Early Detection Of Pancreatic Adenocarcinoma Research Articles

Urinary biomarkers analysis as a diagnostic tool for early detection of pancreatic adenocarcinoma: Molecular quantification approach

Background and aimsPancreatic ductal adenocarcinoma (PDAC) is infrequent. Currently, non-invasive biomarkers for early detection of PDAC are not accessible. Here, we intended to identify a set of urine markers able to discriminate patients with early-stage PDAC from healthy individuals. Patients and methodsSeventy-five urine samples from PDAC patients and 50 healthy controls were assayed using quantitative real-time PCR (qPCR). The chosen biomarkers were lymphatic vessel endothelial HA receptor (LYVE-1), regenerating islet-derived 1 alpha (REG1A), and trefoil factor family (TFF1). ResultsLYVE-1, REG1A, and TFF1 expression in PDAC proved to be significantly elevated compared to healthy individuals (p < 0.05). Determination of these markers' expression might be useful for early tumor diagnosis with a sensitivity of 96 %, 100 %, and 73.33 % respectively, and a specificity of 100 %, 82 %, and 100 % respectively. ConclusionWe have recognized three diagnostic biomarkers REG1A, TFF1, and LYVE1 that can detect patients with early-stage pancreatic cancer in non-invasive urine specimens with improved sensitivity and specificity. To the best of our knowledge, there have been no prior investigations examining the mRNA expression levels of them in urine within the Egyptian population.

Construction of prognostic markers for pancreatic adenocarcinoma based on mitochondrial fusion-related genes.

Early detection of pancreatic adenocarcinoma (PAAD) remains a pressing clinical problem. Information on the clinical prognostic value of mitochondrial fusion-related genes in PAAD remains limited. In this study, we investigated mitochondrial fusion-related genes of PAAD to establish an optimal signature plate for the early diagnosis and prognosis of PAAD. The cancer genome atlas database was used to integrate the Fragments Per Kilobase Million data and related clinical data for patients with PAAD. Least absolute shrinkage and selection operator regression, cox regression, operating characteristic curves, and cBioPortal database was used to evaluate model performance, assess the prognostic ability and sensitivity. The levels of immune infiltration were compared by CIBERSORT, QUANTISEQ, and EPIC. Chemotherapy sensitivity between the different risk groups was compared by the Genomics of Drug Sensitivity in Cancer database and the "pRRophetic" R package. At last, a total of 4 genes were enrolled in multivariate Cox regression analysis. The risk-predictive signature was constructed as: (0.5438 × BAK1) + (-1.0259 × MIGA2) + (1.1140 × PARL) + (-0.4300 × PLD6). The area under curve of these 4 genes was 0.89. Cox regression analyses indicates the signature was an independent prognostic indicator (P < .001, hazard ratio [HR] = 1.870, 95% CI = 1.568-2.232). Different levels of immune cell infiltration in the 2 risk groups were observed using the 3 algorithms, with tumor mutation load (P = .0063), tumor microenvironment score (P = .01), and Tumor Immune Dysfunction and Exclusion score (P = .0012). The chemotherapeutic sensitivity analysis also revealed that the half-maximal inhibitory concentration of 5-fluorouracil (P = .0127), cisplatin (P = .0099), docetaxel (P < .0001), gemcitabine (P = .0047), and pacilataxel (P < .0001) were lower in the high-risk groups, indicating that the high-risk group patients had a greater sensitivity to chemotherapy. In conclude, we established a gene signature plate comprised of 4 mitochondrial fusion related genes to facilitate early diagnosis and prognostic prediction of PAAD.

Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods.

