Detection Of Pancreatic Cancer Research Articles

Fragmentomics of cell-free DNA as a sensitive biomarker for early detection of pancreatic cancer.

4173 Background: Pancreatic ductal adenocarcinoma (PDAC) is one for the most lethal types of cancer. Nearly 80% of the PDAC patients are diagnosed at advanced unresectable stages. Therefore, an accurate early diagnosis assay is urgently needed for reducing PDAC related mortalities. In this study, we aim to utilize cfDNA fragmentomics and machine learning techniques for sensitively detecting PDAC patients. Methods: Total647 cases were prospectively enrolled in this study. Plasma samples were collected from each participant for shallow WGS (~5X). After quality control process, 166 PDAC patients and 167 healthy controls were enrolled as a training cohort. Additional 112 PDAC patients and 111 healthy controls were included as an independent validation cohort. Additional 67 cases with pancreatic benign cystic tumors were also enrolled for model validation including IPMN (Intraductal papillary mucinous neoplasms), MCN (Mucinous cystic neoplasms) and SCN (Serous cystic neoplasms). Four fragmentomics profiles, including copy number variation, fragment size, mutation signatures, and FRAGmentomics-based Methylation Analysis (FRAGMA) were utilized by a machine learning algorithm for developing a predictive model. Results: The predictive model showed an exceptional performance to distinguish PDAC patients from healthy controls, yielding Area Under the Curve (AUC) of 0.993 in the training cohort (5-fold cross validation) and 0.990 in the validation cohort. In the training cohort, our model was capable of detecting PDAC patients at sensitivity of 95.8% and specificity of 95.2% while using 0.49 as cutoff. The validation cohort achieved 99.1% sensitivity at 91.0% specificity while applying the same cutoff. Conclusions: Our model was able to accurately detect PDAC at early stages, by incorporating fragmentomics features through machine learning. It can potentially be used for PDAC early screening, and therefore reducing PDAC related mortalities.

Analyzing imbalance of short tandem repeats for pancreatic cancer detection.

e16359 Background: Short tandem repeats (STRs), alternatively known as microsatellites, encompass repeat units of 1 to 6 base pairs (bp) in the genome and function as regulators of gene expression, impacting plasticity and complex traits. STRs are prone to hypermutation with alterations correlated to pathogenicity in multiple Mendelian and acquired disorders. STR variant classes include insertions, mobile element insertions (MEIs), deletions, and multi-allelic copy number variants (mCNVs). These can lead to repeat expansions, allelic imbalance (AI), and microsatellite instability (MSI) observed in diseases such as Huntington's disease, hereditary ataxia, and multiple cancers. In pancreatic cancer, AI and associated loss-of-heterozygosity (LOH) at tumor suppressor loci flanked by hypervariable STR regions correspond to a pathogenic phenotype with a risk of aggressive disease. Consequently, it is critical to develop accurate and robust methods to precisely identify and genotype pancreatic samples with varying specimen quality in a clinical setting to aid in diagnosis. Methods: Capillary electrophoresis (CE) and Sanger sequencing have been used to analyze STRs. The availability of newer tools such as 2nd generation short-read (NGS) and 3rd generation long-read (TGS) sequencers could allow for high throughput scaling, however, informatics challenges associated with analyzing repetitive regions remain. Here we developed amplification and enrichment methods for targeted analysis for detection by CE, NGS and TGS. Results: Amplification and enrichment strategies for detecting 17 short tandem repeat regions that play a role in pancreatic carcinogenesis were developed. Methods were optimized for detecting STR alterations in cell-free nucleic acids isolated from endoscope-guided pancreatic fine needle aspirate biopsies. Furthermore, informatics tools were developed to analyze short-read and long-read sequencing data to accurately analyze repeat regions. Conclusions: Here we compared 3 platforms (CE, NGS, and TGS) and found that CE remains the gold standard for analyzing AI/LOH with data comparable to long-read TGS, while short-read NGS is not always amenable to accurate analysis dependent on the region of interest. Additionally, we found that artifacts of amplification persist, irrespective of the 3 platforms analyzed.

Independent blinded validation of a novel blood-based early pancreatic cancer detection test.

Independent blinded validation of a novel blood-based early pancreatic cancer detection test.

