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The FELIX Project: Deep Networks To Detect Pancreatic Neoplasms

Xia, Y.; Yu, Q.; Chu, L.; Kawamoto, S.; Park, S.; Liu, F.; Chen, J.; Zhu, Z.; Li, B.; Zhou, Z.; Lu, Y.; Wang, Y.; Shen, W.; Xie, L.; Zhou, Y.; Wolfgang, C.; Javed, A.; Fouladi, D. F.; Shayesteh, S.; Graves, J.; Blanco, A.; Zinreich, E. S.; Kinny-Koster, B.; Kinzler, K.; Hruban, R. H.; Vogelstein, B.; Yuille, A. L.; Fishman, E. K.

2022-09-25 radiology and imaging
10.1101/2022.09.24.22280071 medRxiv
Show abstract

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.

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