Detection Of Pancreatic Cancer Research Articles

S31 Cole Relaxation Frequency: A Parameter for Pancreatic Cancer Detection

Introduction: Pancreatic cancer (PC) accounts for half a million new cases and 4.7% of the world's cancer-related deaths in 2020. It is considered one of the most lethal malignancies with a global survival rate of only 5%. Innovative imaging and endoscopic techniques are being studied to improve the accuracy of PC detection. Endoscopic ultrasound (EUS) guided tissue acquisition is currently the gold standard for sampling pancreatic masses. EUS guided fine needle biopsy has been considered superior to fine needle aspiration, but both are limited by the subjectivity of endoscopists and invasiveness of the procedure. Nonetheless, at present, there is no standard procedure for PC detection. The Cole Relaxation Frequency (CRF) – a derived electrical bioimpedance signature for cancer detection proposed by NovaScan – has been proven to quantitatively detect cancer in breast, skin, and lung tissues. The aim of this pilot study was to determine if the CRF based NovaScan technology can detect cancer in pancreatic tissues. Methods: CRF was measured with NovaScan technology in multiple locations (Figure A) on the pancreas samples in a cohort of n=26 mice, of which 15 were KPC (model of pancreatic ductal adenocarcinoma), 2 were KC (model of pancreatic neoplasia), and 9 were wild type controls. NovaScan determined cancer presence when the CRF parameter was measured above 1 MHz (Figure:B,C). Outcomes were compared to histopathology results for each of these samples to determine the specificity and sensitivity of the NovaScan device. Results: From histology, 12 KPC pancreases were confirmed as cancerous, 9 controls were confirmed as noncancerous, while 5 pancreases (3 KPC and 2 KC) presented with pancreatic intraepithelial neoplasia (PanIN), a precancerous condition. NovaScan technology identified 4 out of 5 PanIN samples as cancerous. Considering the entire cohort (n=26), specificity and sensitivity were 100% and 94%, respectively. If PanIN samples were excluded, specificity and sensitivity were both 100% (n=21). Conclusion: Future development of NovaScan CRF based technology into a rapid onsite evaluation or an endoscopic device would provide an affordable means for accurate and quantitative PC detection that guides or substitutes pancreatic biopsy acquisition. Identification of malignant precursors indicates the technological capability of NovaScan’s device to detect early stage PCs. Further technology development could allow for the distinction between cancerous and precancerous lesions.Figure 1.: (A) KPC pancreas sample placed on Novascan’s electrode array for a series of spectral bioimpedance measurements. A zoom in of the electrode with a pancreas sample is also shown. White rectangles indicate multiple locations measured across the sample. Spectral impedance scans for a control mouse (B) and a KPC mouse (C). The examination of CRF peak properties was used for cancer identification in pancreas samples. For the control mouse the CRF peaks fall below the threshold of 1 MHz, determining no cancer. For the KPC mouse several scans have CRF peaks above the threshold of 1 MHz, determining cancer presence.

United Kingdom Early Detection Initiative (UK-EDI): protocol for establishing a national multicentre cohort of individuals with new-onset diabetes for early detection of pancreatic cancer

IntroductionPancreatic cancer is a leading cause of cancer deaths worldwide. Screening for this disease has potential to improve survival. It is not feasible, with current screening modalities, to screen the...

Classification of endoscopic ultrasound guided fine needle aspiration cytology of pancreatic space occupying lesionsby Papanicolaou Society of Cytopathology System: A five year study.

