scholarly journals Role of 3D Volumetric and Perfusion Imaging for Detecting Early Changes in Pancreatic Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Syed Rahmanuddin ◽  
Ronald Korn ◽  
Derek Cridebring ◽  
Erkut Borazanci ◽  
Jordyn Brase ◽  
...  
Keyword(s):  
Early Detection ◽  
Pancreatic Adenocarcinoma ◽  
Perfusion Imaging ◽  
Early Stage ◽  
Risk Groups ◽  
Volumetric Analysis ◽  
Detection Methods ◽  
Ductal Adenocarcinoma ◽  
Imaging Biomarkers ◽  
3D Volume

PurposeThere is a major shortage of reliable early detection methods for pancreatic cancer in high-risk groups. The focus of this preliminary study was to use Time Intensity-Density Curve (TIDC) and Marley Equation analyses, in conjunction with 3D volumetric and perfusion imaging to demonstrate their potential as imaging biomarkers to assist in the early detection of Pancreatic Ductal Adenocarcinoma (PDAC).Experimental DesignsA quantitative retrospective and prospective study was done by analyzing multi-phase Computed Tomography (CT) images of 28 patients undergoing treatment at different stages of pancreatic adenocarcinoma using advanced 3D imaging software to identify the perfusion and radio density of tumors.ResultsTIDC and the Marley Equation proved useful in quantifying tumor aggressiveness. Perfusion delays in the venous phase can be linked to Vascular Endothelial Growth Factor (VEGF)-related activity which represents the active part of the tumor. 3D volume analysis of the multiphase CT scan of the patient showed clear changes in arterial and venous perfusion indicating the aggressive state of the tumor.ConclusionTIDC and 3D volumetric analysis can play a significant role in defining the response of the tumor to treatment and identifying early-stage aggressiveness.

scholarly journals MicroRNA-21 Is Induced Early in Pancreatic Ductal Adenocarcinoma Precursor Lesions

2010 ◽  
Vol 56 (4) ◽  
pp. 603-612 ◽  
Author(s):  
Maël Chalret du Rieu ◽  
Jérôme Torrisani ◽  
Janick Selves ◽  
Talal Al Saati ◽  
Anny Souque ◽  
...  
Keyword(s):  
Early Detection ◽  
Mouse Model ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Early Stage ◽  
Survival Rates ◽  
Ductal Adenocarcinoma ◽  
Early Event ◽  
Precursor Lesions ◽  
Tissue Samples ◽  
Ductal Cells

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) has the poorest overall prognosis among gastrointestinal cancers; however, curative resection in early-stage PDAC greatly improves survival rates, indicating the importance of early detection. Because abnormal microRNA production is commonly detected in cancer, we investigated noninvasive precursor pancreatic intraepithelial neoplasia (PanIN) lesions for microRNA production as a potential early biomarker of PDAC. Methods: Pathologists identified and classified ductal lesions. We extracted total RNA from laser-capture microdissected PanIN tissue samples from a conditional KRAS(G12D) mouse model (n = 29) or of human origin (n = 38) (KRAS is v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog). MicroRNA production was quantified by quantitative real-time PCR. Internal controls included 5S and U6 RNAs. Results: Production of microRNAs miR-21, miR-205, and miR-200 paralleled PanIN progression in the KRAS(G12D) mouse model, compared with microRNA production in samples of nonpathologic ducts. miR-21 demonstrated the highest relative concentrations in the precursor lesions. Interestingly, miR-205 and miR-21 up-regulation preceded phenotypic changes in the ducts. The production of microRNAs miR-21, miR-221, miR-222, and let-7a increased with human PanIN grade, with peak production occurring in hyperplastic PanIN-2/3 lesions. In situ hybridization analysis indicated miR-21 production to be concentrated in pathologic ductal cells. miR-21 production was regulated by KRAS(G12D) and epidermal growth factor receptor in PDAC-derived cell lines. Conclusions: Aberrant microRNA production is an early event in the development of PanIN. Our findings indicate that miR-21 warrants further investigation as a marker for early detection of PDAC.

