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Tovar et al. Art Int Surg 2023;3:14-26 https://dx.doi.org/10.20517/ais.2022.38 Page 20

[48]
distinguishing cancer or high-grade dysplasia from low-grade dysplasia in IPMNs , demonstrating
radiomics-based AI models [Table 3] may be developed as an alternative method of diagnosis that is
noninvasive, time efficient, and cost-effective. Similarly, Permuth et al. extracted 14 radiomic features for a
logistic regression model along with miRNA expression data and clinical factors, resulting in an AUC of
0.93 . Polk et al. built a model to predict IPMN malignancy using CT radiomics. In this multivariable
[44]
model, both venous and arterial phase scans from patients with histologically confirmed IPMNs were
utilized. All scans were separated into two cohorts, “malignant” and “benign”, for model building, with the
major image feature differences being pancreatic duct diameter, cyst wall width, and enhancing solid
component. Their model achieved an AUC of 0.93 using ICG and radiomic features . Similarly, Tobaly et
[45]
al. validated and trained logistic regression models to predict IPMN malignancy using radiomic features,
obtaining an AUC of 0.84. Further models were created to predict between the several subtypes of IPMNs,
with the best performing model discriminating between the high-grade dysplasia and invasive pancreatic
[46]
IPMNs (AUC, 0.92) .

In addition to CT and MRI imaging, AI models have been applied to endoscopic ultrasound (EUS) images
to assess IPMN malignant potential. A study published in 2019 investigated AI usefulness in diagnosing
IPMN-associated PDAC using preoperative EUS imaging. Using 3,970 images, the DL algorithm was
trained to output the probability of malignancy, performing with an AUC of 0.98 and an accuracy of 0.94.
In comparison with human diagnosis accuracy measured as 0.56 at a preoperative stage, the AI model was
[47]
more accurate .

Detection models
With a specificity of 96% and a sensitivity of 92%, endoscopic ultrasound-guided fine needle aspirations
(EUS-FNA) biopsy of solid pancreatic lesions is highly accurate in diagnosing pancreatic cancer using rapid
on-site cytopathology evaluation (ROSE) [62,63] . Nevertheless, FNA often results in the ambiguous diagnosis of
“atypical cells”. In such cases, diagnosis is difficult, and the underlying pathology can be varied, including
chronic pancreatitis and benign and malignant lesions . To shorten time and effort in detection, AI can
[64]
assist cytopathologists in diagnosing these difficult cases. Momeni-Boroujeni et al. created a multilayer
perceptron neural network to better distinguish between benign and malignant cell clusters by segmenting
and extracting the cytology features from the 277 images of benign, malignant, and atypical cases. The
model performed with an accuracy of 90.6% to categorize the images as benign or malignant when
[49]
including all three types of cases . To increase efficiency and speed of ROSE, Zhang et al. used deep
convolutional neural network models to segment stained cell clusters and distinguish malignant cells from
benign cells. Their cancer identification model performed with an AUC of 0.958 in the internal test and
[51]
0.948-0.976 in the external test and achieved a sensitivity of 0.94 , similar to that of cytopathologists and
higher than trained endoscopists .
[65]

Another area of research has been in the use of the microbiome as a potential early detection biomarker of
pancreatic cancer. Bacterial microbiomes within individuals are similar across multiple organs, including
the pancreas, duodenum, and oral cavity. Additionally, there is an observable difference in the composition
[66]
of bacterial species between those with and without pancreatic cancer . Kartal et al. used shotgun
metagenomics and 16S RNA sequencing to distinguish pancreatic cancer cases from controls. Samples were
collected from saliva, feces, pancreatic parenchyma, and pancreatic tumor in the Spanish and German
cohorts. Although certain bacterial species were found in abundance in the gut in those with PDAC, such as
Veillonella atypica, other species had reduced in number. Using the 27 species found in the fecal
microbiome, they trained a LASSO logistic regression model to distinguish those with and without PDAC
with an AUC of 0.84. Notably, no microbiome populations were associated with other clinical variables,
suggesting the unique microbiome seen in PDAC patients is due to the tumor growth and is a valid