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

Author:

Kuwahara Takamichi1ORCID,Hara Kazuo1ORCID,Mizuno Nobumasa1,Haba Shin1ORCID,Okuno Nozomi1ORCID,Kuraishi Yasuhiro1,Fumihara Daiki1,Yanaidani Takafumi1,Ishikawa Sho1,Yasuda Tsukasa1,Yamada Masanori1ORCID,Onishi Sachiyo2,Yamada Keisaku2,Tanaka Tsutomu2,Tajika Masahiro2ORCID,Niwa Yasumasa2,Yamaguchi Rui34,Shimizu Yasuhiro5

Affiliation:

1. Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan

2. Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan

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

5. Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, Japan

Abstract

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.

Funder

Japan Society for the Promotion of Science

Publisher

Georg Thieme Verlag KG

Subject

Gastroenterology

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