To evaluate robustness of a radiomics-based support vector machine (SVM) model for detection of visually occult PDA on pre-diagnostic CTs by simulating common variations in image acquisition and radiomics workflow using image perturbation methods. Eighteen algorithmically generated-perturbations, which simulated variations in image noise levels (σ, 2σ, 3σ, 5σ), image rotation [both CT image and the corresponding pancreas segmentation mask by 45° and 90° in axial plane], voxel resampling (isotropic and anisotropic),gray-level discretization [bin width (BW) 32 and 64)], and pancreas segmentation (sequential erosions by 3, 4, 6, and 8 pixels and dilations by 3, 4, and 6 pixels from the boundary), were introduced to the original (unperturbed) test subset (n = 128; 45 pre-diagnostic CTs, 83 control CTs with normal pancreas). Radiomic features were extracted from pancreas masks of these additional test subsets, and the model's performance was compared vis-a-vis the unperturbed test subset. The model correctly classified 43 out of 45 pre-diagnostic CTs and 75 out of 83 control CTs in the unperturbed test subset, achieving 92.2% accuracy and 0.98 AUC. Model's performance was unaffected by a three-fold increase in noise level except for sensitivity declining to 80% at 3σ (p = 0.02). Performance remained comparable vis-a-vis the unperturbed test subset despite variations in image rotation (p = 0.99), voxel resampling (p = 0.25-0.31), change in gray-level BW to 32 (p = 0.31-0.99), and erosions/dilations up to 4 pixels from the pancreas boundary (p = 0.12-0.34). The model's high performance for detection of visually occult PDA was robust within a broad range of clinically relevant variations in image acquisition and radiomics workflow.

Early detection of pancreatic adenocarcinoma

Early detection of pancreatic adenocarcinoma

Coupling magnetic levitation of graphene oxide–protein complexes with blood levels of glucose for early detection of pancreatic adenocarcinoma

IntroductionPancreatic adenocarcinoma (PDAC) has a poor prognosis since often diagnosed too late. Dyslipidemia and hyperglycemia are considered risk factors, but the presence of the tumor itself can determine the onset of these disorders. Therefore, it is not easy to predict which subjects with diabetes or dyslipidemia will develop or have already developed PDAC. Over the past decade, tests based on the use of nanotechnology, alone or coupled with common laboratory tests (e.g., hemoglobin levels), have proven useful in aiding the diagnosis of PDAC. Tests based on magnetic levitation (MagLev) have demonstrated high diagnostic accuracy in compliance with the REASSURED criteria. Here, we aimed to assess the ability of the MagLev test in detecting PDAC when coupled with the blood levels of glycemia, cholesterol, and triglycerides.MethodsBlood samples from 24 PDAC patients and 22 healthy controls were collected. Human plasma was let to interact with graphene oxide (GO) nanosheets and the emerging coronated systems were put in the MagLev device. Outcomes from Maglev experiments were coupled to glycemia, cholesterol, and triglycerides levels. Linear discriminant analysis (LDA) was carried out to evaluate the classification ability of the test in terms of specificity, sensitivity, and global accuracy. Statistical analysis was performed with Matlab (MathWorks, Natick, MA, USA, Version R2022a) software.ResultsThe positions of the levitating bands were measured at the starting point (i.e., as soon as the cuvette containing the sample was subjected to the magnetic field). Significant variations in the starting position of levitating nanosystems in controls and PDACs were detected. The combination of the MagLev outcomes with the blood glycemic levels returned the best value of global accuracy (91%) if compared to the coupling with those of cholesterol and triglycerides (global accuracy of ~ 77% and 84%, respectively).ConclusionIf confirmed by further studies on larger cohorts, a multiplexed Maglev-based nanotechnology-enabled blood test could be employed as a screening tool for PDAC in populations with hyperglycemia.

Multianalyte Serum Biomarker Panel for Early Detection of Pancreatic Adenocarcinoma.

We determined whether a large, multianalyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC). We defined a biologically relevant subspace of blood analytes on the basis of previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, and 182 early-stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects on the basis of their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects. A classification model was trained on 669 subjects (358 healthy, 159 benign, and 152 early-stage PDAC). Model evaluation on a hold-out test set of 168 subjects (103 healthy, 35 benign, and 30 early-stage PDAC) yielded an area under the receiver operating characteristic curve (AUC) of 0.920 for classification of PDAC from non-PDAC (benign and healthy controls) and an AUC of 0.944 for PDAC versus healthy controls. The algorithm was then validated in 146 subsequent cases presenting with pancreatic disease (73 benign pancreatic disease and 73 early- and late-stage PDAC cases) and 40 healthy control subjects. The validation set yielded an AUC of 0.919 for classification of PDAC from non-PDAC and an AUC of 0.925 for PDAC versus healthy controls. Individually weak serum biomarkers can be combined into a strong classification algorithm to develop a blood test to identify patients who may benefit from further testing.

