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ID 113316
Title Alternative
Standardization in radiomics analysis
Author
Takahashi, Wataru The University of Tokyo
Aoki, Shuri The University of Tokyo
Nawa, Kanabu The University of Tokyo
Yamashita, Hideomi The University of Tokyo
Abe, Osamu The University of Tokyo
Nakagawa, Keiichi The University of Tokyo
Keywords
Radiomics
Quantitative imaging
Standardization
Histology prediction
Machine learning
Content Type
Journal Article
Description
Radiomics has the potential to provide tumor characteristics with noninvasive and repeatable way. The purpose of this paper is to evaluate the standardization effect of imaging features for radiomics analysis. For this purpose, we prepared two CT databases ; one includes 40 non-small cell lung cancer (NSCLC) patients for whom tumor biopsies was performed before stereotactic body radiation therapy in The University of Tokyo Hospital, and the other includes 29 early-stage NSCLC datasets from the Cancer Imaging Archive. The former was used as the training data, whereas the later was used as the test data in the evaluation of the prediction model. In total, 476 imaging features were extracted from each data. Then, both training and test data were standardized as the min-max normalization, the z-score normalization, and the whitening from the principle component analysis. All of standardization strategies improved the accuracy for the histology prediction. The area under the receiver observed characteristics curve was 0.725, 0.789, and 0.785 in above standardizations, respectively. Radiomics analysis has shown that robust features have a high prognostic power in predicting early-stage NSCLC histology subtypes. The performance was able to be improved by standardizing the data in the feature space.
Journal Title
The Journal of Medical Investigation
ISSN
13496867
13431420
NCID
AA12022913
AA11166929
Publisher
Tokushima University Faculty of Medicine
Volume
66
Issue
1-2
Start Page
35
End Page
37
Sort Key
35
Published Date
2019-02
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Medical Sciences