Deep Learning Detection of Direct and Indirect Imaging Findings Associated with Pancreatic Cancer at Contrast-enhanced and Noncontrast CT

Takeru Yamaguchi; Keitaro Sofue; Atsuhiro Masuda; Nobuyuki Hirahara; Aya Ogasawara; Masanori Gonda; Mika Miki; Eisuke Ueshima; Shinji Yabe; Akihiro Umeno; Naoya Ebisu; Takashi Kobayashi; Arata Sakai; Utaru Tanaka; Takao Iemoto; Saori Kakuyama; Takeshi Ezaki; Takuya Ikegawa; Yuichi Hirata; Hidetaka Tsumura; Kyohei Ogisu; Hideyuki Shiomi; Seiji Fujigaki; Takashi Nakagawa; Keisuke Furumatsu; Kodai Yamanaka; Yu Sato; Koichi Fujita; Shigeto Ashina; Takao Katoh; Mizuki Takei; Y Kodama; Takamichi Murakami

doi:10.1148/radiol.253122

Key Points

The aim is to evaluate how deep learning models can identify imaging findings linked to pancreatic cancer on CT scans.
Used deep learning models to analyze abdominal CT scans for pancreatic cancer detection.
Compared the performance of these models against that of medical professionals.
Deep learning models diagnosed pancreatic cancer with performance comparable to physicians.
Particularly effective in detecting small pancreatic cancers.

Implication

Demonstrates deep learning detects pancreatic cancer imaging findings, indicating superior diagnostics compared to physicians.

Abstract

Deep learning models detected direct and indirect imaging findings associated with pancreatic cancer (PC) on abdominal CT scans and diagnosed PC with performance comparable to or better than that of physicians, particularly for small PCs.

Discussion

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Cite This Study

Yamaguchi et al. (2026) studied this question.

synapsesocial.com/papers/6a338d85630953a74978e7a7 https://doi.org/10.1148/radiol.253122

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Deep Learning Detection of Direct and Indirect Imaging Findings Associated with Pancreatic Cancer at Contrast-enhanced and Noncontrast CT

Key Points

Implication

Abstract

Discussion

Cite This Study

Deep Learning Detection of Direct and Indirect Imaging Findings Associated with Pancreatic Cancer at Contrast-enhanced and Noncontrast CT

Key Points

Implication

Abstract

Discussion

Cite This Study

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