A retrospective preliminary study of intrapancreatic late enhancement as a noteworthy imaging finding in the early stages of pancreatic adenocarcinoma.

To characterize intrapancreatic late enhancement (ILE) observed in the early stages of pancreatic adenocarcinoma (PAC). Among 203 patients pathologically diagnosed with PAC between October 2011 and February 2021, 32 patients with pre-diagnostic abdominal contrast-enhanced CT performed from 6months to 5years before the diagnosis were enrolled in this study. Indirect findings (IFs) on pre-diagnostic CT, including ILE, were evaluated and examined for various clinical data and time intervals to diagnosis (TIDs). The detected ILE was quantitatively evaluated, and the effect of ILE awareness on lesion detection by two radiologists and their interobserver agreement were assessed. Among the 32 patients, 23 showed IFs. ILE was observed in 14 patients (63%), with a median TID of 17months (interquartile ratio [IQR]: 9.3-42.3). ILE alone was observed in eight patients (35%), ILE with focal pancreatic parenchymal atrophy (FPPA) was observed in five patients (22%), and ILE with main pancreatic duct abnormalities (MPDA) was observed in one patient (4%). Pancreatic head lesions were significantly more frequent in patients with ILE alone than in patients with FPPA or MPDA (p = 0.026). The median long-axis diameters of the region with ILE and ILE-to-pancreas contrast were 10 (IQR: 5-11) mm and 24 (IQR: 17-33) HU, respectively. Awareness of ILE led observers to detect two or three more pancreatic head lesions, and interobserver agreement increased from poor agreement (k = 0.17) to moderate agreement (k = 0.55). ILE is a significant IF for early PAC detection. • Intrapancreatic late enhancement (ILE) is a significant indirect finding in the early detection of pancreatic adenocarcinoma. • ILE without other indirect findings is expected to help detect pancreatic head lesions. • Image evaluation focusing on ILE can increase lesion detection and improve the interobserver agreement.

Early detection of pancreatic adenocarcinoma in a rish-risk individual: the importance of the Italian Registry promoted by AISP.

Early detection of pancreatic adenocarcinoma in a rish-risk individual: the importance of the Italian Registry promoted by AISP.

An Improved K-Means Clustering for Segmentation of Pancreatic Tumor from CT Images

Pancreatic tumor is a deadly disease in which cancerous (malignant) cells form in the tissues of the pancreas. There are numerous types of pancreatic cancers, the most common is pancreatic adenocarcinoma. Almost 90% cases suffer from pancreatic adenocarcinoma. However detecting lesions from medical images is very challenging, given that the shape of the pancreas in abdomen is very irregular, low contrast in edges, variability in location which requires more complex and accurate segmentation of boundary of pancreas from abdomen images. In computer-aided diagnosis system, automatic segmentation of a specific organ and its tumor is very important. The proposed framework provides a way to apply K-means clustering method (unsupervised learning algorithm) on pancreas CT image to detect the area of interest from the background. Our aim is to build an efficient framework to segment tumors from pancreas CT images which is most commonly used in the domain of medical imaging as well as help clinicians in better decision making for surgical planning. It is observed from the results that K-means clustering segmentation algorithm produces more accuracy on CT imaging datasets. Early detection of pancreatic adenocarcinoma helps in immediate treatment planning for individuals.

Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising.

Pancreatic ductal adenocarcinoma (PDAC) is a worldwide public health concern. Despite extensive research efforts toward improving diagnosis and treatment, the 5-year survival rate at best is approximately 15%. This dismal figure can be attributed to a variety of factors including lack of adequate screening methods, late symptom onset, and treatment resistance. Pancreatic ductal adenocarcinoma remains a grim diagnosis with a high mortality rate and a significant psy-chological burden for patients and their families. In recent years artificial intelligence (AI) has permeated the medical field at an accelerated pace, bringing potential new tools that carry the promise of improving diagnosis and treatment of a variety of diseases. In this review we will summarize the landscape of AI in diagnosis and treatment of PDAC.

