Detection Of Pancreatic Cancer Research Articles

Mucins as Potential Biomarkers for Early Detection of Cancer.

Early detection significantly correlates with improved survival in cancer patients. So far, a limited number of biomarkers have been validated to diagnose cancers at an early stage. Considering the leading cancer types that contribute to more than 50% of deaths in the USA, we discuss the ongoing endeavors toward early detection of lung, breast, ovarian, colon, prostate, liver, and pancreatic cancers to highlight the significance of mucin glycoproteins in cancer diagnosis. As mucin deregulation is one of the earliest events in most epithelial malignancies following oncogenic transformation, these high-molecular-weight glycoproteins are considered potential candidates for biomarker development. The diagnostic potential of mucins is mainly attributed to their deregulated expression, altered glycosylation, splicing, and ability to induce autoantibodies. Secretory and shed mucins are commonly detected in patients' sera, body fluids, and tumor biopsies. For instance, CA125, also called MUC16, is one of the biomarkers implemented for the diagnosis of ovarian cancer and is currently being investigated for other malignancies. Similarly, MUC5AC, a secretory mucin, is a potential biomarker for pancreatic cancer. Moreover, anti-mucin autoantibodies and mucin-packaged exosomes have opened new avenues of biomarker development for early cancer diagnosis. In this review, we discuss the diagnostic potential of mucins in epithelial cancers and provide evidence and a rationale for developing a mucin-based biomarker panel for early cancer detection.

Amplification-Free, High-Throughput Nanoplasmonic Quantification of Circulating MicroRNAs in Unprocessed Plasma Microsamples for Earlier Pancreatic Cancer Detection.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that is often detected at an advanced stage. Earlier diagnosis of PDAC is key to reducing mortality. Circulating biomarkers such as microRNAs are gaining interest, but existing technologies require large sample volumes, amplification steps, extensive biofluid processing, lack sensitivity, and are low-throughput. Here, we present an advanced nanoplasmonic sensor for the highly sensitive, amplification-free detection and quantification of microRNAs (microRNA-10b, microRNA-let7a) from unprocessed plasma microsamples. The sensor construct utilizes uniquely designed -ssDNA receptors attached to gold triangular nanoprisms, which display unique localized surface plasmon resonance (LSPR) properties, in a multiwell plate format. The formation of -ssDNA/microRNA duplex controls the nanostructure-biomolecule interfacial electronic interactions to promote the charge transfer/exciton delocalization processes and enhance the LSPR responses to achieve attomolar (10-18 M) limit of detection (LOD) in human plasma. This improve LOD allows the fabrication of a high-throughput assay in a 384-well plate format. The performance of nanoplasmonic sensors for microRNA detection was further assessed by comparing with the qRT-PCR assay of 15 PDAC patient plasma samples that shows a positive correlation between these two assays with the Pearson correlation coefficient value >0.86. Evaluation of >170 clinical samples reveals that oncogenic microRNA-10b and tumor suppressor microRNA-let7a levels can individually differentiate PDAC from chronic pancreatitis and normal controls with >94% sensitivity and >94% specificity at a 95% confidence interval (CI). Furthermore, combining both oncogenic and tumor suppressor microRNA levels significantly improves differentiation of PDAC stages I and II versus III and IV with >91% and 87% sensitivity and specificity, respectively, in comparison to the sensitivity and specificity values for individual microRNAs. Moreover, we show that the level of microRNAs varies substantially in pre- and post-surgery PDAC patients (n = 75). Taken together, this ultrasensitive nanoplasmonic sensor with excellent sensitivity and specificity is capable of assaying multiple biomarkers simultaneously and may facilitate early detection of PDAC to improve patient care.

Utility of promoter hypermethylation in malignant risk stratification of intraductal papillary mucinous neoplasms

