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Tovar et al. Art Int Surg 2023;3:14-26 Artificial
DOI: 10.20517/ais.2022.38
Intelligence Surgery

Review Open Access

Potential of artificial intelligence in the risk
stratification for and early detection of pancreatic

cancer

1
2
3
Daniela R. Tovar , Michael H. Rosenthal , Anirban Maitra , Eugene J. Koay 4
1
Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030,
USA.
2
Department of Radiology, Dana Farber Cancer Institute, Boston, MA 02215, USA.
3
Department of Radiology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA.
4
Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030,
USA.
Correspondence to: Prof. Eugene J. Koay, Department of Gastrointestinal Radiation Oncology, The University of Texas,
Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA. Email: ekoay@mdanderson.org
How to cite this article: Tovar DR, Rosenthal MH, Maitra A, Koay EJ. Potential of artificial intelligence in the risk stratification for
and early detection of pancreatic cancer. Art Int Surg 2023;3:14-26. https://dx.doi.org/10.20517/ais.2022.38

Received: 29 Nov 2022 First Decision: 2 Feb 2023 Revised: 7 Mar 2023 Accepted: 13 Mar 2023 Published: 20 Mar 2023
Academic Editor: Andrew A. Gumbs Copy Editor: Ke-Cui Yang Production Editor: Ke-Cui Yang

Abstract
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

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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