Retrospective evaluation of venous phase contrast-enhanced computed tomography images in patients who developed pancreatic adenocarcinomas after treatment for nonpancreatic primary cancer.

Objectives:To clarify venous phase contrast-enhanced CT findings in early pancreatic adenocarcinomas by retrospectively evaluating CT images of pancreatic adenocarcinomas that developed during follow-up after treatment for non-pancreatic cancers.Methods:The study cohort comprised six patients who developed pancreatic adenocarcinomas between April 2005 and April 2020 during follow-up after treatment for non-pancreatic primary cancers. Two radiologists retrospectively evaluated CT images and reached consensus on previously reported CT findings that were suggestive of small pancreatic adenocarcinomas; namely pancreatic duct interruption and dilatation, pancreatic parenchymal atrophy, focal hypoattenuated areas, and appearance of cystic lesions. Time intervals between the first CT with these suggestive findings and the latest pre-operative CT were recorded. Doubling times were calculated in patients with hypoattenuated areas on initial CT scans.Results:Small (<10 mm) focal hypoattenuated areas with (n = 2) or without rim enhancement (n = 1) were identified on initial CT images of three patients. Pancreatic duct interruption and dilatation, pancreatic parenchymal atrophy, and cystic lesion were identified in two, one and one patient, respectively. Time intervals between initial and latest preoperative CT examination were 6–19 months (median, 14.5 months). Tumor doubling time according to CT findings was calculated as 46–407 days (median 106 days).Conclusion:Venous phase contrast-enhanced CT can provide findings that are suggestive of early pancreatic adenocarcinoma. Pancreatic phase contrast-enhanced CT should therefore be performed in patients with such findings with the aim of early detection of pancreatic adenocarcinoma.Advances in knowledge:Pancreatic adenocarcinoma can develop subsequently in patients with non-pancreatic malignancies. Patients with non-pancreatic cancers are often followed up with monophasic contrast-enhanced CT in venous phase timing. Venous phase contrast-enhanced CT can provide some findings suggestive of early pancreatic adenocarcinoma. Knowledge of these findings is important for early detection of pancreatic adenocarcinoma.

Incidentally detected pancreatic adenocarcinomas on computed tomography obtained during the follow-up for other diseases.

To determine imaging findings of pancreatic adenocarcinomas incidentally detected on contrast-enhanced multiphasic dynamic computed tomography (CT) obtained during the follow-up for other diseases. From January 2007 to December 2018, 14 patients with pancreatic adenocarcinomas incidentally detected on CT obtained during the follow-up for other diseases (incidental group) and 105 patients with pancreatic adenocarcinomas symptomatically detected on ultrasound or CT (non-incidental group) were included. Imaging characteristics of the tumor were compared between the two groups. Additionally, imaging findings prior to the detection of a tumor on previous CT images in the incidental group were also assessed. In cancers of the pancreas body/tail, there was a significantly smaller tumor size (median, 17mm vs. 42mm, p < 0.001), a significantly lower incidence of loss of fatty marbling (p = 0.025), vascular involvement (p < 0.001), lymph node metastasis (p = 0.046) and distant metastasis (p = 0.017), and a significantly higher incidence of preserved lobulation (p < 0.001) in the incidental group than in the non-incidental group. Regarding the cancers of the pancreas head, there were no significant differences in the radiological findings between the two groups. On previous CT images, small pancreatic nodules, secondary signs, and loss of fatty marbling tended to be the preceding findings of incidental pancreatic adenocarcinomas. Incidentally detected pancreatic adenocarcinomas in the pancreas body/tail were characterized by an earlier tumor stage than in cases of symptomatically detected pancreatic adenocarcinoma. Several CT findings prior to the detection of a tumor may be useful for the early detection of pancreatic adenocarcinoma during the follow-up for other diseases.

