Review Open Access
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Dec 8, 2023; 4(3): 48-63
Published online Dec 8, 2023. doi: 10.35712/aig.v4.i3.48
Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis
Palash Rawlani, Nalini Kanta Ghosh, Ashok Kumar, Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
ORCID number: Palash Rawlani (0009-0008-4478-2020); Nalini Kanta Ghosh (0000-0003-1213-0235); Ashok Kumar (0000-0003-3959-075X).
Author contributions: Kumar A designed the concept, corrected, and finalized the manuscript; Ghosh NK and Palash R wrote the manuscript and reviewed the literature; All authors have read and approved the final manuscript.
Conflict-of-interest statement: Dr. Kumar has nothing to disclose.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Ashok Kumar, BSc, FASCRS, FRCS, FRCS (Ed), FRCS (Hon), MBBS, MCh, MS, Professor, Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow 226014, Uttar Pradesh, India.
Received: July 27, 2023
Peer-review started: July 27, 2023
First decision: August 31, 2023
Revised: September 11, 2023
Accepted: October 8, 2023
Article in press: October 8, 2023
Published online: December 8, 2023


Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

Key Words: Artificial intelligence, Indeterminate pancreatic lesion, Imaging, Biomarkers, Diagnosis

Core Tip: Surgical management of a pancreatic head lesion usually requires pancreaticoduodenectomy, which is associated with significant morbidity and mortality. For a benign lesion it is unacceptable. The investigation modalities i.e. computed tomography, magnetic resonance imaging, endoscopic ultrasound, positron emission tomography, and biochemical markers are available today to distinguish benign from malignant lesions and have their limitations (human judgmental errors). The application of artificial intelligence (AI) algorithms can minimize human errors and improve the sensitivity and specificity of diagnostic yield. The AI can help with great precision in differentiating benign from malignant lesions, affecting the management strategy and minimizing post-operative complications.


The concept of a machine that can think like a human being was proposed by Mr. Alan Turing in the year 1950 in his book entitled “Computing Machinery and Intelligence” and later, the term “artificial intelligence (AI)” was coined by John McCarthy[1,2]. The applicability of AI ranges from simple tasks to more complex tasks mimicking a human brain. There are six major sub-fields of AI: machine learning (ML), neural network, deep learning (DL), natural language processing (NLP), cognitive computing, and computer vision. ML can learn from data, recognize typical patterns, and make decisions with little or no human interference. A neural network is the field of AI that is inspired by the human brain, where a set of algorithms is used to derive a correlation. Most of the AI models in the medical field use ML and neural networks. NLP is a method where textual data has been used to search, analyze, and comprehend complex information. Computer vision understands visual inputs (radiological or pathological images, surgical videos) and derives desired information. There are many modifications of conventional sub-fields of AI which have been in use. The twentieth century has seen that AI has become an essential part of day-to-day life, including health tracking devices[3], automobiles[4], banking and finances (robo-traders)[5], surveillance, social media, entertainment, education, space exploration, and disaster management, etc[6,7].

AI has been used in various fields of medicine including online appointments and hospital check-ins, medical records digitalization, follow-up, drug dosage reminders, adverse effect warnings, etc. Moreover, its application in the field of oncology is paramount. AI can be useful in cancer detection, screening, diagnosis, classification, prognostication, new drug discovery, etc[8-11]. It has played its role in differentiating various indeterminate lesions in the thyroid gland[12,13], breast[14], lungs[15,16], liver[17], adrenal[18,19], kidneys[20], and indeterminate biliary strictures[21] (Table 1). Various authors have studied the role of AI algorithms to identify pancreatic lesions from imaging modalities computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasonography (EUS), positron emission tomography (PET) scan, etc and thus can differentiate malignant indeterminate pancreatic lesions (IPLs) from benign ones for better management at an early stage.