Majority of the pancreatic cancer patients present at an advanced stage and have poor 5year survival rate. Thus, there is a need for early detection of pancreatic cancer with the initiation of the therapy. This is a retrospective study including all the endoscopic ultrasound guided (EUS) guided pancreatic FNAs from 2016 to 2020. The aspirate smears were analyzed and classified according to The Papanicolaou Society of Cytopathology System for Reporting Pancreaticobiliary Cytology (PSCPC). A total of 245 EUS guided FNAs from pancreatic lesions were included. Cyto-histological correlation was done wherever available. Category I (non diagnostic) accounted for 40 cases (16%) cases, Category II (negative) comprised of 44 cases (18%); and Category III (Atypical) had 5 cases (2%). Category IV neoplastic-benign category included 3 cases of serous cystadenoma, while neoplastic-others category included pancreatic neuroendocrine tumors (n=21), solid pseudo-papillary neoplasms (SPEN) (n=12) and mucinous cystic neoplasms (n=4). A total of 7 cases (2.8%) were reported in Category V (Suspicious). A diagnosis of adenocarcinoma (Category VI) was rendered in 105 cases (42.8%) cases. Rarer types included non Hodgkins lymphoma (n=3) and one case of primary undifferentiated carcinoma with osteoclastic giant cells. Cyto-histological correlation in all categories was available in 58 cases with 8 false negative cases. Thus overall sensitivity of EUS guided FNAC was found to be 87.8% with a diagnostic yield of 83.6% while sensitivity in diagnosing adenocarcinoma was 96.9%. The present study highlights the spectrum of EUS guided FNA of pancreatic lesions in a subset of North Indian population and classified them according to PSCPC. EUS guided FNAC is a sensitive investigation which plays a crucial role in confirming the diagnosis of pancreatic space occupying lesions (SOLs)in advanced stage.

Multiplexed Detection of Pancreatic Cancer by Combining a Nanoparticle-Enabled Blood Test and Plasma Levels of Acute-Phase Proteins.

Simple SummaryIn this study, a multiplexed strategy based on the combination of a nanoparticle-enabled blood test and serum levels of acute-phase proteins proved to be able to distinguish pancreatic cancer patients from healthy controls with a good and sex-dependent prediction ability. This study suggests a possible role of acute-phase proteins as pancreatic cancer biomarkers and paves the way for the development of multiplexed technologies for early cancer detection.The development of new tools for the early detection of pancreatic ductal adenocarcinoma (PDAC) represents an area of intense research. Recently, the concept has emerged that multiplexed detection of different signatures from a single biospecimen (e.g., saliva, blood, etc.) may exhibit better diagnostic capability than single biomarkers. In this work, we develop a multiplexed strategy for detecting PDAC by combining characterization of the nanoparticle (NP)-protein corona, i.e., the protein layer that surrounds NPs upon exposure to biological fluids and circulating levels of plasma proteins belonging to the acute phase protein (APPs) family. As a first step, we developed a nanoparticle-enabled blood (NEB) test that employed 600 nm graphene oxide (GO) nanosheets and human plasma (HP) (5% vol/vol) to produce 75 personalized protein coronas (25 from healthy subjects and 50 from PDAC patients). Isolation and characterization of protein corona patterns by 1-dimensional (1D) SDS-PAGE identified significant differences in the abundance of low-molecular-weight corona proteins (20–30 kDa) between healthy subjects and PDAC patients. Coupling the outcomes of the NEB test with the circulating levels of alpha 2 globulins, we detected PDAC with a global capacity of 83.3%. Notably, a version of the multiplexed detection strategy run on sex-disaggregated data provided substantially better classification accuracy for men (93.1% vs. 77.8%). Nanoliquid chromatography tandem mass spectrometry (nano-LC MS/MS) experiments allowed to correlate PDAC with an altered enrichment of Apolipoprotein A-I, Apolipoprotein D, Complement factor D, Alpha-1-antichymotrypsin and Alpha-1-antitrypsin in the personalized protein corona. Moreover, other significant changes in the protein corona of PDAC patients were found. Overall, the developed multiplexed strategy is a valid tool for PDAC detection and paves the way for the identification of new potential PDAC biomarkers.

Urine biomarkers enable pancreatic cancer detection up to 2 years before diagnosis.

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1year before PDAC diagnosis (AUC=0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC=0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.

Usefulness of Practitioner-Led Pancreatic Cancer Screening.