DOTA-Based Plectin-1 Targeted Contrast Agent Enables Detection of Pancreatic Cancer in Human Tissue.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and lethal malignancy with extremely poor patient survival rates. A key reason for the poor prognosis is the lack of effective diagnostic tools to detect the disease at curable, premetastatic stages. Tumor surgical resection is PDAC's first-line treatment, however distinguishing between cancerous and healthy tissue with current imaging tools remains a challenge. In this work, we report a DOTA-based fluorescent probe targeting plectin-1 for imaging PDAC with high specificity. To enable heterogeneous functionalization of the DOTA-core with multiple targeting peptide units and the fluorophore, a novel, fully clickable synthetic route that proceeds in one pot was developed. Extensive validation of the probe set the stage for PDAC detection in mice and human tissue. Altogether, these findings may pave the way for improved clinical understanding and early detection of PDAC progression as well as more accurate resection criteria.

DOTA‐Based Plectin‐1 Targeted Contrast Agent Enables Detection of Pancreatic Cancer in Human Tissue

AbstractPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and lethal malignancy with extremely poor patient survival rates. A key reason for the poor prognosis is the lack of effective diagnostic tools to detect the disease at curable, premetastatic stages. Tumor surgical resection is PDAC's first‐line treatment, however distinguishing between cancerous and healthy tissue with current imaging tools remains a challenge. In this work, we report a DOTA‐based fluorescent probe targeting plectin‐1 for imaging PDAC with high specificity. To enable heterogeneous functionalization of the DOTA‐core with multiple targeting peptide units and the fluorophore, a novel, fully clickable synthetic route that proceeds in one pot was developed. Extensive validation of the probe set the stage for PDAC detection in mice and human tissue. Altogether, these findings may pave the way for improved clinical understanding and early detection of PDAC progression as well as more accurate resection criteria.

Analysis of the occurrence of germline mutations BRCA1\2, PALB2, CHEK2, NBN in patients with pancreatic malignancies. Single-center cohort non-randomized retrospective study

Objective. To analyze the frequency of carriage of BRCA1\2, PALB2, CHEK2, NBN mutations in patients with malignant neoplasms of pancreas. Materials and methods. The single-center cohort non-randomized retrospective study is based on the data of 82 patients who were examined and treated in Russian Research Center of Radiology and Surgical Technologies named after academician A.M. Granov from 2020 to 2022. Patients with confirmed ductal adenocarcinoma of pancreas were included into the study group. Screening of mutations in exons 2,10, 18, 19 of BRCA1 gene and exon 11 of BRCA2 gene was performed in these patients. In addition, oncological family histories were studied. Results. Analysis of medical documentation data showed that 18 (22 %) patients with pancreatic cancer had a hereditary oncological history. In this cohort of patients, 5 (28 %) had relatives with pancreatic cancer, 9 (50 %) had a family history of ovarian cancer, 2 (11 %) female relatives of patients in the study group were diagnosed with breast cancer before the age of 50, also 2 (11 %) patients had a history of more than 2 relatives who suffered from breast cancer and / or prostate cancer. When evaluating the results of revealing the mutations in the entire study group (82 patients), BRCA1 (c.5266dupC) was revealed in 8 patients (9.7 %), PALB-2 (c.1592delT) – in 2 patients (2.4 %), mutations CHEK2, NBN and BRCA2 were not diagnosed in any patient. 5 (6 %) patients who were BRCA1 mutation carriers and one patient with an established PALB2 mutation, according to the analysis of case histories, had no oncological history. None of the patients in the study group was a carrier of the BRCA2, CHEK2 and NBN mutations. Conclusions. Some patients with pancreatic cancer are carriers of germline mutations. Considering our data on the trend of association between germline mutations and pancreatic cancer, we can make an assumption about the prospect of using this indicator as one of the markers for early detection of pancreatic cancer not only in patients with hereditary risk factors for neoplasia, but also in patients without cancer anamnesis. To obtain the results, further observation of patients in the study group and randomized multicenter studies are required.

Diabetes and Cancer: A Twisted Bond.

This paper presents an overview of the interconnection between various factors related to both cancer and type 2 diabetes mellitus (T2DM). Hyperglycemia, hyperinsulinemia, chronic inflammation, and obesity are involved in the development and progression of both diseases but, strong evidence for a direct causal relationship between diabetes and cancer, is lacking. Several studies described a relationship between hyperglycemia and cancer at the cellular, tissular and organismic levels but at the same time recent Mendelian randomization studies proved a significant causal relationship only between hyperglycemia and breast cancer. On the other hand, the association between both hyperinsulinemia and obesity and several cancer types appears to be robust as demonstrated by Mendelian randomized studies. Metabolic alterations, including the Warburg effect and excessive glucose consumption by tumors, are discussed, highlighting the potential impact of dietary restrictions, such as fasting and low-carb diets, on tumor growth and inflammation. Recent data indicates that circulating branched-chain amino acids levels, may represent novel biomarkers that may contribute to both better diabetes control and early pancreatic cancer detection. Understanding the underlying mechanisms and shared risk factors between cancer and T2DM can provide valuable insights for cancer prevention, early detection, and management strategies.