BackgroundIntraductal papillary mucinous neoplasms (IPMNs), a type of cystic pancreatic cancer (PC) precursors, are increasingly identified on cross-sectional imaging and present a significant diagnostic challenge. While surgical resection of IPMN-related advanced neoplasia, i.e., IPMN-related high-grade dysplasia or PC, is an essential early PC detection strategy, resection is not recommended for IPMN-low-grade dysplasia (LGD) due to minimal risk of carcinogenesis, and significant procedural risks. Based on their promising results in prior validation studies targeting early detection of classical PC, DNA hypermethylation-based markers may serve as a biomarker for malignant risk stratification of IPMNs. This study investigates our DNA methylation-based PC biomarker panel (ADAMTS1, BNC1, and CACNA1G genes) in differentiating IPMN-advanced neoplasia from IPMN-LGDs.MethodsOur previously described genome-wide pharmaco-epigenetic method identified multiple genes as potential targets for PC detection. The combination was further optimized and validated for early detection of classical PC in previous case–control studies. These promising genes were evaluated among micro-dissected IPMN tissue (IPMN-LGD: 35, IPMN-advanced neoplasia: 35) through Methylation-Specific PCR. The discriminant capacity of individual and combination of genes were delineated through Receiver Operating Characteristics curve analysis.ResultsAs compared to IPMN-LGDs, IPMN-advanced neoplasia had higher hypermethylation frequency of candidate genes: ADAMTS1 (60% vs. 14%), BNC1 (66% vs. 3%), and CACGNA1G (25% vs. 0%). We observed Area Under Curve (AUC) values of 0.73 for ADAMTS1, 0.81 for BNC1, and 0.63 for CACNA1G genes. The combination of the BNC1/ CACNA1G genes resulted in an AUC of 0.84, sensitivity of 71%, and specificity of 97%. Combining the methylation status of the BNC1/CACNA1G genes, blood-based CA19-9, and IPMN lesion size enhanced the AUC to 0.92.ConclusionDNA-methylation based biomarkers have shown a high diagnostic specificity and moderate sensitivity for differentiating IPMN-advanced neoplasia from LGDs. Addition of specific methylation targets can improve the accuracy of the methylation biomarker panel and enable the development of noninvasive IPMN stratification biomarkers.

Familial pancreatic cancer syndrome

In contrast to many other malignant entities the risk factors for pancreatic cancer are mostly unknown. Hereditary factors are causative in about 3-6% of all pancreatic cancers. In addition to hereditary tumor disposition syndromes and hereditary pancreatitis, the familial pancreatic cancer syndrome accounts for about 75% of all hereditary pancreatic cancers. Afamilial pancreatic cancer syndrome is present when at least two first-degree relatives in afamily have histologically proven pancreatic cancer and there are no criteria for another hereditary syndrome. An underlying uniform monogenetic gene defect in familial pancreatic cancer syndrome is currently unknown and agermline mutation in the BRCA2 gene is so far the most frequently identified genetic alteration. Patients at risk for familial pancreatic cancer should be offered participation in registry studies with associated early detection programs (e.g., the FaPaCa registry). These enable the detection of high-grade neoplasms and pancreatic cancer in the early stages, which appears to reduce mortality; however, the detected pancreatic lesions are often incorrectly diagnostically classified, and patients undergo complex pancreatic resection despite benign findings. Finally, studies have so far not been able to clearly clarify whether patients at risk benefit from an early detection program. According to current data, prophylactic operations are not indicated for familial pancreatic cancer syndrome and should only be performed if there is evidence of asuspicious finding that requires resection.

Variabilities in global DNA methylation and β-sheet richness establish spectroscopic landscapes among subtypes of pancreatic cancer

PurposeKnowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue.MethodsHere, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues.ResultsThe results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies.ConclusionsMolecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.

Salivary Polyamines Help Detect High-Risk Patients with Pancreatic Cancer: A Prospective Validation Study.

Pancreatic cancer is one of the most malignant cancer types and has a poor prognosis. It is often diagnosed at an advanced stage because of the absence of typical symptoms. Therefore, it is necessary to establish a screening method for the early detection of pancreatic cancer in high-risk individuals. This is a prospective validation study conducted in a cohort of 1033 Japanese individuals (male, n = 467, age = 63.3 ± 11.5 years; female, n = 566, age = 64.2 ± 10.6 years) to evaluate the use of salivary polyamines for screening pancreatic diseases and cancers. Patients with pancreatic cancer were not included; however, other pancreatic diseases were treated as positive cases for accuracy verification. Of the 135 individuals with elevated salivary polyamine markers, 66 had pancreatic diseases, such as chronic pancreatitis and pancreatic cysts, and 1 had gallbladder cancer. These results suggest that the salivary polyamine panel is a useful noninvasive pancreatic disease screening tool.

Early detection of pancreatic cancer using 5-hydroxymethylation profiles in plasma-derived cell-free DNA.

