Spatial assessments in texture analysis: what the radiologist needs to know

Frontiers in Radiology

Radiology

Brandon K. K. Fields¹  George R. Matcuk²  Bino A. Varghese³  Vinay A. Duddalwar³  Darryl H. Hwang³  Steven Y. Cen³ 

[1] Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States;Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States;Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States;

关键词: radiomics;    texture analysis;    spatial assessment;    machine learning;    artificial intelligence;   

DOI  :  10.3389/fradi.2023.1240544

 received in 2023-06-15, accepted in 2023-08-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.

【 授权许可】

Unknown   
© 2023 Varghese, Fields, Hwang, Duddalwar, Matcuk and Cen.