Radiomics and Artificial Intelligence for Renal Mass Characterization

被引:36
|
作者
Lubner, Meghan G. [1 ]
机构
[1] Univ Wisconsin, Dept Radiol, Sch Med & Publ Hlth, E3-311 Clin Sci Ctr,600 Highland Ave, Madison, WI 53792 USA
关键词
Renal cell carcinoma; Angiomyolipoma; Oncocytoma; CT; MR imaging; Texture; Radiomics; CT TEXTURE ANALYSIS; CARCINOMA ATTENUATION VALUES; CELL-CARCINOMA; UNENHANCED CT; TUMOR HETEROGENEITY; MINIMAL FAT; VISIBLE FAT; CM; IMAGING FEATURES; RISING INCIDENCE;
D O I
10.1016/j.rcl.2020.06.001
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.
引用
收藏
页码:995 / +
页数:15
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