Are radiomics features universally applicable to different organs?

Cancer Imaging

Hyunjin Park¹  Hwan-ho Cho²  Junmo Kwon²  Seung-Hak Lee³  Ho Yun Lee⁴ 

[1] Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), 16419, Suwon, South Korea;School of Electronic and Electrical Engineering, Center for Neuroscience Imaging Research, Sungkyunkwan University, 16419, Suwon, South Korea;Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, 16419, Suwon, South Korea;Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), 16419, Suwon, South Korea;Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, 16419, Suwon, South Korea;Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), 16419, Suwon, South Korea;Core Research & Development Center, Korea University Ansan Hospital, 15355, Ansan, South Korea;Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, 06351, Seoul, South Korea;Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, 06351, Seoul, South Korea;

关键词: Radiomics;    Macroscale tumor features;    Tumor microenvironment;    Computed tomography;    Magnetic resonance imaging;    Survival analysis;   

DOI  :  10.1186/s40644-021-00400-y

来源: Springer

【 摘 要 】

BackgroundMany studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments.MethodsFour datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated.ResultsEach organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified.ConclusionAlthough the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties.

【 授权许可】

CC BY