672 Background: Pancreatic cancer is one of the deadliest cancers, with approximately 15-20% of patients who present at diagnosis with a resectable disease. The major barrier to better outcomes is the lack of early-detection molecular tools to enable timely intervention. We have developed a test that enables the detection of pancreatic cancer from a simple blood draw. The test incorporates a novel, genome-wide sequencing-based epigenomics detection method that enriches for DNA loci that undergo active de-methylation. The measurement of 5-hydroxymethylcytosine (5hmC) provides a unique and stable biomarker for the early detection of cancer including pancreatic cancer. Methods: Whole-blood was obtained from a training cohort of 660 individuals (consisting of 132 pancreatic cancers (PaCa) and 528 non-cancers) and a validation cohort of 2,150 individuals (consisting of 102 PaCa and 2,048 non-cancers). Cell-free DNA (cfDNA) was isolated from plasma from which 5hmC and whole-genome libraries were generated and sequenced. Logistic regression algorithms were employed using 5hmC feature sets combined with physical characteristics of DNA fragments to optimally partition cancer from non-cancer samples. Results: Cross validation of the training model yielded an overall sensitivity of 65.9%,(95% CI, 57.2%–73.9%), early-stage (stage I-II) sensitivity of 57.1% (95% CI, 44%–69.5%) and a specificity of 98%. The model was further validated in a separate, non-overlapping set of blinded and independently processed samples and yielded an early-stage sensitivity of 68.3% (95% CI, 51.9%–81.9%) and a specificity of 96.9% (95% CI, 96.0%–97.6%). Conclusions: Our results demonstrate that plasma-derived cfDNA 5hmC profiles enable the accurate detection of early-stage PaCa, providing a valuable non-invasive tool especially for those individuals at high risk for the disease, including individuals with genetic predisposition and newly diagnosed type 2 diabetes. A larger clinical study (NODMED - NCT05188586) is ongoing and will provide clinical validation for the detection in individuals at high risk for this deadly disease.

Detection of pancreatic cancer with two- and three-dimensional radiomic analysis in a nationwide population-based real-world dataset

BackgroundCT is the major detection tool for pancreatic cancer (PC). However, approximately 40% of PCs < 2 cm are missed on CT, underscoring a pressing need for tools to supplement radiologist interpretation.MethodsContrast-enhanced CT studies of 546 patients with pancreatic adenocarcinoma diagnosed by histology/cytology between January 2005 and December 2019 and 733 CT studies of controls with normal pancreas obtained between the same period in a tertiary referral center were retrospectively collected for developing an automatic end-to-end computer-aided detection (CAD) tool for PC using two-dimensional (2D) and three-dimensional (3D) radiomic analysis with machine learning. The CAD tool was tested in a nationwide dataset comprising 1,477 CT studies (671 PCs, 806 controls) obtained from institutions throughout Taiwan.ResultsThe CAD tool achieved 0.918 (95% CI, 0.895–0.938) sensitivity and 0.822 (95% CI, 0.794–0.848) specificity in differentiating between studies with and without PC (area under curve 0.947, 95% CI, 0.936–0.958), with 0.707 (95% CI, 0.602–0.797) sensitivity for tumors < 2 cm. The positive and negative likelihood ratios of PC were 5.17 (95% CI, 4.45–6.01) and 0.10 (95% CI, 0.08–0.13), respectively. Where high specificity is needed, using 2D and 3D analyses in series yielded 0.952 (95% CI, 0.934–0.965) specificity with a sensitivity of 0.742 (95% CI, 0.707–0.775), whereas using 2D and 3D analyses in parallel to maximize sensitivity yielded 0.915 (95% CI, 0.891–0.935) sensitivity at a specificity of 0.791 (95% CI, 0.762–0.819).ConclusionsThe high accuracy and robustness of the CAD tool supported its potential for enhancing the detection of PC.

Current Status of the Diagnosis of Early-Stage Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) can be treated with surgery, chemotherapy, and radiotherapy. Despite medical progress in each field in recent years, it is still insufficient for managing PDAC, and at present, the only curative treatment is surgery. A typical pancreatic cancer is relatively easy to diagnose with imaging. However, it is often not recommended for surgical treatment at the time of diagnosis due to metastatic spread beyond the pancreas. Even if it is operable, it often recurs during postoperative follow-up. In the case of PDAC with a diameter of 10 mm or less, the 5-year survival rate is as good as 80% or more, and the best index for curative treatment is tumor size. The early detection of pancreatic cancer with a diameter of less than 10 mm or carcinoma in situ is critical. Here, we provide an overview of the current status of diagnostic imaging features and genetic tests for the accurate diagnosis of early-stage PDAC.