Abstract 1622: Role of 3d volumetric and perfusion imaging for detecting early changes in pancreatic adenocarcinoma

Abstract Introduction: The aggressive nature of pancreatic cancer makes it one of the leading causes of cancer-related deaths worldwide. Survival rates remain low with standard therapy. Pancreatic adenocarcinoma accounts for nearly 90% of pancreatic malignancies, and early detection could have a major impact on disease prevention and early treatment. This study assesses the utility of CT perfusion imaging and 3D modeling for the early detection of pancreatic adenocarcinoma, as well as its role in monitoring treatment response. Methods: This study was a collaboration approved by the City of Hope and Honor Health institutional review boards. Thirty patients diagnosed with pancreatic adenocarcinoma who initiated treatment between 2015- 2018 were selected. Six patients treated with Neoadjuvant and Standard of care (SOC) therapy at Honor Health &amp; 24 patients received SOC therapy alone at City of Hope. Multiphase CT imaging was performed with 2.5mm slices according to standard imaging protocol, and reconstructed into 0.625 x 1.25mm slices for 3D post-processing. Images from the picture archiving and communication system (PACS) were uploaded to advanced imaging software (GE Advantage Workstation 3.2) for volumetric analysis. 3D tumor volume and perfusion studies before and during treatment were used to detect early changes in pancreatic cancer and assess response to treatment. Results: Measured 3D tumor volume correlated with disease burden, and decreased with clinical response to treatment. Change in arterial vs venous phase perfusion indicates the aggressivity of tumor biologics and potential early detection imaging biomarker in pancreatic adenocarcinoma. Serial imaging in patients receiving combination neoadjuvant and SOC therapy showed decreases in tumor volume and venous vasculature, while imaging in patients receiving standard therapy showed variable responses during treatment. Conclusion: 3D volumetric imaging biomarker is one of the clinical outcome variables which define the treatment response on follow-up imaging in both neoadjuvant therapies &amp; SOC alone. Results of our pilot study suggest that change in 3D volume and perfusion are important imaging markers that correlate with tumor progression vs regression &amp; define the aggressivity of the tumor. Larger studies are needed to validate the utility of imaging-based volume and perfusion analysis for detecting early changes in pancreatic cancer. Special thanks to: 1. made possible by the Kemper and Ethel Marley Foundation 2. Stand Up To Cancer-care Research UK-Lustgarten Foundation Pancreatic Cancer Dream Team Research Grant ( Grant number SU2C-AACR-DT-20-16) Citation Format: Syed Rahmanuddin, Erkut Borazanci, Vincent Chung, Derek Cridebring, Ronald Korn, Joyce Ho, William Boswell, Daniel Von Hoff. Role of 3d volumetric and perfusion imaging for detecting early changes in pancreatic adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1622.

Epidemiology, national trends and clinical associations of pancreatic adenocarcinoma in chronic pancreatitis patients: A nationwide inpatient sample (NIS) study.

e15737 Background: Chronic pancreatitis (CP) is a known risk factor for pancreatic adenocarcinoma (PA). The aim of this study was to evaluate epidemiology, national trends and clinical associations of PA in hospitalized US patients with CP using the NIS database. Methods: ICD-9 CM codes were used to identify patients with CP and PA between 2002-2014. Age, gender, race, mortality, length of stay (LOS) and commonly associated comorbidities were correlated. Statistical analysis was done using SPSS 25. Results: We identified 1,677,517 patients with CP between 2002-2014. Patients with concurrent PA comprised 1.3 % (21,832) of total patients with CP. During the study period, the prevalence of PA in CP patients remained nearly steady (1.2% in 2002 and 1.3% in 2014). Median LOS was 8 days and decreased from 10 days in 2002 to 6 days in 2014. Overall mortality was 3.5% during hospitalization with the trend of mortality not showing a significant change in the study period (4.2% in 2002 and 4.1% in 2014). Median cost of total hospitalization for CP with PA was 52,116 USD, almost three-folds higher than for CP alone (18,797 USD) and increased from 42,241 USD in 2002 to 55,525 USD in 2014. For patients with CP and PA, 87.5% were above 50-years-old; 55.6% were males; Caucasians and African-Americans represented 72.2% and 12.6% of patients respectively. Interestingly, factors less associated with PA in CP included alcohol (OR 0.27, 95% CI 0.26-0.29, P &lt; .0001), Diabetes Mellitus (DM) (OR 0.78, 95% CI 0.74-0.82, P &lt; 0.0001), Chronic hepatitis C (OR 0.56, 95%CI 0.48-0.66, P &lt; 0.0001) and pancreatic cysts and pseudocyst (OR 0.76, 95%, CI 0.69-0.83, P &lt; 0.0001). Tobacco use, metabolic syndrome, Chronic hepatitis B and H. Pylori infection were not statistically associated with PA. Conclusions: In hospitalized patients with CP, 1.3% had PA. During the study duration, trends of prevalence and mortality did not show significant change, median LOS decreased and hospitalization cost of charge increased. Risk factors for PA like alcohol, DM, chronic hepatitis C, and pancreatic cysts were less associated with PA in patients with CP. Future studies are needed for early detection of PA in patients with CP.