Table 1 Studies on differentiation of indeterminate lesions using artificial intelligence.
Number of patients
Organ of interest
Sub-type of AI
1Ippolito et al[12], 2004453Thyroid nodule (benign vs malignant)ANNRefinement of risk stratification of FNAB and clinical data
2Daniels et al[13], 2020121Indeterminant thyroid noduleMLML and ultrasonography can identify genetically high risk lesions
3Becker et al[14], 2018632Breast lesion (benign vs malignant)Generic DLSAids diagnosing cancer on breast ultrasound images with an accuracy comparable to radiologists
4Scott et al[15], 2019125Lung GGO (benign vs malignant)ANNImprove diagnostic ability using CT scan, PET, and clinical data
5Guo et al[16], 202220Indeterminant small lung lesionsDNNDNN based method may detect small lesions < 10 mm at an effective radiation dose < 0.1 mSv.
6Yasaka et al[17], 2018460Liver mass (HCC vs others)CNNHigh diagnostic performance in differentiation of liver masses using dynamic CT
7Moawad et al[18], 202140Adrenal incidentaloma (benign vs malignant)MLMachine learning and CT texture analysis can differentiate between benign and malignant indeterminate adrenal tumors
8Stanzione et al[19], 202155Indeterminant solid adrenal lesionsMLMRI handcrafted radiomics and ML can be used to different adrenal incidentalomas
9Massa'a et al[20], 2022160Indeterminant solid renal mass (benign vs malignant)MLMRI-based radiomics and ML can be useful in differentiation
10Saraiva et al[21], 202285Indeterminant biliary stricturesCNNCNN can accurately differentiate benign strictures from malignant ones

IPLs are those detected by imaging techniques performed for non-specific abdominal complaints or detected incidentally, otherwise known as pancreatic incidentaloma. With the increase in imaging modalities, the detection of such IPLs has increased[22]. These incidentalomas are mostly detected in other organs, i.e. the thyroid gland, pituitary gland, kidney, lungs, adrenal gland, etc. Though, the incidence of indeterminate lesions is less in the pancreas, however, most of them are malignant compared to other sites[23]. Identification of such lesions creates confusion in clinicians and anxiety among the patients. Moreover, early diagnosis of malignancy can provide reasonably early management and better overall outcomes. Therefore, it is necessary to diagnose such lesions for better patient management.

The overall prevalence of such lesions was reported to be 0.01%–0.6% in 2009, which may be less compared to its true incidence[24]. A review of a series of pancreatic resections shows an asymptomatic neoplastic lesion to be 6%-23% (24% to 50% of them are malignant, and 24% to 47% are considered potentially malignant or pre-malignant)[25,26]. A recently published Leopard-2 trial comparing laparoscopic and open pancreaticoduodenectomy has shown the incidence of benign or pre-malignant lesions to be 12%[27]. Frequently, cystic lesions of the pancreas are detected on MRI and their incidence is up to 20%[28] and recent series shows the incidence to be 49% in the general population[29]. The majority of cystic lesions are benign, however, approximately, 3% are malignant or potentially malignant[30].

The etiology of such lesions is diverse, benign adenoma to adenocarcinoma, borderline malignant tumors, mesenchymal tumors, neuroendocrine tumors, cysts, congenital changes, metastatic lesions, inflammatory masses etc[23]. These lesions may be broadly divided into benign, pre-malignant, or malignant lesions[24]. Figure 1 shows different pathologies of IPLs[31].

Figure 1
Figure 1 Pathology of different indeterminate pancreatic lesions.

There is a considerable overlap of imaging features of different benign and malignant pancreatic lesions. Cystic degeneration of solid tumors may masquerade as cystic lesions. Various modalities (ultrasonography, contrast-enhanced CT, MRI, EUS, PET, cytopathology, histopathology, and tumor markers) have been used to differentiate the possible etiology, however, there are limitations of each modality intrinsic to the investigation itself or on the operator. Recently, AI has been used to distinguish various indeterminate lesions in the breast, lungs, adrenal gland, kidney, etc. Thus, the use of AI in association with conventional imaging or diagnostic modalities can improve their overall diagnostic yield and therefore, more precise diagnosis and patient care.

This paper reviews the current status of AI in the differentiation of various IPLs and its future implications.


All the relevant articles were searched from PubMed and Google Scholar using the keywords, i.e. “artificial intelligence” AND “pancreatic lesions” OR “cystic lesions”, OR “CT”, OR “MRI”, OR “EUS”, OR “PET” OR “pathology”, OR “biomarkers” between 2005 and 2023, and only full articles were studied. Articles discussing the differentiation of different types of pancreatic lesions were included and screened by all authors. Abstracts and conference presentations were excluded. Studies discussing the differentiation of any pancreatic lesion (benign vs. malignant) were included in relevant sections for discussion. The study flow chart is shown in Figure 2.

