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Endoscopy 2023; 55(02): 140-149
DOI: 10.1055/a-1873-7920
Original article

Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses

Takamichi Kuwahara 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Kazuo Hara 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Nobumasa Mizuno
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Shin Haba 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Nozomi Okuno 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Yasuhiro Kuraishi
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Daiki Fumihara
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Takafumi Yanaidani
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Sho Ishikawa
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Tsukasa Yasuda
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Masanori Yamada 
1   Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
,
Sachiyo Onishi
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Keisaku Yamada
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Tsutomu Tanaka
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Masahiro Tajika 
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Yasumasa Niwa
2   Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
,
Rui Yamaguchi
3   Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Japan
4   Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Japan
,
Yasuhiro Shimizu
5   Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
› InstitutsangabenGefördert durch: Japan Society for the Promotion of Science JP 21K15938
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Abstract

Background There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study aimed to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic mass (pancreatic ductal adenocarcinoma [PDAC], pancreatic adenosquamous carcinoma [PASC], acinar cell carcinoma [ACC], metastatic pancreatic tumor [MPT], neuroendocrine carcinoma [NEC], neuroendocrine tumor [NET], solid pseudopapillary neoplasm [SPN], chronic pancreatitis, and autoimmune pancreatitis [AIP]).

Methods Patients who underwent EUS were included in this retrospective study. The included patients were divided into training, validation, and test cohorts. Using these cohorts, an AI model that can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions was developed using a deep-learning architecture and the diagnostic performance of the AI model was evaluated.

Results 22 000 images were generated from 933 patients. The area under the curve, sensitivity, specificity, and accuracy (95 %CI) of the AI model for the diagnosis of pancreatic carcinomas in the test cohort were 0.90 (0.84–0.97), 0.94 (0.88–0.98), 0.82 (0.68–0.92), and 0.91 (0.85–0.95), respectively. The per-category sensitivities (95 %CI) of each disease were PDAC 0.96 (0.90–0.99), PASC 1.00 (0.05–1.00), ACC 1.00 (0.22–1.00), MPT 0.33 (0.01–0.91), NEC 1.00 (0.22–1.00), NET 0.93 (0.66–1.00), SPN 1.00 (0.22–1.00), chronic pancreatitis 0.78 (0.52–0.94), and AIP 0.73 (0.39–0.94).

Conclusions Our developed AI model can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions, but external validation is needed.

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Publikationsverlauf

Eingereicht: 16. Januar 2022

Angenommen nach Revision: 10. Juni 2022

Accepted Manuscript online:
10. Juni 2022

Artikel online veröffentlicht:
29. September 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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