The 5-year survival rate for pancreatic cancer has improved (10%) but remains worse than that for other cancers. Early pancreatic cancer diagnosis is challenging, and delayed diagnosis can delay treatment, which impairs survival. Practitioners do not promptly refer cases to a general hospital, causing delayed discovery. Herein, we aimed to examine the usefulness of the Pancreatic Cancer Project in Matsue, whose objective is to detect pancreatic cancer in patients presenting at any medical institution in Matsue City. Clinical data were extracted from medical records, and abdominal ultrasonography and tumor marker blood level assessments were performed (n = 234; median age, 71 [range, 41–94] years; 51% male). Cases with abnormal abdominal ultrasonography or blood test findings were referred for specialist imaging and followed up. The pancreatic cancer detection rate was 6.0% (n = 14); all cases were referred to a general hospital by practitioners within 1 month. Patients had stage IA (n = 1), IIA (n = 6), IIB (n = 2), III (n = 1), and IV (n = 4) disease. Overall, pancreatic cancer could be detected at an earlier stage (I–II), but referral to a general hospital by visiting practitioners should be prompt. The Pancreatic Cancer Project in Matsue may help improve the detection and prognosis of pancreatic cancer.

Nanotechnology Meets Oncology: A Perspective on the Role of the Personalized Nanoparticle-Protein Corona in the Development of Technologies for Pancreatic Cancer Detection.

In recent years nanotechnology has opened exciting opportunities in the struggle against cancer. In 2007 Dawson and coworkers demonstrated that nanomaterials exposed to biological fluids are coated with plasma proteins that form the so-called “protein corona”. A few years later our joint research team made of physicists, chemists, biotechnologists, surgeons, oncologists, and bioinformaticians introduced the concept of “personalized protein corona” and demonstrated that it is unique for each human condition. This concept paved the way for the development of nano-enabled blood (NEB) tests for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). These studies gave an impetus to serious work in the field that came to maturity in the late 2010s. In this special issue, we provide the reader with a comprehensive overview of the most significant discoveries of our research team in the field of PDAC detection. We focus on the main achievements with an emphasis on the fundamental aspects of this arena and how they shaped the integration of different scientific backgrounds towards the development of advanced diagnostic technologies. We conclude the review by outlining future perspectives and opportunities to transform the NEB tests into a reliable clinical diagnostic technology for early diagnosis, follow-up, and management of PDAC patients.

Preclinical Safety Evaluation of Intraperitoneally Administered Cu-Conjugated Anti-EGFR Antibody NCAB001 for the Early Diagnosis of Pancreatic Cancer Using PET.

Detecting tumor lesions <1 cm in size using current imaging methods remains a clinical challenge, especially in pancreatic cancer. Previously, we developed a method to identify pancreatic tumor lesions ≥3 mm using positron emission tomography (PET) with an intraperitoneally administered 64Cu-labeled anti-epidermal growth factor receptor (EGFR) antibody (64Cu-NCAB001 ipPET). Here, we conducted an extended single-dose toxicity study of 64Cu-NCAB001 ipPET in mice based on approach 1 of the current ICH M3 [R2] guideline, as our new drug formulation contains 45 μg of the antibody. We used NCAB001 labeled with stable copper isotope instead of 64Cu. The total content of size variants was approximately 6.0% throughout the study. The relative binding potency of Cu-NCAB001 to recombinant human EGFR was comparable to that of cetuximab. The general and neurological toxicities of Cu-NCAB001 ipPET at 62.5 or 625 μg/kg were assessed in mice. The no-observed-adverse-effect level of Cu-NCAB001 was 625 μg/kg, a dose approximately 1000-fold higher at the μg/kg level than the dose of 64Cu-NCAB001 in our formulation (45 µg). The size variants did not affect the safety of the formulation. Therefore, clinical studies on the efficacy of 64Cu-NCAB001 ipPET for early detection of pancreatic cancer using PET imaging can be safely conducted.

Investigation of key signaling pathways and appropriate diagnostic biomarkers selection between non-invasive to invasive stages in pancreatic cancer: a computational observation.