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.

Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Identification of biomarkers and potential therapeutic targets for pancreatic cancer by proteomic analysis in two prospective cohorts

Pancreatic cancer (PC) is the deadliest malignancy due to late diagnosis. Aberrant alterations in the blood proteome might serve as biomarkers to facilitate early detection of PC. We designed a nested case-control study of incident PC based on a prospective cohort of 38,295 elderly Chinese participants with ∼5.7 years' follow-up. Forty matched case-control pairs passed the quality controls for the proximity extension assay of 1,463 serum proteins. With a lenient threshold of p<0.005, we discovered regenerating family member 1A (REG1A), REG1B, tumor necrosis factor (TNF), and phospholipase A2 group IB (PLA2G1B) in association with incident PC, among which the two REG1 proteins were replicated using the UK Biobank Pharma Proteomics Project, with effect sizes increasing steadily as diagnosis time approaches the baseline. Mendelian randomization analysis further supported the potential causal effects of REG1 proteins on PC. Taken together, circulating REG1A and REG1B are promising biomarkers and potential therapeutic targets for the early detection and prevention of PC.

Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics

BackgroundPlasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection.MethodsOne hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19–9 (CA19-9) was explored to improve model performance.ResultsThe stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved.ConclusionsOur model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Dysbiosis Signature of Fecal Microbiota in Patients with Pancreatic Adenocarcinoma and Pancreatic Intraductal Papillary Mucinous Neoplasms.

Pancreatic cancer (PC) ranks as the seventh leading cause of cancer-related deaths, with approximately 500,000 new cases reported in 2020. Existing strategies for early PC detection primarily target individuals at high risk of developing the disease. Nevertheless, there is a pressing need to identify innovative clinical approaches and personalized treatments for effective PC management. This study aimed to explore the dysbiosis signature of the fecal microbiota in PC and potential distinctions between its Intraductal papillary mucinous neoplasm (IPMN) and pancreatic ductal adenocarcinoma (PDAC) phenotypes, which could carry diagnostic significance. The study enrolled 33 participants, including 22 diagnosed with PDAC, 11 with IPMN, and 24 healthy controls. Fecal samples were collected and subjected to microbial diversity analysis across various taxonomic levels. The findings revealed elevated abundances of Firmicutes and Proteobacteria in PC patients, whereas healthy controls exhibited higher proportions of Bacteroidota. Both LEfSe and Random Forest analyses indicated the microbiome's potential to effectively distinguish between PC and healthy control samples but fell short of differentiating between IPMN and PDAC samples. These results contribute to the current understanding of this challenging cancer type and highlight the applications of microbiome research. In essence, the study provides clear evidence of the gut microbiome's capability to serve as a biomarker for PC detection, emphasizing the steps required for further differentiation among its diverse phenotypes.

3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

Potential Health Disparities in the Early Detection and Prevention of Pancreatic Cancer.

Pancreatic cancer remains one of the deadliest cancers in the United States. Some types of pancreatic cysts, which are being detected more frequently and often incidentally on imaging, have the potential to develop into pancreatic cancer and thus provide a valuable window of opportunity for cancer interception. Although racial disparity in pancreatic cancer has been described, little is known regarding health disparities in pancreatic cancer prevention. In the present study, we investigate potential health disparities along the continuum of care for pancreatic cancer. The racial and ethnic composition of pancreatic patients at high-volume centers in Indiana were evaluated, representing patients undergoing surgery for pancreatic cancer (n=390), participating in biobanking (972 pancreatic cancer patients and 1984 patients with pancreatic disease), or being monitored for pancreatic cysts at an early detection center (n=1514). To assess racial disparities and potential differences in decision-making related to pancreatic cancer prevention and early detection, an exploratory online survey was administered through a volunteer registry (n=708). Results: We show that despite comprising close to 10% or 30% of the Indiana or Indianapolis population, respectively, African Americans make up only about 4-5% of our study cohorts consisting of patients undergoing pancreatic surgery or participating in biobanking and early detection. Analysis of online survey results revealed that given the hypothetical situation of being diagnosed with a pancreatic cyst or pancreatic cancer, the vast majority of respondents (>90%) would agree to undergo surveillance or surgery, respectively, regardless of race.Only a minority (3-12%) acknowledged any significant transportation, financial, or emotional barriers that would impact a decision to undergo surveillance or surgery. This suggests that the observed racial disparities may be due in part to the existence of other barriers that lie upstream of this decision point. Racial disparities exist not only for pancreatic cancer but also at earlier points along the continuum of care such as prevention and early detection. To our knowledge, this is the first study to document racial disparity in the management of patients with pancreatic cysts who are at risk of developing pancreatic cancer. Our results suggest that improving access to information and care for such at-risk individuals may lead to more equitable outcomes.

