| Autor: |
Yingda Xia, Qihang Yu, Linda Chu, Satomi Kawamoto, Seyoun Park, Fengze Liu, Jieneng Chen, Zhuotun Zhu, Bowen Li, Zongwei Zhou, Yongyi Lu, Yan Wang, Wei Shen, Lingxi Xie, Yuyin Zhou, Christopher Wolfgang, Ammar Javed, Daniel Fadaei Fouladi, Shahab Shayesteh, Jefferson Graves, Alejandra Blanco, Eva S. Zinreich, Miriam Klauss, Philipp Mayer, Benedict Kinny-Köster, Kenneth Kinzler, Ralph H. Hruban, Bert Vogelstein, Alan L. Yuille, Elliot K. Fishman |
| Rok vydání: |
2022 |
| DOI: |
10.1101/2022.09.24.22280071 |
| Popis: |
Tens of millions of abdominal images are obtained with computed tomography (CT) in the U.S. each year but pancreatic cancers are sometimes not initially detected in these images. We here describe a suite of algorithms (named FELIX) that can recognize pancreatic lesions from CT images without human input. Using FELIX,>95% of patients with pancreatic ductal adenocarcinomas were detected at a specificity of>95% in patients without pancreatic disease. FELIX may be able to assist radiologists in identifying pancreatic cancers earlier, when surgery and other treatments offer more hope for long-term survival. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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