Pancreatic cancer is the seventh most lethal cancer in the world. Despite its moderate prevalence, the 5-year survival rate of patients with pancreatic cancer is about 10%. Despite different therapeutic and diagnostic strategies for pancreatic cancer, this cancer is still uncontrollable in the invasive stage and can invade various body organs and cause death. Early detection for pancreatic cancer can be an excellent solution to manage treatment better and increase patients' survival rates. This study aimed to find diagnostic biomarkers between non-invasive to invasive stages of pancreatic cancer in the extracellular matrix to facilitate the early diagnosis of this cancer. Using bioinformatics analysis, we selected the appropriate datasets between non-invasive and invasive pancreatic cancer stages and categorized their genes. Then, we charted and confirmed the signaling pathways, gene ontology, protein relationships, and protein expression levels in the human samples using bioinformatics databases. Cell adhesion and hypoxia signaling pathways were observed in up-regulated genes, different phases of the cell cycle, and metabolic signaling pathways with down-regulated genes between non-invasive and invasive pancreatic cancer stages. For proper diagnostic biomarkers selection, the overexpressed genes that released protein into the extracellular matrix were examined in more detail, with 62 proteins selected and SPARC, THBS2, COL11A1, COL1A1, COL1A2, COL3A1, SERPINH1, PLAU proteins chosen. Bioinformatics analysis can more accurately assess the relationship between molecular mechanisms and key actors in pancreatic cancer invasion and metastasis to facilitate early detection and improve treatment management for patients with pancreatic cancer.

Cost-effectiveness of MicroRNA for Pancreatic Cancer Screening in Patients With Diabetes

The study aimed to assess the cost-effectiveness of microRNA compared with carbohydrate antigen 19-9, abdominal ultrasound, magnetic resonance imaging, endoscopic ultrasound, computed tomography, positron emission tomography, and no screening for pancreatic cancer (PC) screening in patients with diabetes. We developed a state-transition model from a health care payer perspective and a lifetime horizon. We targeted 3 hypothetical cohorts of patients with long-standing type 2 diabetes (LSD), new-onset diabetes (NOD), and LSD having intraductal papillary mucinous neoplasm (IPMN), aged 40, 50, 60, and 70 years. The main outcomes were costs, quality-adjusted life-years, life expectancy life-years, incremental cost-effectiveness ratios, and deaths from PC. In the base-case analysis, abdominal ultrasound in patients with LSD and microRNA in patients with NOD and LSD having IPMN were the most cost-effective for all age groups. Cost-effectiveness was sensitive to PC incidence and the cost of microRNA. Probabilistic sensitivity analysis showed that microRNA was 42% to 54% cost-effective for NOD and 76% to 78% cost-effective for LSD having IPMN at a willingness-to-pay level of US $100,000 per quality-adjusted life-year gained. MicroRNA prevented 30,641 PC deaths in diabetic patients compared with no screening. In patients with NOD and LSD having IPMN, microRNA-based PC screening is cost-effective and recommended for early PC detection.

Automatic pancreas segmentation using ResNet-18 deep learning approach

The accurate pancreas segmentation process is essential in the early detection of pancreatic cancer. The pancreas is situated in the abdominal cavity of the human body. The abdominal cavity contains the pancreas, liver, spleen, kidney, and adrenal glands. Sharp and smooth detection of the pancreas from this abdominal cavity is a challenging and tedious job in medical image investigation. Top-down approaches like Novel Modified K-means Fuzzy clustering algorithm (NMKFCM), Scale Invariant Feature Transform (SIFT), Kernel Density Estimator (KDE) algorithms were applied for pancreas segmentation in the early days. Recently, Bottom-up method has become popular for pancreas segmentation in medical image analysis and cancer diagnosis. LevelSet algorithm is used to detect the pancreas from the abdominal cavity. The deep learning, bottom-up approach performance is better than another. Deep Residual Network (ResNet-18) deep learning, bottom-up approach is used to detect accurate and sharp pancreas from CT scan medical images. 18 layers are used in the architecture of ResNet-18. The automatic pancreas and kidney segmentation is accurately extracted from CT scan images. The proposed method is applied to the medical CT scan images dataset of 82 patients. 699 images and 150 images with different angles are used for training and testing purposes, respectively. ResNet-18 attains a dice similarity index value up to 98.29±0.63, Jaccard Index value up to 96.63±01.25, Bfscore value up to 84.65±03.96. The validation accuracy of the proposed method is 97.01%, and the loss rate value achieves up to 0.0010. The class imbalance problem is solved by class weight and data augmentation.