Unveiling liquid markers: The diagnostic revolution in early detection of pancreatic cancer through liquid biopsy

Unveiling liquid markers: The diagnostic revolution in early detection of pancreatic cancer through liquid biopsy

Multiscale and multiperception feature learning for pancreatic lesion detection based on noncontrast CT

Background. Pancreatic cancer is one of the most malignant tumours, demonstrating a poor prognosis and nearly identically high mortality and morbidity, mainly because of the difficulty of early diagnosis and timely treatment for localized stages. Objective. To develop a noncontrast CT (NCCT)-based pancreatic lesion detection model that could serve as an intelligent tool for diagnosing pancreatic cancer early, overcoming the challenges associated with low contrast intensities and complex anatomical structures present in NCCT images. Approach. We design a multiscale and multiperception (MSMP) feature learning network with ResNet50 coupled with a feature pyramid network as the backbone for strengthening feature expressions. We added multiscale atrous convolutions to expand different receptive fields, contextual attention to perceive contextual information, and channel and spatial attention to focus on important channels and spatial regions, respectively. The MSMP network then acts as a feature extractor for proposing an NCCT-based pancreatic lesion detection model with image patches covering the pancreas as its input; Faster R-CNN is employed as the detection method for accurately detecting pancreatic lesions. Main results. By using the new MSMP network as a feature extractor, our model outperforms the conventional object detection algorithms in terms of the recall (75.40% and 90.95%), precision (40.84% and 68.21%), F1 score (52.98% and 77.96%), F2 score (64.48% and 85.26%) and Ap50 metrics (53.53% and 70.14%) at the image and patient levels, respectively. Significance.The good performance of our new model implies that MSMP can mine NCCT imaging features for detecting pancreatic lesions from complex backgrounds well. The proposed detection model is expected to be further developed as an intelligent method for the early detection of pancreatic cancer.

Detection of Pancreatic Cancer and Nutritional Support in Chronic Pancreatitis

Chronic pancreatitis (CP) is a significant risk factor for pancreatic cancer and there had been a great deal of interest in surveillance and detection of pancreatic cancer. According to presence of genetic mutation, CP can be classified into hereditary CP and sporadic CP. CP with PRSS1 gene mutation showed a high incidence of pancreatic cancer justifying screening and surveillance for those older than 40 years. But CP with other mutations and sporadic CP have relatively low incidence of pancreatic cancer and surveillance is not recommended. While computed tomography or magnetic resonance imaging is widely used for screening, endoscopic ultrasound is not recommended because of parenchymal inflammation, fibrosis, and calcification associated with CP. However, when a patient with CP has multiple risk factors for pancreatic cancer, risk of pancreatic cancer increases significantly. Patients with CP have increased risk of pancreatic cancer at five years after diagnosis; individualized screening and surveillance based on symptoms and specific circumstances should be considered. Patients with CP have a high risk of malnutrition and malnutrition is quite common. Clinicians should assess nutritional status and implement nutritional support. Nutritional support must include both macronutrients and micronutrients. Given the high risk of osteoporosis and osteopathy in CP, clinicians should consider early diagnosis, prevention, and treatment. Consultation with nutritional support team is highly recommended.

Electrochemical sensor for rapid diagnosis and early-stage detection of pancreatic cancer using Er-GQDs decorated MoS2 nanoflowers