scholarly journals COVID-19: molecular and serological detection methods

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10180 ◽  
Author(s):  
Ahmed E. Dhamad ◽  
Muna A. Abdal Rhida
Keyword(s):  
Early Detection ◽  
Current Knowledge ◽  
Early Stage ◽  
Diagnostic Methods ◽  
World Health ◽  
Detection Methods ◽  
Gold Standard Method ◽  
Advantages And Disadvantages ◽  
The World ◽  
Health Organization

Since COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared as a pandemic disease by the World Health Organization in early 2020, many countries, organizations and companies have tried to find the best way to diagnose the virus and contain its spreading. SARS-CoV-2 is a positive-sense single RNA (+ssRNA) coronavirus and mainly spreads through droplets, respiratory secretions, and direct contact. The early detection of the virus plays a central role in lowering COVID19 incidents and mortality rates. Thus, finding a simple, accurate, cheap and quick detection approach for SARS-CoV-2 at early stage of the viral infection is urgent and at high demand all around the world. The Food and Drug Administration and other health agencies have declared Emergency Use Authorization to develop diagnostic methods for COVID-19 and fulfill the demand. However, not all developed methods are appropriate and selecting a suitable method is challenging. Among all detection methods, rRT-PCR is the gold standard method. Unlike molecular methods, serological methods lack the ability of early detection with low accuracy. In this review, we summarized the current knowledge about COVID-19 detection methods aiming to highlight the advantages and disadvantages of molecular and serological methods.

scholarly journals The Impact of Biomarkers in Pancreatic Ductal Adenocarcinoma on Diagnosis, Surveillance and Therapy

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 217
Author(s):  
Niklas Sturm ◽  
Thomas J. Ettrich ◽  
Lukas Perkhofer
Keyword(s):  
Early Detection ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Early Stage ◽  
Therapy Response ◽  
Ductal Adenocarcinoma ◽  
Current State ◽  
Novel Biomarkers ◽  
State Of Research ◽  
Noninvasive Biomarkers ◽  
The Impact

Pancreatic ductal adenocarcinoma (PDAC) is still difficult to treat due to insufficient methods for early diagnosis and prediction of therapy response. Furthermore, surveillance after curatively intended surgery lacks adequate methods for timely detection of recurrence. Therefore, several molecules have been analyzed as predictors of recurrence or early detection of PDAC. Enhanced understanding of molecular tumorigenesis and treatment response triggered the identification of novel biomarkers as predictors for response to conventional chemotherapy or targeted therapy. In conclusion, progress has been made especially in the prediction of therapy response with biomarkers. The use of molecules for early detection and recurrence of PDAC is still at an early stage, but there are promising approaches in noninvasive biomarkers, composite panels and scores that can already ameliorate the current clinical practice. The present review summarizes the current state of research on biomarkers for diagnosis and therapy of pancreatic cancer.

scholarly journals Molecular Approaches Using Body Fluid for the Early Detection of Pancreatic Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 375
Author(s):  
Kennichi Satoh
Keyword(s):  
Early Detection ◽  
Invasive Carcinoma ◽  
Early Stage ◽  
Genetic Alterations ◽  
Disease Stage ◽  
Ductal Adenocarcinoma ◽  
Genetic Changes ◽  
Non Invasive ◽  
Molecular Approaches ◽  
Cause Of Deaths

Pancreatic ductal adenocarcinoma (PDAC) is the most malignant form of gastrointestinal tumor and is the fourth leading cause of deaths due to cancer in Japan. This cancer shows a poor outcome due to the difficulty of its early diagnosis and its rapid growth. Once this disease becomes clinically evident, it is frequently accompanied by distant metastasis at the time of diagnosis. A recent multicenter study in Japan revealed that patients with the early stage of this disease (stage 0 and I) showed favorable prognosis after surgical resection, indicating the importance of early detection for improvement of PDAC prognosis. PDAC develops through a stepwise progression from the precursor lesion, and over the last few decades molecular analyses have shown the detailed genetic alterations that occur in this process. Since advances in molecular technologies have enabled the detection of genetic changes from a very small quantity of samples, a large number of non-invasive molecular approaches have been utilized in an attempt to find precursor or non-invasive carcinoma lesions. In this review, the current efforts in terms of the molecular approaches applied for the early detection of PDAC—especially using body fluids such as pancreatic juice, blood, and saliva—are summarized.