Figure 2
Figure 2 Study flow chart.
Role of clinical parameters and AI on the identification of IPLs

Pancreatic cancer is one of the leading causes of cancer-related death worldwide, thus early diagnosis is crucial for better management. Often, patients are asymptomatic to start with, so presentation is delayed leading to advanced disease at diagnosis. This delay in diagnosis can be minimized by the identification of high-risk groups and the introduction of targeted screening of high-risk populations. Any lesion identified in these patient groups can be subjected to further evaluation using an AI augmented imaging system (CT, MRI, PET, EUS), which will be discussed later. The proposed schema of patient evaluation and management is presented in Figure 3.

Figure 3
Figure 3 Schematic presentation of diagnosis of indeterminate pancreatic lesion using artificial intelligence. AI: Artificial intelligence; CT: Computed tomography; EUS: Endoscopic ultrasonography; MRI: Magnetic resonance imaging; PET: Positron emission tomography; SOL: Space occupying lesion.

Several clinical parameters can be used to predict the future incidence of pancreatic cancer including, symptoms, hereditary factors (BRCA1, BRCA2, PALB2, Hereditary pancreatitis, and Peutz-Jeghers Syndrome), pre-existing clinical conditions (new-onset diabetes mellitus), lifestyle (smoking, alcohol, obesity, nutrient-poor diet), and demographic factors. Elevation of CA 19-9, CEA, and recently developed CEMIP (cell migration-inducing hyaluronan binding protein) can be considered as an early indicator of pancreatic cancer[32-34]. None of these parameters can confirm pancreatic cancer, however, a combined assessment can suggest a possible pancreatic cancer leading to screening of high-risk populations. In a retrospective study from Kaiser Permanente Southern California, an algorithm for risk stratification for pancreatic cancer was generated using imaging (CT/magnetic resonance) and clinical factors[35]. In this study, imaging features used were pancreatic duct dilatation as a predictor of malignancy and other features such as atrophy, calcification, pancreatic cyst, and irregular pancreatic duct. Multi-state prediction model showed a discriminatory index (c-index: 0.825–0.833) between normal individuals and individuals with pancreatic cancer. A study at the Biomedical Imaging Research Institute of Cedars Sinai Medical Center, Los Angeles used ML and CT-based radiomic features as an indicator of pancreatic ductal adenocarcinoma (PDAC)[36]. The scans were obtained in non-pancreatic cancer patients for different purposes, who later developed pancreatic cancer after 6 mo to 3 years. The AI model had an accuracy of 86% in the prediction of PDAC. As CT scans were performed frequently for different purposes, such AI models can identify patients having potential risk for future pancreatic malignancy.

Muhammad et al[37], Placido et al[38], and Chen et al[39] used demographic and clinical parameters with artificial neural networks (ANNs) algorithms to predict pancreatic cancer. In the validation arm, the area under the curve (AUC) was 0.85, and the sensitivity and specificity of diagnosis were 80.7%. Malhotra et al[40] used ML principles to identify symptoms to predict pancreatic cancer. Their algorithm could detect 41.3% of patients with pancreatic cancer < 60 years of age, 20 mo earlier than diagnosis (AUC: 0.66), and 43.2% of patients with pancreatic cancer > 60 years of age, 17 mo earlier than diagnosis (AUC: 0.61). Appelbaum et al[41] used neural network algorithms to identify high-risk groups 1 year in advance. Thus, these AI techniques not only help to detect pancreatic cancer but also, earlier than conventional imaging.

Role of AI on CT scan imaging on detection of pancreatic lesions

If a mass lesion is detected in the pancreas, the possibility of neoplasm is kept as a differential diagnosis. The most common (85%–95%) among the lesions is pancreatic ductal adenocarcinoma (PDAC) and it has a poor prognosis[42,43]. Ill-defined hypovascular mass is the characteristic of PDAC in contrast-enhanced imaging[44]. Atypical imaging of a solid mass may harbor a malignancy, however, its mimic, an inflammatory mass, can have a better prognosis than PDAC, and management of both these conditions is different.