In this study, for the first time, the erbium-doped graphene quantum dots (Er-GQDs) were decorated onto a three-dimensional MoS2 nanoflower (EGM) for electrochemically sensitive and selective detection of carbohydrate antigen, CA 19-9, in phosphate buffer saline (PBS) and biological fluids. The MoS2 nanoflowers (MoS2 NF) were hydrothermally prepared at 200 °C for 24 h, and then 2–8 nm of Er-GQDs were uniformly deposited onto 200 nm MoS2 NF. The MoS2 NF has a high electric conductivity for electrochemical performance, whereas Er-GQDs act as an effective anchor with anti-CA19-9 antibodies (CA19-9 Ab) for ultra-selectivity to CA19-9 antigen. The SPE||EGM/Ab immunosensor has substantial benefits by providing a long-lasting, ultra-sensitive, and highly selective platform for CA19-9 antigen detection. The dynamic ranges in PBS, human serum, and saliva are 5 × 10−5–250, 5 × 10−4–250, and 3 × 10−4–250 U mL−1, respectively, with limits of detection (LODs) of (0.18–2.95) × 10−4 U mL−1. Furthermore, the SPE||EGM/Ab immunosensor can detect CA19-9 in human serum with a recovery of 97–108 %. The non-invasive detection of CA19-9 in saliva is stably sensitive before and after 1 h of breakfast. Moreover, the SPE||EGM/Ab immunosensor has excellent selectivity toward CA19-9 over the other 17 interferences. Results elaborate that the SPE||EGM/Ab immunosensor is a promising electrochemical biosensing probe for detecting CA19-9 in human serum and saliva for clinical diagnosis and prognosis of pancreatic cancer in the early stage.

Deep learning‐based aggregate analysis to identify cut‐off points for decision‐making in pancreatic cancer detection

AbstractThis study addresses the problem of detecting pancreatic cancer by classifying computed tomography (CT) images into cancerous and non‐cancerous classes using the proposed deep learning‐based aggregate analysis framework. The application of deep learning, as a branch of machine learning and artificial intelligence, to specific medical challenges can lead to the early detection of diseases, thus accelerating the process towards timely and effective intervention. The concept of classification is to reasonably select an optimal cut‐off point, which is used as a threshold for evaluating the model results. The choice of this point is key to ensure efficient evaluation of the classification results, which directly affects the diagnostic accuracy. A significant aspect of this research is the incorporation of private CT images from Vilnius University Hospital Santaros Klinikos, combined with publicly available data sets. To investigate the capabilities of the deep learning‐based framework and to maximize pancreatic cancer diagnostic performance, experimental studies were carried out combining data from different sources. Classification accuracy metrics such as the Youden index, (0, 1)‐criterion, Matthew's correlation coefficient, the F1 score, LR+, LR−, balanced accuracy, and g‐mean were used to find the optimal cut‐off point in order to balance sensitivity and specificity. By carefully analyzing and comparing the obtained results, we aim to develop a reliable system that will not only improve the accuracy of pancreatic cancer detection but also have wider application in the early diagnosis of other malignancies.

The role of biomarkers in the early detection of pancreatic cancer.

Pancreatic surveillance can detect early-stage pancreatic cancer and achieve long-term survival, but currently involves annual endoscopic ultrasound and MRI/MRCP, and is recommended only for individuals who meet familial/genetic risk criteria. To improve upon current approaches to pancreatic cancer early detection and to expand access, more accurate, inexpensive, and safe biomarkers are needed, but finding them has remained elusive. Newer approaches to early detection, such as using gene tests to personalize biomarker interpretation, and the increasing application of artificial intelligence approaches to integrate complex biomarker data, offer promise that clinically useful biomarkers for early pancreatic cancer detection are on the horizon.

Pancreatic cancer: from early detection to personalized treatment approaches.

Pancreatic cancer is notorious for its persistently poor prognosis and health outcomes, so some of the questions that may be begged are "Why is it mostly diagnosed at end stage?", "What could we possibly do with the advancing technology in today's world to detect early pancreatic cancer and intervene?", and "Are there any implementation of the existing novel imaging technologies?". Well, to start with, this is in part because the majority of patients presented would already have reached a locally advanced or metastatic stage at the time of diagnosis due to its highly aggressive characteristics and lack of symptoms. Due to this striking disparity in survival, advancements in early detection and intervention are likely to significantly increase patients' survival. Presently, screening is frequently used in high-risk individuals in order to obtain an early pancreatic cancer diagnosis. Having a thorough understanding of the pathogenesis and risk factors of pancreatic cancer may enable us to identify individuals at high risk, diagnose the disease early, and begin treatment promptly. In this review, the authors outline the clinical hurdles to early pancreatic cancer detection, describe high-risk populations, and discuss current screening initiatives for high-risk individuals. The ultimate goal of this current review is to study the roles of both traditional and novel imaging modalities for early pancreatic cancer detection. A lot of the novel imaging techniques mentioned seem promising, but they need to be put to the test on a large scale and may need to be combined with other non-invasive biomarkers before they can be widely used.