Diagnostic and Therapeutic Indications for Endoscopic Ultrasound (EUS) in Patients with Pancreatic and Biliary Disease-Novel Interventional Procedures.

There is growing evidence supporting the substantial, essential and indispensable role of endoscopic ultrasound (EUS) as a key diagnostic armamentarium for upper GI oncologic surgery. Well described in countless publications, EUS holds that position in gastroenterological expert centers all over Europe. Despite its undisputable contributions to oncologic upper GI surgery, the availability of this technique at the expert level shows up in an irregular spread pattern. Endoscopic ultrasound’s primary use during the first few years after its creation was the detection of pancreatic cancer. From then on, EUS developed in different directions, becoming a diagnostic tool that increasingly better defines its status as a method of minimally invasive therapeutic applications and a useful addition to surgical options. As a result, several surgical interventions could even be replaced by ultrasound-targeted interventions. This process took place in just a few years and was made possible by technical development that sensibly combined high-resolution ultrasound with therapeutic endoscopy. The present article will serve to cover the most prevalent uses with supporting data considering the growing list of suggested indications for EUS while also examining cutting-edge initiatives that might soon become the standard of clinical practice. Endoscopic centers with high expertise are needed to train future experts in the growing field of EUS interventions.

Identification of novel early pancreatic cancer biomarkers KIF5B and SFRP2 from “first contact” interactions in the tumor microenvironment

BackgroundPancreatic cancer is one of the most difficult cancers to detect early and most patients die from complications arising due to distant organ metastases. The lack of bona fide early biomarkers is one of the primary reasons for late diagnosis of pancreatic cancer. It is a multifactorial disease and warrants a novel approach to identify early biomarkers.MethodsIn order to characterize the proteome, Extracellular vesicles (EVs) isolated from different in vitro conditions mimicking tumor-microenvironment interactions between pancreatic cancer epithelial and stromal cells were analyzed using high throughput mass spectrometry. The biological activity of the secreted EVome was analyzed by investigating changes in distant organ metastases and associated early changes in the microbiome. Candidate biomarkers (KIF5B, SFRP2, LOXL2, and MMP3) were selected and validated on a mouse-human hybrid Tissue Microarray (TMA) that was specifically generated for this study. Additionally, a human TMA was used to analyze the expression of KIF5B and SFRP2 in progressive stages of pancreatic cancer.ResultsThe EVome of co-cultured epithelial and stromal cells is different from individual cells with distinct protein compositions. EVs secreted from stromal and cancer cells cultures could not induce significant changes in Pre-Metastatic Niche (PMN) modulation, which was assessed by changes in the distant organ metastases. However, they did induce significant changes in the early microbiome, as indicated by differences in α and β-diversities. KIF5B and SFRP2 show promise for early detection and investigation in progressive pancreatic cancer. These markers are expressed in all stages of pancreatic cancer such as low grade PanINs, advanced cancer, and in liver and soft tissue metastases.ConclusionsProteomic characterization of EVs derived from mimicking conditions of epithelial and stromal cells in the tumor-microenvironment resulted in the identification of several proteins, some for the first time in EVs. These secreted EVs cannot induce changes in distant organ metastases in in vivo models of EV education, but modulate changes in the early murine microbiome. Among all the proteins that were analyzed (MMP3, KIF5B, SFRP2, and LOXL2), KIF5B and SFRP2 show promise as bona fide early pancreatic cancer biomarkers expressed in progressive stages of pancreatic cancer.

Circulating cancer-associated extracellular vesicles as early detection and recurrence biomarkers for pancreatic cancer.

