Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging
Abstract 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, f...
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2020
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oai:doaj.org-article:5d643fe6f2454a899dfcc37d47688e922021-12-02T13:58:13ZDistinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging10.1038/s41598-020-79021-72045-2322https://doaj.org/article/5d643fe6f2454a899dfcc37d47688e922020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79021-7https://doaj.org/toc/2045-2322Abstract 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.Daiki SatoToshihiro TakamatsuMasakazu UmezawaYuichi KitagawaKosuke MaedaNaoki HosokawaKyohei OkuboMasao KamimuraTomohiro KadotaTetsuo AkimotoTakahiro KinoshitaTomonori YanoTakeshi KuwataHiroaki IkematsuHiroshi TakemuraHideo YokotaKohei SogaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020) |
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Medicine R Science Q Daiki Sato Toshihiro Takamatsu Masakazu Umezawa Yuichi Kitagawa Kosuke Maeda Naoki Hosokawa Kyohei Okubo Masao Kamimura Tomohiro Kadota Tetsuo Akimoto Takahiro Kinoshita Tomonori Yano Takeshi Kuwata Hiroaki Ikematsu Hiroshi Takemura Hideo Yokota Kohei Soga Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging |
| description |
Abstract 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. |
| format |
article |
| author |
Daiki Sato Toshihiro Takamatsu Masakazu Umezawa Yuichi Kitagawa Kosuke Maeda Naoki Hosokawa Kyohei Okubo Masao Kamimura Tomohiro Kadota Tetsuo Akimoto Takahiro Kinoshita Tomonori Yano Takeshi Kuwata Hiroaki Ikematsu Hiroshi Takemura Hideo Yokota Kohei Soga |
| author_facet |
Daiki Sato Toshihiro Takamatsu Masakazu Umezawa Yuichi Kitagawa Kosuke Maeda Naoki Hosokawa Kyohei Okubo Masao Kamimura Tomohiro Kadota Tetsuo Akimoto Takahiro Kinoshita Tomonori Yano Takeshi Kuwata Hiroaki Ikematsu Hiroshi Takemura Hideo Yokota Kohei Soga |
| author_sort |
Daiki Sato |
| title |
Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging |
| title_short |
Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging |
| title_full |
Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging |
| title_fullStr |
Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging |
| title_full_unstemmed |
Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging |
| title_sort |
distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/5d643fe6f2454a899dfcc37d47688e92 |
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