Among all the imaging modalities, CT is most commonly favored for the investigation of a pancreatic lesion, as it is widely available, quick to acquire, has a high spatial resolution, assesses relationship to vascular structures, and determines surgical planning. Recent advances in CT imaging in the form of multiplanar reformatted images, and three-dimensional (3D) techniques have improved sensitivity by up to 96% in tumor identification[45,46]. However, small tumors or tumors with atypical features may not be visible on CT scans or subtle changes may not be appreciable to the human eye and prone to errors. These limitations of conventional CT imaging can be overcome by the use of AI algorithms.

Differentiation of PDAC

Among all malignancies, PDAC has the worst overall survival[47]. It is because patients present late at an advanced stage due to late detection of asymptomatic subtle pancreatic lesions on imaging[40]. Zhu et al[48] and Liu et al[49] have used DL to detect pancreatic cancer and in the study by Liu et al[49], malignancy could be detected in just 3 s with an AUC of 0.96. Chu et al[50] could diagnose PDAC with an AUC of 99.9% using ML algorithms.

Differentiation of cystic lesions

With the increase in the frequency of cross-sectional imaging, the detection of cystic lesions of the pancreas has increased and it is aptly called “technopathies”. Management of these cystic lesions requires classification of the type of lesion and the risk of malignancy which is sub-optimal with present imaging modalities[51,52]. AI has been used to differentiate the types of cystic lesions into, intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasia (SCN), solid pseudopapillary neoplasia, etc[53,54]. Dmitriev et al[53] used the convolutional neural network (CNN) model (contrast-enhanced CT and clinical data) to differentiate the types of cystic lesions with an accuracy of 84% which is better than radiologists which has an accuracy of less than 70%[53,55]. However, Li et al[54] used only CT images and AI (DL) to differentiate the cystic lesions with an accuracy of 73% compared to radiologists in their study which had an accuracy of only 48%. Differentiation of SCN from other cystic lesions is important as they have a rare chance of being malignant, thus, Wei et al[56] used an ML-based algorithm to distinguish SCN from others based on CT images. Yang et al[57] and Chen et al[58] have used AI algorithms to distinguish SCN from MCN. Chakraborty et al[59] and Polk et al[60] used the RF model to differentiate low-grade IPMN from high-grade IPMN which has management implications. Table 2 summarizes studies on the uses of AI along with CT images in the differentiation of pancreatic lesions.

Table 2 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on computed tomography images.
Number of patients
Primary objective
Sub-type of AI used
1Qureshi et al[36], 2022108Identification of PDACMLAccuracy: 86%
2Ebrahimian et al[121], 2022103Differentiation of benign vs malignant pancreatic lesionsRFAUC: 0.94
3Chakraborty et al[59], 2018103High risk vs low risk IPMNRF, SVMAUC: 0.81
4Polk et al[60], 202029High risk vs low risk IPMNLRAUC: 0.90
5Ikeda et al[122], 199771PDAC vs pancreatitisNNAUC: 0.916
6Chen et al[58], 2021100SCN vs MCNLASSO and RFE_Linear SVCAUC: 0.932
7Yang et al[57], 201953SCN vs MCNLASSOAUC: 0.66
8Yang et al[123], 202263SCN vs MCNMMRF-ResNetAUC: 0.98
9Ren et al[124], 2020112PDAC vs pancreatic adenosquamous carcinomaRFAUC: 0.98
10Xie et al[125], 2021226MCN vs ASCNRFAUC: 0.734
11Ziegelmayer et al[126], 202086AIP vs PDACCNN, MLAUC: 0.90
12Li et al[62], 202297Focal-type AIP vs PDACLASSO regressionAUC: 0.97
13Gao et al[127], 2021170MCN vs SCNmRMR + LASSOAUC: 0.91
14Dmitriev et al[53], 2017134Classification of pancreatic cystRF, CNNAccuracy: 83.6%
15Li et al[54], 2019206Classification of pancreatic cystsDNN (Dense-Net)Accuracy: 72.8%
16Wei et al[56], 2019260SCN vs other cystic neoplasmsMLAUC: 0.767
Role of AI on MRI on the detection of pancreatic lesions

