A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy
Abstract Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has become the mainstay of preoperative pathological diagnosis. However, on EUS-FNB specimens, accurate histopathological evaluation is difficul...
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2021
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oai:doaj.org-article:8efa37602ae04180b4b65201336e8c572021-12-02T18:27:46ZA deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy10.1038/s41598-021-87748-02045-2322https://doaj.org/article/8efa37602ae04180b4b65201336e8c572021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87748-0https://doaj.org/toc/2045-2322Abstract Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has become the mainstay of preoperative pathological diagnosis. However, on EUS-FNB specimens, accurate histopathological evaluation is difficult due to low specimen volume with isolated cancer cells and high contamination of blood, inflammatory and digestive tract cells. In this study, we performed annotations for training sets by expert pancreatic pathologists and trained a deep learning model to assess PDAC on EUS-FNB of the pancreas in histopathological whole-slide images. We obtained a high receiver operator curve area under the curve of 0.984, accuracy of 0.9417, sensitivity of 0.9302 and specificity of 0.9706. Our model was able to accurately detect difficult cases of isolated and low volume cancer cells. If adopted as a supportive system in routine diagnosis of pancreatic EUS-FNB specimens, our model has the potential to aid pathologists diagnose difficult cases.Yoshiki NaitoMasayuki TsunekiNoriyoshi FukushimaYutaka KogaMichiyo HigashiKenji NotoharaShinichi AishimaNobuyuki OhikeTakuma TajiriHiroshi YamaguchiYuki FukumuraMotohiro KojimaKenichi HirabayashiYoshihiro HamadaTomoko NoroseKeita KaiYuko OmoriAoi SukedaHirotsugu NoguchiKaori UchinoJunya ItakuraYoshinobu OkabeYuichi YamadaJun AkibaFahdi KanavatiYoshinao OdaToru FurukawaHirohisa YanoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
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Medicine R Science Q Yoshiki Naito Masayuki Tsuneki Noriyoshi Fukushima Yutaka Koga Michiyo Higashi Kenji Notohara Shinichi Aishima Nobuyuki Ohike Takuma Tajiri Hiroshi Yamaguchi Yuki Fukumura Motohiro Kojima Kenichi Hirabayashi Yoshihiro Hamada Tomoko Norose Keita Kai Yuko Omori Aoi Sukeda Hirotsugu Noguchi Kaori Uchino Junya Itakura Yoshinobu Okabe Yuichi Yamada Jun Akiba Fahdi Kanavati Yoshinao Oda Toru Furukawa Hirohisa Yano A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy |
| description |
Abstract Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has become the mainstay of preoperative pathological diagnosis. However, on EUS-FNB specimens, accurate histopathological evaluation is difficult due to low specimen volume with isolated cancer cells and high contamination of blood, inflammatory and digestive tract cells. In this study, we performed annotations for training sets by expert pancreatic pathologists and trained a deep learning model to assess PDAC on EUS-FNB of the pancreas in histopathological whole-slide images. We obtained a high receiver operator curve area under the curve of 0.984, accuracy of 0.9417, sensitivity of 0.9302 and specificity of 0.9706. Our model was able to accurately detect difficult cases of isolated and low volume cancer cells. If adopted as a supportive system in routine diagnosis of pancreatic EUS-FNB specimens, our model has the potential to aid pathologists diagnose difficult cases. |
| format |
article |
| author |
Yoshiki Naito Masayuki Tsuneki Noriyoshi Fukushima Yutaka Koga Michiyo Higashi Kenji Notohara Shinichi Aishima Nobuyuki Ohike Takuma Tajiri Hiroshi Yamaguchi Yuki Fukumura Motohiro Kojima Kenichi Hirabayashi Yoshihiro Hamada Tomoko Norose Keita Kai Yuko Omori Aoi Sukeda Hirotsugu Noguchi Kaori Uchino Junya Itakura Yoshinobu Okabe Yuichi Yamada Jun Akiba Fahdi Kanavati Yoshinao Oda Toru Furukawa Hirohisa Yano |
| author_facet |
Yoshiki Naito Masayuki Tsuneki Noriyoshi Fukushima Yutaka Koga Michiyo Higashi Kenji Notohara Shinichi Aishima Nobuyuki Ohike Takuma Tajiri Hiroshi Yamaguchi Yuki Fukumura Motohiro Kojima Kenichi Hirabayashi Yoshihiro Hamada Tomoko Norose Keita Kai Yuko Omori Aoi Sukeda Hirotsugu Noguchi Kaori Uchino Junya Itakura Yoshinobu Okabe Yuichi Yamada Jun Akiba Fahdi Kanavati Yoshinao Oda Toru Furukawa Hirohisa Yano |
| author_sort |
Yoshiki Naito |
| title |
A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy |
| title_short |
A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy |
| title_full |
A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy |
| title_fullStr |
A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy |
| title_full_unstemmed |
A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy |
| title_sort |
deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/8efa37602ae04180b4b65201336e8c57 |
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