Lamb Leslie; Thornhill Rebecca E.

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Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

机译：可以使用定量CT纹理分析在未增强的CT图像上将低脂性肾血管平滑肌脂肪瘤与肾细胞癌区分开吗？

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Purpose: To determine the accuracy of texture analysis to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC) on unenhanced computed tomography (CT) images.

机译：目的：确定在未增强的计算机断层扫描（CT）图像上进行质地分析以区分脂肪少的血管平滑肌脂肪瘤（fp-AML）和肾细胞癌（RCC）的准确性。

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《Radiology》 |2015年第3期|共10页
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Hodgdon Taryn; McInnes Matthew D. F.; Schieda Nicola; Flood Trevor A.; Lamb Leslie; Thornhill Rebecca E.;
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正文语种 eng
中图分类放射医学;
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1. Re: Can Quantitative CT Texture Analysis be Used to Differentiate Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? [J] . Siegel Cary The Journal of Urology . 2016,第6期

机译：回复：CT CT定量分析可用于将CT增强图像上的脂肪少的肾血管平滑肌脂肪瘤与肾细胞癌区分开吗？
2. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification [J] . Lee Han Sang, Hong Helen, Jung Dae Chul, Medical Physics . 2017,第7期

机译：使用定量特征分类，从透明细胞肾细胞癌中的透明细胞肾细胞癌的脂肪贫血症瘤的分化
3. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma [J] . Zhichao Feng, Pengfei Rong, Peng Cao, European radiology . 2018,第4期

机译：基于机器学习的小肾肿块CT图像的定量纹理分析：肾细胞癌无可见脂肪的血管肌脂肪疟原虫的分化
4. An Effective Radiomics Model for Noninvasive Discrimination of Fat-poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma [C] . Yi Yang, Jianbing Zhu, Zhiyong Zhou, IEEE Symposium Series on Computational Intelligence . 2019

机译：从透明细胞肾细胞癌无脂肪鉴别血管性脂肪异常的有效放射学模型。
5. Analysis of prognostic factors for the development of metastases and survival in renal cell carcinoma patients [D] . Cheng, Jed-Sian 2008

机译：肾细胞癌患者转移和存活的预后因素分析
6. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma [O] . Yuhan Zhang, Xu Li, Yang Lv, 2020

机译：CT纹理分析与机器学习价值综述肾细胞癌含脂肪差肾血管肾病瘤
7. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma [O] . Yuhan Zhang, Xu Li, Yang Lv, 2020

机译：CT纹理分析与机器学习价值综述肾细胞癌含脂肪差肾血管肾病瘤

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

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