MRI is favored over CT scan due to superior soft tissue delineation and it also helps to detect small lesions, assessment of the vascular relationship, and relationship to the pancreatic duct, lymph node, or distant metastasis[43,61]. Detection of iso-attenuating pancreatic lesions on CT scan is challenging which is observed in approximately 10% of patients. In these situations, indirect evidence of malignancy is used for diagnosis, i.e. convex pancreatic contour, double duct sign, vascular involvement, mass effect, etc[42]. However, MRI can be helpful to diagnose such lesions. Recently, the use of AI algorithms has improved the diagnostic ability of MRI. Li et al[62] and Chen et al[63] used AI algorithms for the identification of PDAC on different phases of MRI (Table 3).

Table 3 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on magnetic resonance images.
Number of patients
Primary objective
Sub-type of AI used
1Li et al[62], 2022267PDAC detectionUDA + meta learning + GCNDSC (62.08%, T1), (61.35%, T2), (61.88%, DWI), (60.43%, AP)
2Chen et al[63], 202273PDAC detectionSpiral-ResUNetDSC: 65.60%, Jaccard index: 49.64%
3Liang Y et al[128], 202056PDAC detectionCNNDSC: 71%
5Cui et al[129], 2021202Grading-BD IPMNLASSOAUC: 0.903
6Corral et al[67], 2019139Classification of IPMNCNNAUC: 0.783
7Cheng et al[68], 202260Malignant IPMNLR, SVMMRI + SVM: AUC: 0.940, CT + SVM: AUC: 0.864
8Hussein et al[130], 2019171Classification of IPMNSVM, RF, 3D, CNNAccuracy 84.22%

Management of cystic lesions depends upon the precise characterization, which indicates its clinical behavior[64]. However, overlapping imaging features make differentiation challenging[64]. The role of imaging is to differentiate benign from malignant cystic neoplasms. MRI uses T2 images to identify ductal communication and post-contrast images to characterize the lesion. It is limited in the detection of calcifications which is better appreciated on a CT image. MRI can differentiate benign from malignant lesions with an accuracy of 73% to 81% compared to a CT scan which has an accuracy of 75% to 78%[52,65,66].

The use of AI has enabled MRI to detect high-grade dysplasia or malignancy in IPMN with a sensitivity and specificity of 75% and 78%, respectively[67]. Corral et al[67] used 3D CNN to classify IPMN into different types with an accuracy of 58%. Interestingly, Cheng et al[68] compared radiomics features of CT and MRI using AL algorithms [LASSO, LR, support vector machine (SVM)] and found out that, the MRI-based model(AUC: 0.940) had better diagnostic ability than the CT based model(AUC: 0.864). Studies on the use of AI with MRI to detect the type of cystic or solid pancreatic lesions are presented in Table 3.

Role of AI on EUS in the detection of pancreatic lesions

EUS uses a high-frequency transducer at the tip of an endoscope. It helps to obtain high-resolution images of the pancreas through the esophagus, stomach, or duodenum. Various modalities of EUS including contrast-enhanced EUS, EUS-guided fine needle aspiration (FNA), and EUS elastography have been used for the evaluation of pancreatic cancer, detection of small lesions, differentiation of solid from cystic tumors, and assessment of resectability[69]. Most importantly, it helps to obtain tissue for cytopathology or histopathology[70,71]. The main drawback is operator dependency, which may reduce the diagnostic yield[72,73]. AI algorithms have been used in association with EUS to detect pancreatic cancers and to differentiate from other lesions (Table 4). Mass-forming chronic pancreatitis may masquerade as pancreatic malignancy, EUS based AI algorithms can be used to distinguish pancreatic cancer from chronic pancreatitis.

