論文

査読有り 筆頭著者
2015年1月

Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer

JOURNAL OF BIOMEDICAL OPTICS
  • Atsushi Goto
  • ,
  • Jun Nishikawa
  • ,
  • Shu Kiyotoki
  • ,
  • Munetaka Nakamura
  • ,
  • Junichi Nishimura
  • ,
  • Takeshi Okamoto
  • ,
  • Hiroyuki Ogihara
  • ,
  • Yusuke Fujita
  • ,
  • Yoshihiko Hamamoto
  • ,
  • Isao Sakaida

20
1
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1117/1.JBO.20.1.016017
出版者・発行元
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS

Hyperspectral imaging (HSI) is a new technology that obtains spectroscopic information and renders it in image form. This study examined the difference in the spectral reflectance (SR) of gastric tumors and normal mucosa recorded with a hyperspectral camera equipped with HSI technology and attempted to determine the specific wavelength that is useful for the diagnosis of gastric cancer. A total of 104 gastric tumors removed by endoscopic submucosal dissection from 96 patients at Yamaguchi University Hospital were recorded using a hyperspectral camera. We determined the optimal wavelength and the cut-off value for differentiating tumors from normal mucosa to establish a diagnostic algorithm. We also attempted to highlight tumors by image processing using the hyperspectral camera's analysis software. A wavelength of 770 nm and a cut-off value of 1/4 the corrected SR were selected as the respective optimal wavelength and cut-off values. The rates of sensitivity, specificity, and accuracy of the algorithm's diagnostic capability were 71%, 98%, and 85%, respectively. It was possible to enhance tumors by image processing at the 770-nm wavelength. HSI can be used to measure the SR in gastric tumors and to differentiate between tumorous and normal mucosa. (C) 2015 Society of PhotoOptical Instrumentation Engineers (SPIE).

リンク情報
DOI
https://doi.org/10.1117/1.JBO.20.1.016017
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000350206400026&DestApp=WOS_CPL
ID情報
  • DOI : 10.1117/1.JBO.20.1.016017
  • ISSN : 1083-3668
  • eISSN : 1560-2281
  • Web of Science ID : WOS:000350206400026

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