Detection and diagnosis of pancreatic cancer: Computational biology approaches

Detection and diagnosis of pancreatic cancer: Computational biology approaches

Detection and Treatment of Recurrent Pancreatic Cancer – A European Prospective, Snapshot Study

Detection and Treatment of Recurrent Pancreatic Cancer – A European Prospective, Snapshot Study

Proposed carbohydrate antigen 19-9 (CA19-9) cut-off values for the detection of pancreatic cancer in patients with poorly controlled diabetes: a real-world study.

CA19-9 is a tumor marker for pancreatic cancer (PC), and the nondiabetic cut-off level is 37 U/mL. CA19-9 levels are said to rise in patients with tumors like PC and intraductal papillary mucinous neoplasm (IPMN). CA19-9 levels have also been shown to be related to HbA1c levels. We hypothesized that the CA19-9 cut-off levels would differ between patients with poorly controlled diabetes. This real-world trial was designed to test our hypotheses. This was a retrospective cohort study. All inpatients with poorly controlled diabetes had mean HbA1c levels of 10.0% and were divided into three groups: those with pancreatic cancer (PC group, N = 20), those with IPMN (IPMN group, N = 55), and those with neither (NC group, N = 985). Serum CA19-9 levels in the PC group were significantly higher than in the IPMN and NC groups (p < 0.001). CA19-9 levels did not differ statistically between the IPMN and NC groups. According to the receiver operating characteristic (ROC) analysis, serum CA19-9 levels of 98.4 U/mL had the highest sensitivity and specificity to detect PC, when comparing PC to IPMN + NC groups. Using this cut-off, the sensitivity and specificity of CA19-9 for PC were 70.0% and 96.5%, respectively, with a 0.81 area under the ROC curve. CA19-9 levels in two inpatients were >98.4 U/mL, most likely due to hepatocellular carcinoma and esophageal cancer. CA19-9 cut-off levels were thought to be 98.4 U/mL. However, we should keep in mind that the sensitivity and specificity were not 100%.

Early Detection of Pancreatic Cancer: Opportunities Provided by Cancer-induced Paraneoplastic Phenomena and Artificial Intelligence

Abstract Objective: Pancreatic ductal adenocarcinoma (PDAC) is the most lethal cancer, with a 5-year survival rate of only 11%. Surgery is the only potential cure for PDAC, but approximately 85% of patients present with unresectable tumors at diagnosis. The difficulty in early detection is attributed to the fact that early PDACs cause few or nonspecific symptoms and are frequently obscure or even invisible in imaging studies such as computed tomography (CT). This review aims to briefly summarize the status of screening/surveillance for PDAC and elaborate on the potential windows of opportunity for early detection through PDAC-induced paraneoplastic phenomena and artificial intelligence (AI)-augmented image analysis. Data Sources: Relevant studies and review articles were searched in PubMed. Study Selection: Studies and articles on human subjects were selected. Results: Surveillance for high-risk individuals with imaging-based tools (endoscopic ultrasound and magnetic resonance image) is now advocated, whereas screening for asymptomatic general populations is not warranted at present. Paraneoplastic syndromes, including pancreatic cancer-associated diabetes and cachexia, are prevalent in PDAC patients and may provide windows of opportunity for early detection. S100A9 and galectin-3 are novel PDAC-derived factors mediating pancreatic cancer-associated diabetes and have shown promise in facilitating the early detection of PDAC. Novel computer-aided detection tools based on AI technologies, including deep learning and radiomic analysis with machine learning, have achieved accurate detection and might supplement human interpretation to improve the sensitivity for early PDAC on CT images. Conclusion: Novel blood-based biomarkers and AI-augmented image analysis may be complementary and hold promise for the early detection of PDAC.

The Co-expression of HER Family Members and CD109 is common in Pancreatic Cancer.