Table 4 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on endoscopic ultrasonography images.
Number of patients
Primary outcome
Sub type of AI used
1Zhu et al[78], 2013262PDAC vs CPSVMAccuracy: 94.2%
2Zhu et al[131], 2015100AIP vs CPSVMAccuracy: 89.3%
3Zhang et al[74], 2010216Normal pancreas vs PDACSVMAccuracy: 97.98%
4Ozkan et al[76], 2016332Recognition of pancreatic cancer amongst various age groupANNAccuracy: Average: 87.5% (all ages), Min: 88.46% (40-60 yr), Max: 92% (< 40 yr)
5Kuwahara et al[83], 201950Benign vs malignant IPMNCNNAccuracy: 94%
6Das et al[75], 200856PDAC vs normal pancreas vs CPANN AUC: 0.93
7Săftoiu et al[80], 200868Benign vs malignant pancreatic lesionANNAccuracy: 89.7%
8Tonozuka et al[132], 2021139PDAC vs CPCNNAUC: 0.94
9Marya et al[133], 2021583PDAC vs benign causes of pancreatic SOLCNNAUC: 0.976
10.Xu et al[134], 2013Systemic Analysis of 6 studiesBenign vs malignant pancreatic lesion-AUC: 0.962

Authors have used ML algorithms to differentiate normal pancreatic tissue from PDAC with more than 93% accuracy[74-76]. Two studies have used AI to distinguish chronic pancreatitis from PDAC on EUS images with an accuracy of more than 80%[77,78]. Săftoiu et al[79] demonstrated better diagnostic ability of contrast-enhanced EUS (94.6% and a specificity of 94.4%) compared to EUS-FNA (87.5% and 92.7%) in differentiating CP from PDAC using AI.

Recently, EUS elastography has been used to diagnose focal pancreatic lesions. Using ANN, it can differentiate benign from malignant lesions with an accuracy of 95%[80]. In another multicenter prospective study using ANN, they demonstrated that EUS elastography (sensitivity (87.6%) and specificity (82.9%)) had better diagnostic ability than two experienced endoscopists combined (sensitivity 80.0%, specificity 50.0%)[81]. Udriştoiu et al[82] used ML principles to distinguish focal pancreatitis from pancreatic mass (neuroendocrine tumor or PDAC) with an accuracy of 98.26%. Differentiation of benign IPMN from malignant IPMN has management implications, Kuwahara et al[83] studied to detect malignant IPMN using CNN (ResNet-50).

Role of AI on PET imaging on the detection of pancreatic lesions

PET is a functional imaging technique used for staging malignant lesions and is based on the physiological characteristics of tumor cells[84,85]. However, inflammation may mimic a malignant lesion due to high metabolic activity giving rise to false positive results, conversely, in patients with hyperglycemia, it can give a false negative result[86,87]. PET CT is also useful in the assessment of tumor response to therapy[43]. Li et al[88] used a hybrid feedback-SVM-random forest model to detect pancreatic cancer from a normal pancreas with an accuracy of 96.47%. Liu et al[89] studied the role of dual time PET/CT and SVM model to differentiate PDAC from AIP with an AUC of 0.96 similarly, Xing et al[90] showed a diagnostic performance of 0.93 of AUC.

Role of AI in pathological examination on detection of pancreatic lesions

Often, imaging cannot achieve an accurate diagnosis, requiring a tissue diagnosis-cytology or histology[91,92]. AI can be applied to hematoxylin-eosin-stained slides for the detection of pancreatic cancer[93]. Song et al[94] used AI algorithms to segment epithelial cell nuclei on slide images and extract morphological features and could differentiate SCN from MCN and grading of PDAC[95]. The CNN was used by Kriegsmann et al[96] to localize pancreatic intra-epithelial neoplasm or PDAC in a slide. Niazi et al[97] used DL to detect neuroendocrine tumors from normal tissues on Ki-67 stained biopsy images with a 97.8% sensitivity and 88.8% specificity. Momeni-Boroujeni et al[98] could differentiate benign from malignant pathology using a K-means clustering algorithm from FNA-based slides with an accuracy of 100%. Naito et al[99] used CNN in FNB-based slides to assess PDAC with an AUC of 0.984. Cyst fluid analysis is an essential part of the diagnosis of pancreatic cystic lesions. Kurita et al[100] used a neural network to differentiate benign from malignant cysts taking into consideration biomarkers in cyst fluid, cytology and clinical parameters.

