TY - JOUR
T1 - Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging
AU - Sato, Daiki
AU - Takamatsu, Toshihiro
AU - Umezawa, Masakazu
AU - Kitagawa, Yuichi
AU - Maeda, Kosuke
AU - Hosokawa, Naoki
AU - Okubo, Kyohei
AU - Kamimura, Masao
AU - Kadota, Tomohiro
AU - Akimoto, Tetsuo
AU - Kinoshita, Takahiro
AU - Yano, Tomonori
AU - Kuwata, Takeshi
AU - Ikematsu, Hiroaki
AU - Takemura, Hiroshi
AU - Yokota, Hideo
AU - Soga, Kohei
N1 - Funding Information:
The authors wish to acknowledge Mr. Yuya Yasuda for his assistance with NIR-HSI images analysis. Further, we would like to appreciate Dr. Kazuhiro Kaneko for his contribution and great efforts in the planning of this study. A part of this study was supported by The National Cancer Center Research and Development Fund (29-A-10 and 31-A-11).
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12
Y1 - 2020/12
N2 - The diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical information from deep inside tissues. However, far less progress has been made in the development of techniques for distinguishing deep lesions like GIST. This study aimed to investigate whether NIR-HSI is suitable for distinguishing deep SMT lesions. In this study, 12 gastric GIST lesions were surgically resected and imaged with an NIR hyperspectral camera from the aspect of the mucosal surface. Thus, the images were obtained ex-vivo. The site of the GIST was defined by a pathologist using the NIR image to prepare training data for normal and GIST regions. A machine learning algorithm, support vector machine, was then used to predict normal and GIST regions. Results were displayed using color-coded regions. Although 7 specimens had a mucosal layer (thickness 0.4–2.5 mm) covering the GIST lesion, NIR-HSI analysis by machine learning showed normal and GIST regions as color-coded areas. The specificity, sensitivity, and accuracy of the results were 73.0%, 91.3%, and 86.1%, respectively. The study suggests that NIR-HSI analysis may potentially help distinguish deep lesions.
AB - The diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical information from deep inside tissues. However, far less progress has been made in the development of techniques for distinguishing deep lesions like GIST. This study aimed to investigate whether NIR-HSI is suitable for distinguishing deep SMT lesions. In this study, 12 gastric GIST lesions were surgically resected and imaged with an NIR hyperspectral camera from the aspect of the mucosal surface. Thus, the images were obtained ex-vivo. The site of the GIST was defined by a pathologist using the NIR image to prepare training data for normal and GIST regions. A machine learning algorithm, support vector machine, was then used to predict normal and GIST regions. Results were displayed using color-coded regions. Although 7 specimens had a mucosal layer (thickness 0.4–2.5 mm) covering the GIST lesion, NIR-HSI analysis by machine learning showed normal and GIST regions as color-coded areas. The specificity, sensitivity, and accuracy of the results were 73.0%, 91.3%, and 86.1%, respectively. The study suggests that NIR-HSI analysis may potentially help distinguish deep lesions.
UR - http://www.scopus.com/inward/record.url?scp=85098476131&partnerID=8YFLogxK
U2 - 10.1038/s41598-020-79021-7
DO - 10.1038/s41598-020-79021-7
M3 - Article
C2 - 33318595
AN - SCOPUS:85098476131
VL - 10
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 21852
ER -