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Page 17 Tovar et al. Art Int Surg 2023;3:14-26 https://dx.doi.org/10.20517/ais.2022.38

Broadly speaking, AI techniques include machine learning (ML), convolutional neural networks (CNN),
and deep learning (DL), and these methods work by interpreting and analyzing big data [Table 2]. For
PDAC early detection, AI has illustrated promise in several domains. One of the principal points of interest
involves finding predictors using health records to create predictive models to identify those with a higher
risk of developing PDAC. Another focus of AI research investigates the ways that models detect the cancer
at a localized, potentially curable stage using biomarkers or imaging.

This expert literature review highlights recent developments of models created to stratify high-risk
individuals (HRI) using patient data, including new-onset diabetes mellitus and hyperglycemia, and
radiomics, which can identify image features and patient anatomy that are predictive of future malignancy.
Also, this review summarizes studies utilizing radiomics for the classification of high-grade IPMNs and
tumor detection using CT images, and the classification of cell clusters and microbiota for early detection.
Furthermore, the ethical and privacy concerns researchers must consider when training models using
patient data, as well as the steps needed to develop a transparent and ethical model that can be clinically
adopted, are discussed.

RISK PREDICTION MODELS
One strategy to reduce the unacceptably high false positive results that stem from the low prevalence of
PDAC in the general population is to focus on higher-risk populations. The “sequential sieve” model has
been widely adopted for PDAC to enrich screening populations . In this model, a first sieve is used to filter
[33]
the general population to enrich high-risk individuals based on a common phenotype, while a second sieve
then filters this enriched cohort to find a blood-based or an imaging-based marker among these high-risk
[33]
individuals predicted to develop PDAC . One of the risk factors is familial risk and germline mutations,
[34]
representing about 10% of PDAC patients . Other risk factors include cystic lesions and new-onset
[35]
diabetes . To better understand the natural history of PDAC in the setting of new-onset diabetes (NOD), a
prospective trial is recruiting participants to investigate the incident rate of PDAC in those with new-onset
hyperglycemia and diabetes, wherein patients considered to be at the highest risk of harboring an occult
[36]
(asymptomatic) PDAC will undergo abdominal imaging . Another example of a large cohort of patients
who are being followed for incident PDAC and treatment outcomes includes the Florida Pancreas
Collaborative, which has created biorepositories, including blood, CT scans, and tissue samples from 15
institutions in Florida to address PDAC disparities . These cohort-building efforts, aligned with risk
[37]
prediction models [Table 3], are aimed at improving risk assessment, as well as biomarker discovery and
validation.

Models using health records to assess risk
About 50% of PDAC patients are diagnosed with NOD and 85% are hyperglycemic, both believed to be
induced by the tumor . Importantly, hyperglycemia can start manifesting on laboratory testing several
[54]
months to 2 years prior to the clinical appearance of “classic” PDAC-associated symptoms (jaundice, weight
loss) . In 2017, Boursi et al. used health records to create a model to predict PDAC-induced diabetes
[55]
mellitus amongst a cohort of all new-onset diabetes mellitus patients. Using a study cohort of approximately
180,000 patients with new-onset diabetes mellitus, the trained model analyzed predictors, such as age,
smoking, body mass index, as well as blood serum levels within three years of the diabetes diagnosis for
stratification (AUC, 0.82) . For further stratification, Boursi et al. created a model using a study cohort of
[38]
138,232 patients with impaired fasting glucose, or prediabetes. The model analyzed those with impaired
fasting glucose (IFG), including predictors such as age, body mass index (BMI), and blood serum levels, to
train the model to identify high-risk patients (AUC, 0.71) .
[39]