Circulating tumor DNA methylation as markers for early detection of pancreatic ductal adenocarcinoma (PDAC).

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 395-395
Author(s):  
Xiaoding Liu ◽  
Shiwei Guo ◽  
Chengcheng Ma ◽  
Yatong Li ◽  
Xiaoqian Liu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Early Detection ◽  
Circulating Tumor Dna ◽  
Detection Methods ◽  
Ductal Adenocarcinoma ◽  
Plasma Samples ◽  
Average Sensitivity ◽  
Reduced Representation ◽  
Non Invasive ◽  
Normal Plasma

395 Background: PDAC is a cancer of high mortality and low survival. Its early detection is critical due to symptoms often occur only at advanced stages. However there is no reliable screening tool to identify high-risk patients. ctDNA methylation has recently emerged as a promising new target to differentiate PDAC plasma from normal plasma for its early detection. Methods: Reduced representation bisulfite sequencing libraries were made in 46 PDAC tissues, 30 para-PDAC tissues and 20 PDAC plasmas to screen PDAC-specific markers, which was done by quantifying and comparing methylation levels of genomic regions and individual CpG sites between those groups. Markers were validated in plasma samples from 84 PDAC patients and 64 normal controls to propose a blood classifier. The best-performing markers were developed into a targeted sequencing panel, which was tested on a larger collection of plasma samples from patients of a variety of pancreatic diseases to build and validate a PDAC-predicting model. Results: We profiled genome-wide methylation patterns of tissues samples to identify 171 PDAC-specific markers. We reiterated training and cross-validating PDAC classification models using SVM method, and achieved an average sensitivity of 86% and specificity of 88%. To prove the feasibility of a non-invasive detection in plasma, a targeted methylation assay using those markers was tested on PDAC and normal plasmas, and yielded an average sensitivity of 68.4% and a specificity of 85.8%. We refined the panel by selecting the most discriminatory markers and built the version II of the panel, which was named PANcreatic Cancer Detection Assay, or PANDA, for a more efficient target capture, which was validated in an independent cohort of plasma samples that included 94 PDAC cases, 25 chronic pancreatitis (CP) cases and 80 normal samples from multiple centers. The PANDA achieved an AUC of 0.906 when classifying PDAC from normal, and an AUC of 0.882 when separating PDAC from CPs, both of which are more accurate than CA19-9, the conventional blood marker for PDAC. We further integrated test subjects’ age and their CA19-9 level as features into the PANDA model, which further elevated their AUC to 0.882 and 0.933 when classifying PDAC plasma from either CP plasma or normal plasma, respectively. Conclusions: We have developed PANDA, an NGS based target assay covering PDAC-specific DNA methylation targets by screening and validation on PDAC tissues and plasmas. Combined with age and CA19-9 blood level, PANDA has shown encouraging results to classify PDAC plasma from non-malignant diseases, demonstrating its potential to be optimized into non-invasive diagnostics for blood-based early PDAC screening.

Incidentally discovered pancreatic ductal adenocarcinoma: Is detection of asymptomatic cancer associated with better outcomes than symptomatic cancer?

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 243-243
Author(s):  
Yoshinori Takeda ◽  
Akio Saiura ◽  
Yoshihiro Mise ◽  
Takeaki Ishizawa ◽  
Yosuke Inoue ◽  
...  
Keyword(s):  
Early Detection ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Surgical Resection ◽  
Early Stage ◽  
Ductal Adenocarcinoma ◽  
Incomplete Resection ◽  
Long Term Outcomes ◽  
Potential Implication ◽  
Treatment Type

