TY - JOUR
T1 - Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer
AU - Tovar, Daniela R.
AU - Rosenthal, Michael H.
AU - Maitra, Anirban
AU - Koay, Eugene J.
N1 - Funding Information:
Maitra A and Koay EJ were supported with grants from NIH (1U01DK126365, 5U01CA200468, 3U01CA196403, 5R01CA218004). Koay EJ was also partially supported by DOD (W81XWH-21-1-0709) and NIH (U54CA210181, U54CA143837, U01CA214263, P50CA221707, R01CA221971, P30CA016672). Maitra A and Koay EJ would also like to gratefully acknowledge the generous support from Jennifer and Wil vanLoh, KWS Foundation, and the Michael C Linn Family Foundation. Rosenthal MH received funding from the Hale Family Center for Pancreatic Cancer Research at Dana-Farber Cancer Institute, NIH (NIH/NCI U01CA210171 and U01CA200468), Lustgarten Foundation, and Stand Up to Cancer for related work. Maitra A is supported by the MD Anderson Pancreatic Cancer Moon Shot Program, the Sheikh Khalifa Bin Zayed Al-Nahyan Foundation. Maitra A receives royalties for a pancreatic cancer biomarker test from Cosmos Wisdom Biotechnology, and this financial relationship is managed and monitored by the UTMDACC Conflict of Interest Committee. Maitra A is also listed as an inventor on a patent that has been licensed by Johns Hopkins University to ThriveEarlier Detection. Maitra A serves as a consultant for Freenome and Tezcat Biotechnology.
Publisher Copyright:
© The Author(s) 2023.
PY - 2023
Y1 - 2023
N2 - Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.
AB - Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.
KW - artificial intelligence
KW - early detection
KW - Pancreatic cancer
KW - risk prediction
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UR - http://www.scopus.com/inward/citedby.url?scp=85174919403&partnerID=8YFLogxK
U2 - 10.20517/ais.2022.38
DO - 10.20517/ais.2022.38
M3 - Review article
AN - SCOPUS:85174919403
SN - 2771-0408
VL - 3
SP - 14
EP - 26
JO - Artificial Intelligence Surgery
JF - Artificial Intelligence Surgery
IS - 1
ER -