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Page 15 Tovar et al. Art Int Surg 2023;3:14-26 https://dx.doi.org/10.20517/ais.2022.38

medicine for early detection purposes.

Keywords: Pancreatic cancer, artificial intelligence, early detection, risk prediction

INTRODUCTION
Pancreatic ductal adenocarcinoma carcinoma (PDAC) is a relatively rare disease, with approximately 62,000
[1]
people diagnosed each year in the United States . Although PDAC accounts for only 3% of all cancers, it
causes 7% of all cancer-related deaths and is projected to become the second leading cause of cancer-related
[2,3]
deaths by 2030 . Surgery, in conjunction with chemotherapy (with or without radiation therapy), is the
only curative treatment but is appropriate only for 15%-20% of patients . Indeed, the high mortality rate of
[4]
PDAC is attributed to 80%-85% of patients receiving a diagnosis at advanced stages that are not eligible for
[5]
potentially curative treatments . The idea of detecting PDAC at early stages that could be cured using
biomarkers and screening methods is an area of intense investigation [Figure 1] and well recognized for the
potential to significantly improve the currently dismal 5-year survival rate of 11% .
[14]
Reports indicate that when PDAC is detected early at a localized stage that is eligible for potentially curative
therapies, the 5-year survival rate is as high as 60 to 73% [15,16] .

A challenge in detecting PDAC early is the lack of effective screening in the general population. With an
estimated incidence of 12.9 cases per 100,000 person-years and low prevalence in the general population,
PDAC imposes constraints on traditional metrics of biomarker or model performance for early detection
and risk prediction. Key considerations of the performance of any biomarker test or model are positive
predictive value, negative predictive value, sensitivity, specificity, accuracy, and area under the receiver
operating characteristic curve (AUC). The positive predictive value of a biomarker test poses a particularly
daunting challenge for performance with PDAC. For example, while it would seem that a hypothetical
diagnostic screening test with 95% sensitivity and 95% specificity for early detection of PDAC in the general
population would be desirable, the low incidence of PDAC would lead to an extremely high number of false
positive results, giving this hypothetical test a very low positive predictive value of approximately 1.4%
[5]
[17]
(Table 1, adapted from with updated statistics from ).
Biomarker performance for PDAC screening is especially important considering the potential harms of a
definitive diagnosis with tissue. To diagnose PDAC, a biopsy of the pancreas must be done using
endoscopic ultrasound with fine needle aspiration (EUS-FNA), biopsy under computed tomography (CT)
image guidance, or tissue acquisition from pancreatectomy. The invasiveness of these procedures and their
costs remain strong considerations against general screening and any potential benefits of early detection in
the general population. Indeed, the US Preventive Services Task Force reaffirmed against screening for
PDAC in asymptomatic individuals .
[18]

To overcome the significant challenge of screening in the general population, researchers have focused
surveillance methods for high-risk populations, including patients with multiple first-degree relatives with a
history of PDAC diagnosis and high-risk germline mutations, although the frequency and modality(ies) of
surveillance of these individuals remains an open research question. Furthermore, another major clinical
conundrum is the surveillance of patients who have incidental findings of mucinous cysts such as
intraductal papillary mucinous neoplasms (IPMNs) or mucinous cystic neoplasms (MCNs) in the pancreas.
Only a small proportion of IPMNs and MCNs undergo malignant transformation, but a high proportion
are overdiagnosed and subsequently overtreated [19,20] .