Early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for improving patient survival rates, and noninvasive biomarkers are urgently required to identify patients who are eligible for curative surgery. Here, we examined extracellular vesicles (EVs) from the serum of PDAC patients to determine their ability to detect early‐stage disease. EV‐associated proteins purified by ultracentrifugation and affinity columns underwent proteomic analysis to identify novel PDAC markers G protein‐coupled receptor class C group 5 member C (GPRC5C) and epidermal growth factor receptor pathway substrate 8 (EPS8). To verify the potency of GPRC5C‐ or EPS8‐positive EVs as PDAC biomarkers, we analyzed EVs from PDAC patient blood samples using ultracentrifugation in two different cohorts (a total of 54 PDAC patients, 32 healthy donors, and 22 pancreatitis patients) by immunoblotting. The combination of EV‐associated GPRC5C and EPS8 had high accuracy, with area under the curve values of 0.922 and 0.946 for distinguishing early‐stage PDAC patients from healthy controls in the two cohorts, respectively, and could detect PDAC patients who were negative for CA19‐9. Moreover, we analyzed 30 samples taken at three time points from 10 PDAC patients who underwent surgery: before surgery, after surgery, and recurrence as an early‐stage model. These proteins were detected in EVs derived from preoperative and recurrence samples. These results indicated that GPRC5C‐ or EPS8‐positive EVs were biomarkers that have the potential to detect stage I early pancreatic cancer and small recurrent tumors detected by computed tomography.

Enhanced endoscopic ultrasound imaging for pancreatic lesions: The road to artificial intelligence.

Early detection of pancreatic cancer has long eluded clinicians because of its insidious nature and onset. Often metastatic or locally invasive when symptomatic, most patients are deemed inoperable. In those who are symptomatic, multi-modal imaging modalities evaluate and confirm pancreatic ductal adenocarcinoma. In asymptomatic patients, detected pancreatic lesions can be either solid or cystic. The clinical implications of identifying small asymptomatic solid pancreatic lesions (SPLs) of < 2 cm are tantamount to a better outcome. The accurate detection of SPLs undoubtedly promotes higher life expectancy when resected early, driving the development of existing imaging tools while promoting more comprehensive screening programs. An imaging tool that has matured in its reiterations and received many image-enhancing adjuncts is endoscopic ultrasound (EUS). It carries significant importance when risk stratifying cystic lesions and has substantial diagnostic value when combined with fine needle aspiration/biopsy (FNA/FNB). Adjuncts to EUS imaging include contrast-enhanced harmonic EUS and EUS-elastography, both having improved the specificity of FNA and FNB. This review intends to compile all existing enhancement modalities and explore ongoing research around the most promising of all adjuncts in the field of EUS imaging, artificial intelligence.

Robust Descriptor of Pancreatic Tissue for Automatic Detection of Pancreatic Cancer in Endoscopic Ultrasonography.

Pancreatic cancer (PC) has a reported mortality of 98% and a 5-y survival rate of 6.7%. Experienced gastroenterologists detect 80% of those with early-stage PC by endoscopic ultrasonography (EUS). Here we propose an automatic second reader strategy to detect PC in an entire EUS procedure, rather than focusing on pre-selected frames, as the state-of-the-art methods do. The method unmasks echo tumoral patterns in frames with a high probability of tumor. First, speeded up robust features define a set of interest points with correlated heterogeneities among different filtering scales. Afterward, intensity gradients of each interest point are summarized by 64 features at certain locations and scales. A frame feature vector is built by concatenating statistics of each feature of the 15 groups of scales. Then, binary classification is performed by Support Vector Machine and Adaboost models. Evaluation was performed using a data set comprising 55 participants, 18 of PC class (16,585 frames) and 37 subjects of non-PC class (49,664 frames), randomly splitting 10 times. The proposed method reached an accuracy of 92.1%, sensitivity of 96.3% and specificity of 87.8.3%. The observed results are also stable in noisy experiments while deep learning approaches fail to maintain similar performance.

Research progress on microRNAs as molecular markers in pancreatic cancer: a narrative review.