243 Background: The number of incidentally discovered asymptomatic pancreatic ductal adenocarcinoma (APDAC) has been increasing along with recent wide spread use of imaging studies in general practice. However, the clinical implication in early detection of asymptomatic pancreatic cancer remains yet to be determined. In this study, we reviewed our experience of patients with PDAC in high volume cancer center and compared the characteristics and long-term outcomes between those with APDAC and symptomatic PDAC (SPDAC). Methods: This retrospective study included 569 consecutive patients with PDAC initially treated in our institution from January 2007 to December 2012. Median follow-up period was 29 months for the survivors. Two hundred and fifty patients underwent surgical resection and 319 patients were deemed unresectable. The patient’s demographics, tumor locations, pathologic stages, and treatment type received, and the overall survival (OS) were compared between the patients with APDAC and those with SPDAC. Results: One hundred and sixty-three patients (29%) presented without any subjective symptoms. When compared with SPDAC, APDAC was associated with early stage (stage I, 6% vs. 1%, p<0.01). Among 163 patients with APDAC, 104 patients (64%) underwent surgical resection, while only 146 patients (36%) out of 406 SPDAC underwent resection ( p<0.01). The 5-year OS rate of the patients with APDAC was 18%, comparing with 7% for those with SPDAC ( p<0.01). Among the patients who underwent resection, the presence of symptoms did not affect the chance of incomplete resection (R1, 12% vs. 22% for patients with APDAC and SPDAC, respectively, p=0.06) and the 5-year OS rate (23% vs. 22%, p=0.09). However, the patients with SPDAC required complex operation (concomitant vascular resection and reconstruction 56% vs. 29% for those with APDAC, p<0.01). Conclusions: Asymptomatic PDAC is associated with better long-term outcomes than symptomatic PDAC due to early stage at presentation and higher chance of resectability. Our findings highlight the potential implication of screening program for early detection of PDAC for selected high-risk patient population.

scholarly journals Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach

2021 ◽  
Vol 3 (4) ◽  
pp. 353-364
Author(s):  
R. Kanthavel
Keyword(s):  
Early Detection ◽  
Crack Detection ◽  
Early Stage ◽  
Cost Effective ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Research Article ◽  
Effective Manner ◽  
Main Fracture ◽  
Fracture Identification

Recently, glass crack detection methods have been emerging in Artificial intelligence programming. The early detection of the crack in glass could save many lives. Glass fractures can be detected automatically using machine vision. However, this has not been extensively researched. As a result, a detection algorithm is a benefit to study the mechanics of glass cracking. To test the algorithm, benchmark data are used and analysed. According to the first findings, the algorithm is capable of figuring out the screen more or less correctly and identifying the main fracture structures with sufficient efficiency required for majority of the applications. This research article has addressed the early detection of glass cracks by using edge detection, which delivers excellent accuracy in fracture identification. Following the pre-processing stage, the CNN technique extracts additional characteristics from the input pictures that have been provided due to dense feature extraction. The "Adam" optimizer is used to update the bias weights of networks in a cost-effective manner. Early identification is achievable with high accuracy metrics when using these approaches, as shown in the findings and discussion part of this paper.

scholarly journals Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer (Preprint)

2020 ◽  
Author(s):  
José S Enriquez ◽  
Yan Chu ◽  
Shivanand Pudakalakatti ◽  
Kang Lin Hsieh ◽  
Duncan Salmon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Early Detection ◽  
Real Time ◽  
Unmet Need ◽  
Ductal Adenocarcinoma ◽  
Imaging Biomarkers ◽  
Non Invasive ◽  
Computational Tomography ◽  
Scoping Reviews

BACKGROUND There is an unmet need for non-invasive imaging markers that help identify the aggressive sub-type(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and to evaluate the efficacy of therapy prior to tumor reduction. In the last few years, there are two major developments that can have a significant impact in developing imaging biomarkers for PDAC: I) hyperpolarized metabolic Magnetic Resonance (HP-MR) and II) applications of Artificial Intelligence (AI). OBJECTIVE Our objective is to discuss these two exciting but independent developments in the realm of PDAC imaging and detection from the available literature to date. METHODS A systematic review following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines was conducted. The manuscript addressing the utilization of Hyperpolarization-based magnetic resonance (HP-MR) and/or Artificial Intelligence for early detection, assessing aggressiveness, and interrogating the early efficacy of therapy in PDAC cited in recent clinical guidelines were extracted from PubMed and Google Scholar. The studies were reviewed by reviewers following the exclusion and inclusion criteria and grouped based on the utilization of HP-MR and AI in PDAC diagnosis. RESULTS HP-MR increases the sensitivity of conventional MR by over 10,000-fold enabling real-time metabolic measurements. The utility of HP-MR in PDAC has been verified in several preclinical studies, but has not been proven in a clinical setting. In contrast, AI applications in PDAC imaging in the clinic are nascent, but mostly limited to Computational Tomography (CT) imaging datasets. CONCLUSIONS Combining AI and HP-MR applications may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating the early efficacy of therapy in PDAC.