Intraductal Papillary Mucinous Neoplasm of the Pancreas as the Main Focus for Early Detection of Pancreatic Adenocarcinoma.

For early detection of pancreatic cancer, interests are now focused on the detection of high-risk individuals to undergo screening examinations. Intraductal papillary mucinous neoplasm (IPMN) is a unique dual precursor of pancreatic cancer, characterized by progression to invasive cancer and the development of pancreatic adenocarcinoma either concomitantly (synchronous occurrence) or even after resection of IPMN (metachronous occurrence). Careful examination and surveillance of patients with IPMN may therefore lead to early detection of pancreatic cancer. By reviewing only reports describing detailed breakdown of the morphological types of IPMN and numbers of patients with noninvasive and invasive carcinoma in each type, the rough frequencies of noninvasive carcinoma in main duct IPMNs and branch duct IPMNs (BD-IPMNs) are 20% and 10%, respectively, and those of invasive carcinoma are 40% and 13%, respectively. Roughly 5% of all patients with IPMN had concomitant adenocarcinoma. The real frequency of carcinoma in BD-IPMNs would be far lower because most patients with small asymptomatic BD-IPMNs do not undergo resection. Intraductal papillary mucinous neoplasm can be the main focus for early detection of pancreatic cancer to achieve favorable prognosis after surgical resection. The optimal protocol for surveillance and method for early detection of pancreatic cancer are to be determined.

Pancreatic adenocarcinoma protein-protein interaction network analysis.

Gene assessment of pancreatic adenocarcinoma disease via protein-protein interaction (PPI) Network Analysis. Diagnosis, especially early detection of pancreatic adenocarcinoma as a lethal disease implies more investigation. PPI Network Analysis is a suitable tool to discover new aspects of molecular mechanism of diseases. In the present study the related genes to pancreatic adenocarcinoma are studied in the interactome unit and the key genes are highlighted. The significant clusters were introduced by Cluster-ONE application of Cytoscape software 3.4.0. The genes are retrieved from STRING date base and analyzed by Cytoscape software. The crucial genes based on analysis of central parameters were determined and enriched by ClueGO v2.3.5 via gene ontology. The number of 24 key genes among 794 initial genes were highlighted as crucial nodes in relationship with pancreatic adenocarcinoma. All of the key genes were organized in a cluster including 216 nodes. The main related pathways and cancer diseases were determined. It was concluded that the introduced 24 genes are possible biomarker panel of pancreatic adenocarcinoma.

Predisposing factors for pancreatic adenocarcinoma: What is the role of imaging?

Early detection of pancreatic adenocarcinoma is the goal of imaging, enabling curative surgery. The identification of high-grade dysplastic precursor lesions is even more beneficial. Two forms are now better known: pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasm (IPMN). To detect these lesions with imaging, we need to know the patterns associated with them. A screening program could then be used to pinpoint them. This program could not be applied to the entire population. Identifying patients with an increased risk of pancreas adenocarcinoma is the first step of such screening.

Estimation of the Survival Benefit Obtainable From Screening for the Early Detection of Pancreatic Cancer.