Role of AI in biomarkers on detection of pancreatic lesions

Biomarkers act as an adjunct in diagnosis, prognosis, and screening for recurrence and they can be used for early diagnosis of tumors. However, in the case of pancreatic cancer, it lacks sensitivity and specificity for routine clinical practice[91,101,102]. Liquid biopsy is one of the recent developments in oncology, developed with the intent of detecting tumor cells from blood when biopsy cannot be obtained, or to assess tumor response to therapy (surgery or chemoradiotherapy) and assess genetic mutation. It includes three types of sampling of biological materials; which are circulating tumor cells (CTCs), circulating tumor DNA, and exosomes. CTCs have faced difficulties for years because of very low concentrations in many studies, which is 1–10 cells per 10-mL of blood (much lower than billions of hematopoietic cells) and short half-life (approximately from 1 to 2.4 h) in blood which poses difficulty in further study. AI can be used in the detection of disease from these biomarkers and various studies have explored AI algorithms for biomarkers for diagnosis[91,103]. Studies used exosomes[104-106], cell-free DNA[107], extracellular vesicles long RNA[108], proteins[109-112], and circulating microRNA[113] in association with AI for diagnosis of pancreatic cancer. Table 5 shows studies on the role of biomarkers and AI in the differentiation of pancreatic lesions.

Table 5 Studies on differentiation of indeterminate lesions using artificial intelligence algorithms on different biomarkers.
Number of samples
Type of biomarker used
Sub-type of AI used
1Chen et al[104], 201928Exosomes LDAAccuracy: 100%
2Zheng et al[105], 2022220Exosomes ANNAUC: 0.86
3Ko et al[106], 201728Exosomes LDAAccuracy: 100%
4Cristiano et al[107], 201934Cell-free DNAGBMAUC: 0.86
5Yu et al[108], 2020501extracellular vesicles long RNASVMAUC: 0.96
6Gao et al[109], 2012199ProteomesSVM, KNN, ANNAUC: 0.971
7Yu et al[110], 2005100ProteomesDTSensitivity: 88.9%, specificity: 74.1%
8Qiao et al[112], 2022136Proteomes CNNAccuracy: 87.63%
9Alizadeh et al[113], 2020671Circulating micro RNAANNAccuracy: 0.86

This review has shown that AI can be used in routine investigation modalities (CT, MRI, EUS, PET, biomarkers) to improve diagnostic and differentiating potential; however, it is still in progress. In the beginning, studies have trained and validated AI algorithms, in the future it is a challenge to implement such studies at different geographical locations, ethnicity, genetic makeup, etc. The majority of studies have explored the potential to differentiate, chronic pancreatitis from pancreatic ductal adenocarcinoma, SCN from MCN, and high-risk vs. low-risk IPMN, however, there can be other differential diagnoses in a clinical scenario.


Surgery for malignant pancreatic head lesions was standardized by Whipple et al[114] which is acceptable worldwide. It includes a complex single-stage procedure of pancreaticoduodenectomy, which is associated with morbidity (25%) and mortality (0%-9.3%) even in high-volume centers[115-117]. Professor Whipple[118] reported a mortality of 29.2% in his series of patients who underwent pancreaticoduodenectomy. Though, recent series have reported reduced mortality following pancreaticoduodenectomy, morbidity of the procedure continues to be high. Recently, many modifications have been made to reduce morbidity, however, none of the measures appeared to be successful. Are et al[119] reported a historical perspective where 7 out of 37 pancreaticoduodenectomies performed by Prof Whipple AO turned out to be chronic pancreatitis (18.9%), where such a morbid procedure could have been avoided. Recent series have also supported these findings of incidence of benign pathology following pancreaticoduodenectomy in the range of 5%-10%[117,120]. Hence, there is an unmet need to differentiate benign pancreatic lesions from malignant ones. Multiple imaging modalities have been used to distinguish benign from malignant lesions, however, each investigation modality has its limitations which are compounded by human errors. The application of AI has minimized those errors and can make diagnoses earlier. Table 6 shows how AI increases the yield of different imaging modalities for predicting a malignant pancreatic head lesion. We have proposed an algorithm for the diagnosis of such entities. Whenever a patient presents to a clinician, history and clinical examination precede imaging. Hence, AI can be used to develop algorithms to predict malignancy[32-34]. In a patient with a high risk of pancreatic malignancy, a pancreatic indeterminate lesion should be investigated further with imaging or biopsy to rule out malignancy. Studies have reported the usefulness of biomarkers in the diagnosis of pancreatic cancer[107-110]. Hence, all non-invasive markers (clinical, biochemical) can be used to develop an algorithm that can predict pancreatic cancer before imaging has been performed and it can differentiate malignant pancreatic lesions. As shown in Table 6, AI has an added advantage over conventional imaging in differentiating pancreatic cancer from benign conditions. So, those high-risk patients marked on non-invasive pancreatic cancer detection models can be subjected to AI-enhanced imaging for better diagnosis. Further in line, to clarify the final tissue diagnosis, AI can help to detect subtle markers that can be ignored by human error. Therefore, AI can be used in every step of the diagnosis of an indeterminate pancreatic head mass, to detect malignant lesions early thus, availing proper oncological management.

