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ID 113316
タイトル別表記
Standardization in radiomics analysis
著者
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
キーワード
Radiomics
Quantitative imaging
Standardization
Histology prediction
Machine learning
資料タイプ
学術雑誌論文
抄録
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.
掲載誌名
The Journal of Medical Investigation
ISSN
13496867
13431420
cat書誌ID
AA12022913
AA11166929
出版者
Tokushima University Faculty of Medicine
66
1-2
開始ページ
35
終了ページ
37
並び順
35
発行日
2019-02
EDB ID
出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
医学系