The chance to improve survival from pancreatic adenocarcinoma relies on early diagnosis through screening, but any screening program is subject to lead-time bias and no data are available in this regard. Aim of the present study was to evaluate the benefit obtainable from a screening program for early detection of pancreatic adenocarcinoma, considering screen-related biases. Monte Carlo simulation was performed using data from 1000 pancreatic cancer patients admitted in a tertiary referral hospital and from pertinent literature. Lead-time bias was assessed and subtracted from expected survival. Mean expected life expectancy was 13.0 months. Assuming a 20%, 30%, or 50% stage III/IV reduction with screening, pancreatic resections would increase from 217 to 290 in front of a 20% stage III/IV reduction to 324 in front of a 30% reduction and to 385 in front of a 50% reduction. After lead-time adjustment, life expectancies were 14.0, 14.6, and 15.9 months, respectively. The number-needed-to-screen calculation suggests that screening can be harmful in a proportion of patients inversely dependent on the length of follow-up and a significant improvement of survival after diagnosis. Pancreatic adenocarcinoma screening program would probably be successful in the presence of a considerable improvement of postdiagnostic survival; otherwise, it only increases surgical procedure amount.

Gd(III)-Dithiolane Gold Nanoparticles for T1-Weighted Magnetic Resonance Imaging of the Pancreas.

Pancreatic adenocarcinoma has a 5 year survival of approximately 3% and median survival of 6 months and is among the most dismal of prognoses in all of medicine. This poor prognosis is largely due to delayed diagnosis where patients remain asymptomatic until advanced disease is present. Therefore, techniques to allow early detection of pancreatic adenocarcinoma are desperately needed. Imaging of pancreatic tissue is notoriously difficult, and the development of new imaging techniques would impact our understanding of organ physiology and pathology with applications in disease diagnosis, staging, and longitudinal response to therapy in vivo. Magnetic resonance imaging (MRI) provides numerous advantages for these types of investigations; however, it is unable to delineate the pancreas due to low inherent contrast within this tissue type. To overcome this limitation, we have prepared a new Gd(III) contrast agent that accumulates in the pancreas and provides significant contrast enhancement by MR imaging. We describe the synthesis and characterization of a new dithiolane-Gd(III) complex and a straightforward and scalable approach for conjugation to a gold nanoparticle. We present data that show the nanoconjugates exhibit very high per particle values of r1 relaxivity at both low and high magnetic field strengths due to the high Gd(III) payload. We provide evidence of pancreatic tissue labeling that includes MR images, post-mortem biodistribution analysis, and pancreatic tissue evaluation of particle localization. Significant contrast enhancement was observed allowing clear identification of the pancreas with contrast-to-noise ratios exceeding 35:1.

Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma.

Noninvasive biomarkers for early detection of pancreatic ductal adenocarcinoma (PDAC) are currently not available. Here, we aimed to identify a set of urine proteins able to distinguish patients with early-stage PDAC from healthy individuals. Proteomes of 18 urine samples from healthy controls, chronic pancreatitis, and patients with PDAC (six/group) were assayed using GeLC/MS/MS analysis. The selected biomarkers were subsequently validated with ELISA assays using multiple logistic regression applied to a training dataset in a multicenter cohort comprising 488 urine samples. LYVE-1, REG1A, and TFF1 were selected as candidate biomarkers. When comparing PDAC (n = 192) with healthy (n = 87) urine specimens, the resulting areas under the receiver-operating characteristic curves (AUC) of the panel were 0.89 [95% confidence interval (CI), 0.84-0.94] in the training (70% of the data) and 0.92 (95% CI, 0.86-0.98) in the validation (30% of the data) datasets. When comparing PDAC stage I-II (n = 71) with healthy urine specimens, the panel achieved AUCs of 0.90 (95% CI, 0.84-0.96) and 0.93 (95% CI, 0.84-1.00) in the training and validation datasets, respectively. In PDAC stage I-II and healthy samples with matching plasma CA19.9, the panel achieved a higher AUC of 0.97 (95% CI, 0.94-0.99) than CA19.9 (AUC = 0.88; 95% CI, 0.81-0.95, P = 0.005). Adding plasma CA19.9 to the panel increased the AUC from 0.97 (95% CI, 0.94-0.99) to 0.99 (95% CI, 0.97-1.00, P = 0.04), but did not improve the comparison of stage I-IIA PDAC (n = 17) with healthy urine. We have established a novel, three-protein biomarker panel that is able to detect patients with early-stage pancreatic cancer in urine specimens.