A primer on texture analysis in abdominal radiology

被引:13
|
作者
Horvat, Natally [1 ]
Miranda, Joao [2 ]
El Homsi, Maria [1 ]
Peoples, Jacob J. [3 ]
Long, Niamh M. [1 ]
Simpson, Amber L. [3 ,4 ]
Do, Richard K. G. [1 ]
机构
[1] Mem Sloan Kettering Canc Ctr, Dept Radiol, 1275 York Ave, New York, NY 10065 USA
[2] Univ Sao Paulo, Dept Radiol, Sao Paulo, SP, Brazil
[3] Queens Univ, Sch Comp, Kingston, ON, Canada
[4] Queens Univ, Dept Biomed & Mol Sci, Kingston, ON, Canada
关键词
Radiomics; Texture analysis; Machine learning; Magnetic resonance imaging; Computed tomography; Positron emission tomography; RADIOMIC FEATURES; GASTRIC-CANCER; CT; REPRODUCIBILITY; HETEROGENEITY; PREDICTION; REGRESSION; STABILITY; SELECTION; NOMOGRAM;
D O I
10.1007/s00261-021-03359-3
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
The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.
引用
收藏
页码:2972 / 2985
页数:14
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