直近一年間の累計
アクセス数 : ?
ダウンロード数 : ?
ID 116929
著者
井本, 逸勢 Aichi Cancer Center Research Institute KAKEN研究者をさがす
Nakano, Yasutaka Shiga University of Medical Science
Kusumoto, Masahiko National Cancer Center
Kaneko, Masahiro Tokyo Health Service Association
キーワード
Radiogenomics
Computed tomography
Single nucleotide polymorphism
t-distributed stochastic neighbor embedding
Emphysema
3D U-Net
資料タイプ
学術雑誌論文
抄録
Chronic obstructive pulmonary disease (COPD) is predicted to become the third leading cause of death worldwide by 2030. A longitudinal study using CT scans of COPD is useful to assess the changes in structural abnormalities. In this study, we performed visualization and unsupervised clustering of emphysema progression using t-distributed stochastic neighbor embedding (t-SNE) analysis of longitudinal CT images, smoking history, and SNPs. The procedure of this analysis is as follows: (1) automatic segmentation of lung lobes using 3D U-Net, (2) quantitative image analysis of emphysema progression in lung lobes, and (3) visualization and unsupervised clustering of emphysema progression using t-SNE. Nine explanatory variables were used for the clustering: genotypes at two SNPs (rs13180 and rs3923564), smoking history (smoking years, number of cigarettes per day, pack-year), and LAV distribution (LAV size and density in upper lobes, LAV size, and density in lower lobes). The objective variable was emphysema progression which was defined as the annual change in low attenuation volume (LAV%/year) using linear regression. The nine-dimensional space was transformed to two-dimensional space by t-SNE, and divided into three clusters by Gaussian mixture model. This method was applied to 37 smokers with 68.2 pack-years and 97 past smokers with 51.1 pack-years. The results demonstrated that this method could be effective for quantitative assessment of emphysema progression by SNPs, smoking history, and imaging features.
掲載誌名
Proceedings of SPIE
ISSN
0277786X
cat書誌ID
AA10619755
出版者
SPIE
12033
開始ページ
120331H
発行日
2022-04-04
備考
Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Issei Imoto, Yasutaka Nakano, Masahiko Kusumoto, Masahiro Kaneko, and Noboru Niki "Visualization and unsupervised clustering of emphysema progression using t-SNE analysis of longitudinal CT images and SNPs", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331H (4 April 2022); https://doi.org/10.1117/12.2609296
権利情報
Copyright 2022 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
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
理工学系
医学系