Table 6 Studies demonstrating impact of artificial intelligence on increasing efficacy of diagnostic modalities.
1Corral et al[67], 2019Differentiate cystic SOL of pancreasFukuoka guideline62%7777.5%
Deep learning75%78%78.3%
2Kuwahara et al[83], 2019Detection of malignant IPMNHuman pre-operative diagnosis (Clinical + lab + imaging)95.7%22.2%56%
Artificial intelligence95.7%92.6694%
3Gao et al[135], 2020Ability to differentiate pancreatic diseaseCE-MRNANA83.93%
GAN NANA76.79%
4Rigiroli et al[136], 2021Detection of pancreatic cancer and SMA involvementCT scanNANA71%
Artificial intelligence62%77%54%
5Chen et al[137], 2023Detection of pancreatic cancerCT scan89.9%95.9%AUC: 0.96
6Tang et al[138], 2023Pancreatic mass diagnosisEUS FNA81.6%100%87.9%
CE EUS Master-guided FNA90.9%100%93.8%

Pancreatic incidentalomas or indeterminate lesions are on the rise due to the plethora of cross-sectional imaging performed to diagnose non-specific abdominal complaints. Though plenty of studies have been made in the fields of breast cancer, lung cancer, hepatocellular carcinoma, renal cell carcinoma, and adrenal tumors, there is a dearth of literature discussing how to differentiate benign pancreatic lesions from benign ones. The current literature included studies comparing individual pancreatic lesions, i.e. serous cystadenoma vs. mucinous cystadenoma, autoimmune pancreatitis vs. pancreatic adenocarcinoma, low-grade vs. high-grade IPMN, etc. However, a comprehensive review discussing how to differentiate various malignant pancreatic lesions (both cystic and solid) from benign lesions with the help of AI is lacking. Hence, in this review, we have discussed how to differentiate different pancreatic lesions encountered in day-to-day clinical practice using different algorithms of AI. We have discussed individually about different diagnostic modalities and different types of pancreatic lesions. There are more studies available in the field of radiological investigations and fewer studies available for the histopathological diagnosis or intra-operative differentiation of malignant from benign lesions. As the understanding of the usefulness of AI is increasing, these limitations can be curtailed in the near future.


There is a surge in the number of medical imaging for different indications leading to the identification of many indeterminate pancreatic lesions (IPLs), which help to diagnose a disease earlier or can lead to a plethora of other investigations, psychological stress, clinical dilemmas, etc. Human judgment is prone to errors as subtle differences in these small or atypical lesions are challenging to discern leading to inter-observer and intra-observer variations which can be minimized with the use of AI.


AI is an evolving technical advancement in the field of medicine and can play a significant role in differentiating IPLs into benign or malignant, by enhancing the diagnostic yield of conventional imaging (CT, MRI, PET), EUS, tissue diagnosis (cytopathology, histopathology), and biomarkers (liquid biopsy). An early and accurate diagnosis may lead to timely intervention, thereby improving the patient outcome. The current literature on this is still limited and sparse, therefore, more studies are required to reach a standard approach for the application of AI in IPLs.


Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Gastroenterology and hepatology

Country/Territory of origin: India

Peer-review report’s scientific quality classification

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Grade B (Very good): 0

Grade C (Good): C, C

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Cabezuelo AS, Spain S-Editor: Lin C L-Editor: Filipodia P-Editor: Zhao S

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