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ID 116929
Author
Imoto, Issei Aichi Cancer Center Research Institute KAKEN Search Researchers
Nakano, Yasutaka Shiga University of Medical Science
Kusumoto, Masahiko National Cancer Center
Kaneko, Masahiro Tokyo Health Service Association
Keywords
Radiogenomics
Computed tomography
Single nucleotide polymorphism
t-distributed stochastic neighbor embedding
Emphysema
3D U-Net
Content Type
Journal Article
Description
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.
Journal Title
Proceedings of SPIE
ISSN
0277786X
NCID
AA10619755
Publisher
SPIE
Volume
12033
Start Page
120331H
Published Date
2022-04-04
Remark
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
Rights
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.
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language
eng
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departments
Science and Technology
Medical Sciences