scholarly journals Multi-Biomarker Panel Prediction Model for Diagnosis of Pancreatic Cancer: a retrospective and multi-center cohort study

2020 ◽  
Author(s):  
Doo-Ho Lee ◽  
Woongchang Yoon ◽  
Areum Lee ◽  
Youngmin Han ◽  
Yoonhyeong Byun ◽  
...  
Keyword(s):  
Prediction Model ◽  
Early Stage ◽  
Risk Groups ◽  
Pancreatic Disease ◽  
Training Data ◽  
Ductal Adenocarcinoma ◽  
Diagnostic Tools ◽  
Enzyme Linked Immunosorbent Assay ◽  
Data Set ◽  
Marker Panel

Abstract Background Early diagnosis is paramount in increasing the survival rate of pancreatic ductal adenocarcinoma (PDAC). However, effective early diagnostic tools are lacking at present. The current study aimed to develop a prediction model using a multi-marker panel (LRG1, TTR, and CA19-9) as a diagnostic screening tool for PDAC. Methods A large multi-center cohort of 1,991 samples were collected from January 2011 to September 2019, of which 609 are normal (NL), 145 are other cancer (OC; colorectal, thyroid, and breast cancer), 314 are pancreatic benign disease (PB), and 923 are PDAC. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers, LRG1, TTR, and CA 19 − 9. Using a logistic regression (LR) model trained on training data set, the predicted values for PDACs were obtained, and the result was classified into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and biomarkers TTR, CA 19 − 9, and LRG1. Results Participants were categorized into four groups as NL (n = 609, 30.6%), OC (n = 145, 7.3%), PB (n = 314, 15.7%), and PDAC (n = 923, 46.4%). The NL, OC, and PB groups were clubbed into the non-PDAC group (n = 1068, 53.6%). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. Conclusions This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing PDAC from normal and benign pancreatic disease states, as well as patients with other cancers. The study indicates that the introduced multi-marker panel prediction model for PDAC diagnosis can help guide medical decisions for patients, including patients with early stage PDAC or with normal levels of CA 19 − 9.

scholarly journals Validation of Serum Biomarkers That Complement CA19-9 in Detecting Early Pancreatic Cancer Using Electrochemiluminescent-Based Multiplex Immunoassays

Biomedicines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1897
Author(s):  
Jin Song ◽  
Lori J. Sokoll ◽  
Daniel W. Chan ◽  
Zhen Zhang
Keyword(s):  
Pancreatic Cancer ◽  
Early Detection ◽  
Clinical Performance ◽  
Early Stage ◽  
Ductal Adenocarcinoma ◽  
Ca 125 ◽  
Clinical Validation ◽  
Marker Panel ◽  
Mucinous Neoplasm ◽  
High Analytical Performance

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy; its early detection is critical for improving prognosis. Electrochemiluminescent-based multiplex immunoassays were developed with high analytical performance. All proteins were analyzed in sera of patients diagnosed with PDAC (n = 138), benign pancreatic conditions (111), and healthy controls (70). The clinical performance of these markers was evaluated individually or in combination for their complementarity to CA19-9 in detecting early PDAC. Logistic regression modeling including sex and age as cofactors identified a two-marker panel of CA19-9 and CA-125 that significantly improved the performance of CA19-9 alone in discriminating PDAC (AUC: 0.857 vs. 0.766), as well as early stage PDAC (0.805 vs. 0.702) from intraductal papillary mucinous neoplasm (IPMN). At a fixed specificity of 80%, the panel significantly improved sensitivities (78% vs. 41% or 72% vs. 59%). A two-marker panel of HE4 and CEA significantly outperformed CA19-9 in separating IPMN from chronic pancreatitis (0.841 vs. 0.501). The biomarker panels evaluated by assays demonstrated potential complementarity to CA19-9 in detecting early PDAC, warranting additional clinical validation to determine their role in the early detection of pancreatic cancer.

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