Background and ObjectiveThe incidence and mortality of pancreatic cancer (PC) have increased in recent years. The current status of PC diagnosis and treatment remains grim in clinical practice because the commonly used early screening tools are not sufficient. Improving the early detection of PC and strengthening standardized comprehensive treatment remain the focus of PC research. Many studies have shown that micro RNAs (miRNAs) play an important role in the occurrence, development, and treatment of PC. It is expected that miRNAs will become new molecular markers of PC.MethodsWe extracted and compiled useful information from the PubMed database that met our criteria for analyzing PC diagnosis, treatment, and prognosis.Key Content and FindingsIn this narrative review, we summarize the mechanism of some miRNAs in the occurrence and development of PC and review them as potential markers for the diagnosis, treatment, and prognosis of PC. The function of miRNAs in PC has great potential in studying the pathogenesis of PC. The discovery of many important oncogenic miRNAs and their downstream targets will bring new ideas and research paths for the diagnosis and targeted therapy of PC.ConclusionsMiRNAs are expected to provide novel ideas and research directions for the diagnosis and targeted treatment of PC. However, more patient data and clinical trials are needed before miRNAs can become novel molecular markers for PC.

Diagnostic value of serum carbohydrate antigen 19-9 in pancreatic cancer: a systematic review and meta-analysis.

Carbohydrate antigen 19-9 (CA19-9) is the most widely used serum biomarker for detecting pancreatic cancer (PC). Since early diagnosis is important for improving PC prognosis, a comprehensive understanding of the diagnostic performance of CA19-9 is critical. This study focused on comprehensive evaluation of the efficacy of CA19-9 in PC diagnosis. Literature research was based on the seven databases. Studies released from January 2002 to January 2022 focused on the efficacy of CA19-9 in the detection of PC were included. Summarized sensitivity, specificity, and sROC/accuracy of discrimination (AUC) were estimated. Potential publication bias was measured with Funnel plot and Egger's test. Meta-regression was performed to detect possible causes of heterogeneity. Subgroup analysis was used to assess the diagnostic efficacy of CA19-9 under different conditions. The study is registered on PROSPERO (CRD42021253861). Seventy-nine studies containing 20 991 participants who met the criteria were included. The pooled sensitivity, specificity, and AUC of CA19-9 in diagnose PC were 72% (95% CI, 71-73%), 86% (95% CI, 85-86%), and 0.8474 (95% CI, 0.8272-0.8676). Subgroup analysis suggested that the diagnostic efficiency of CA19-9 in studies with healthy controls was the highest, followed by intraductal papillary mucinous neoplasm, in pancreatitis and diabetes were consistent with the overall result. Our analysis showed that serum CA19-9 had high and stable diagnostic efficacy for PC (not affected by diabetes). Subgroup analysis showed that serum CA19-9 yielded highest effectiveness in the diagnosis of pancreatic precancerous lesions, which indicated an irreplaceable clinical value in the early detection and warning value for PC.

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.

New-onset type ii diabetes mellitus in early diagnosis of pancreatic cancer: literature review

The literature data on the connection between type 2 diabetes mellitus (DM) and pancreatic cancer (PC) were analyzed. Early detection of pancreatic cancer remains an unsolved problem of oncology because of absence of pathognomonic symptoms for this disease at the early stages, difficulties in visualizing changes in the pancreas as well as difficulties in differential diagnosis with benign tumors. Fifty nine articles in Russian and English were studied, and the data presented in literature regarding the connection between type 2 diabetes mellitus and early stages of pancreaticic cancer have been analyzed.Resectable pancreatic cancer makes up less than 15 % of the cases during initial medical examination. Currently, the existing methods of diagnostics do not allow solving the problem of early detection of pancreatic cancer it dictates the need to search for a new marker that will improve the early diagnosis of this tumor. According to the results of many studies, there has been detected a connection between the new-onset type 2 diabetes mellitus and early stages of pancreatic cancer. Based on the data published by a number of authors, manifestation of the new-onset type 2 diabetes mellitus in patients over fifty (fasting blood glucose level 7 mmol/L, or a blood glucose sugar level 11,1 mmol/L after performing oral glucose tolerance test, or random blood glucose level 11,1 mmol/L with a typical hyperglycemia signs and symptoms) can serve as an early symptom of pancreatic cancer.&#x0D; Thus, the new-onset type 2 diabetes mellitus in persons aged 50 years or older can potentially be used to identify a group of patients requiring a directional examination for the purpose of early detection of pancreatic cancer. Further studies on this problem could allow us to formulate a special program of early diagnostics of pancreatic cancer.