| Item type |
文献 / Documents(1) |
| 公開日 |
2022-04-22 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1117/12.2609296 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1117/12.2609296 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Visualization and unsupervised clustering of emphysema progression using t-SNE analysis of longitudinal CT images and SNPs |
|
言語 |
en |
| 著者 |
鈴木, 秀宣
松廣, 幹雄
河田, 佳樹
井本, 逸勢
ナカノ, ヤスタカ
クスモト, マサヒコ
カネコ, マサヒロ
仁木, 登
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
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. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Radiogenomics |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Computed tomography |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Single nucleotide polymorphism |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
t-distributed stochastic neighbor embedding |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Emphysema |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
3D U-Net |
| 書誌情報 |
en : Proceedings of SPIE
巻 12033,
p. 120331H,
発行日 2022-04-04
|
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
0277786X |
| 収録物ID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA10619755 |
| 出版者 |
|
|
出版者 |
SPIE |
|
言語 |
en |
| 備考 |
|
|
言語 |
ja |
|
値 |
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 |
|
|
言語 |
en |
|
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. |
| EID |
|
|
識別子 |
385225 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
procspie_12033_120331H.pdf (563 KB)
