@article{882cdae0adf643d0ab9fe2beaac9e5c2,
title = "Pancreatic Cancer Imaging: A New Look at an Old Problem",
abstract = "Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.",
author = "Chu, {Linda C.} and Seyoun Park and Satomi Kawamoto and Yuille, {Alan L.} and Hruban, {Ralph H.} and Fishman, {Elliot K.}",
note = "Funding Information: Linda C. Chu receives grant support from The Lustgarten Foundation and the Emerson Collective; Seyoun Park receives grant support from The Lustgarten Foundation and the Emerson Collective; Satomi Kawamoto receives grant support from The Lustgarten Foundation; Alan L. Yuille receives grant support from The Lustgarten Foundation Ralph H. Hruban receives grant support from The Lustgarten Foundation, and has potential to receive royalty payments from Thrive Earlier Detection for the GNAS invention; Elliot K. Fishman reports grant support from the Lustgarten Foundation, Siemens Healthineers, GE Healthcare, and is a co-founder of HipGraphics, Inc. Publisher Copyright: {\textcopyright} 2020 Elsevier Inc.",
year = "2021",
month = jul,
day = "1",
doi = "10.1067/j.cpradiol.2020.08.002",
language = "English (US)",
volume = "50",
pages = "540--550",
journal = "Current Problems in Diagnostic Radiology",
issn = "0363-0188",
publisher = "Mosby Inc.",
number = "4",
}