In the absence of biomarkers for the early detection of pancreatic cancer and the development of more effective therapeutic interventions, pancreatic cancer is projected to become the second leading cause of death from cancer in the Western world. In the past four decades, aberrant expression and activation of human epidermal growth factor receptor (EGFR, also called HER) family members have been reported in a wide range of human cancers. Currently, of the various types of drugs targeting one or more members of the HER family, only the EGFR-specific tyrosine kinase inhibitor erlotinib and more recently Zenocutuzumab, a HER-2/HER3 bispecific antibody, were granted breakthrough therapy designation by the FDA for the treatment of patients with pancreatic cancer. However, the therapeutic benefit may be modest in some patients. Hence there is an urgent need for the identification of biomarkers as prognostic indicators, therapeutic targets, and for the response to therapy and the development of more effective therapeutic agents for this highly heterogeneous cancer. In this study, for the first time, we investigated the relative expression and prognostic significance of all members of the HER family, the type-III EGFR mutant (EGFRvIII), and CD109 in tissue microarrays (TMAs) and whole tumour specimens (WTS) from pancreatic cancer patients by immunohistochemistry. We found the positive expression of wild-type EGFR (wt-EGFR) (63%, 4.7%), HER2 (75%, 14%), HER3 (none, 14%), HER4 (45%, 21%), EGFRvIII (5%, 3%), and CD109 (67%, 55%) in TMAs (USA) and WTS (UK) from pancreatic cancer patients respectively. Our results also show that the co-expression of HER family members with CD109 occurs frequently in patients with pancreatic cancer. In addition, the co-expression of HER4/CD109 may also be associated with poorer overall survival in such patients. In this article, we shall discuss these findings and their implications and future opportunities for more effective targeting of HER positive pancreatic cancer using the HER inhibitors in combination with other drugs and therapeutic interventions.

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

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 medicine for early detection purposes.

Combining Urinary Biomarker Panels and Machine Learning for Earlier Detection of Pancreatic Cancer

Combining Urinary Biomarker Panels and Machine Learning for Earlier Detection of Pancreatic Cancer

Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification

Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification

Multifunctional GQDs for receptor targeting, drug delivery, and bioimaging in pancreatic cancer.

Pancreatic cancer is a devastating disease with a low survival rate and limited treatment options. Graphene quantum dots (GQDs) have recently become popular as a promising platform for cancer diagnosis and treatment due to their exceptional physicochemical properties, such as biocompatibility, stability, and fluorescence. This review discusses the potential of multifunctional GQDs as a platform for receptor targeting, drug delivery, and bioimaging in pancreatic cancer. The current studies emphasized the ability of GQDs to selectively target pancreatic cancer cells by overexpressing binding receptors on the cell surface. Additionally, this review discussed the uses of GQDs as drug delivery vehicles for the controlled and targeted release of therapeutics for pancreatic cancer cells. Finally, the potential of GQDs as imaging agents for pancreatic cancer detection and monitoring has been discussed. Overall, multifunctional GQDs showed great promise as a versatile platform for the diagnosis and treatment of pancreatic cancer. Further investigation of multifunctional GQDs in terms of their potential and optimization in the context of pancreatic cancer therapy is needed.

Role of EUS combined with a newly modified scoring system to detect pancreatic high-grade precancerous lesions.

Although pancreatic cancer (PC) has an extremely poor prognosis, the 5-year survival rate of patients with pancreatic high-grade precancerous lesion without invasive carcinoma (PHP) is favorable. PHP diagnosis and identification of patients requiring intervention are needed. We aimed to validate a modified PC detection scoring system regarding its detection ability for PHP and PC in the general population. We modified an existing PC detection scoring system that incorporates low-grade risk (LGR) factors (family history, presence of diabetes mellitus [DM] or worsening DM, heavy drinking, smoking, stomach symptoms, weight loss, and pancreatic enzyme) and high-grade risk (HGR) factors (new-onset DM, familial PC, jaundice, tumor biomarkers, chronic pancreatitis, intraductal papillary mucinous neoplasm, cysts, hereditary PC syndrome, and hereditary pancreatitis). Each factor was scored as one point; LGR score ≥3 points and/or HGR score ≥1 point (positive scores) were indicative of PC. The newly modified scoring system incorporated main pancreatic duct dilation as an HGR factor. The PHP diagnosis rate using this scoring system combined with EUS was prospectively analyzed. Among 544 patients with positive scores, 10 had PHP. The diagnosis rates were 1.8% for PHP and 4.2% for invasive PC. Although the number of LGR and HGR factors tended to increase with PC progression, none of the individual factors were significantly different between patients with PHP and those without lesions. The newly modified scoring system evaluating multiple factors associated with PC could potentially identify patients with higher risk of PHP or PC.

Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?

Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.