16,341 research outputs found

    Predicting Pancreatic Cancer Using Support Vector Machine

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    This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately

    Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance

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    A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations. [Abstract copyright: AJTR Copyright © 2020.

    Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

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    © The Author(s) 2019. Published by Springer Nature on behalf of Cancer Research UK.BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.Peer reviewe

    Duodenal Carcinoma from a Duodenal Diverticulum Mimicking Pancreatic Carcinoma

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    An 81-year-old man was found to have a pancreatic head tumor on abdominal computed tomography (CT) performed during a follow-up visit for sigmoid colon cancer. The tumor had a diameter of 35mm on the CT scan and was diagnosed as pancreatic head carcinoma T3N0M0. The patient was treated with pylorus-preserving pancreaticoduodenectomy. Histopathological examination showed that the tumor had grown within a hollow structure, was contiguous with a duodenal diverticulum, and had partially invaded the pancreas. Immunohistochemistry results were as follows:CK7 negative, CK20 positive, CD10 negative, CDX2 positive, MUC1 negative, MUC2 positive, MUC5AC negative, and MUC6 negative. The tumor was diagnosed as duodenal carcinoma from the duodenal diverticulum. Preoperative imaging showed that the tumor was located in the head of the pancreas and was compressing the common bile duct, thus making it appear like pancreatic cancer. To the best of our knowledge, this is the second report of a case of duodenal carcinoma from a duodenal diverticulum mimicking pancreatic carcinoma

    PPAR-alpha: a novel target in pancreatic cancer

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    Background: Current targeted therapies in pancreatic cancer have been ineffective. The tumor stroma, including intra- and peri-tumoral inflammation and fibrosis, is increasingly implicated in pancreatic cancer. Pancreatic cancer is characterized by a highly fibrotic tumor environment resulting in stromal resistance to chemotherapy. Peroxisome proliferator-activated receptor-alpha (PPARα), a ligand-activated nuclear receptor/transcription factor, is a negative regulator of inflammation. In PPARα deficient mice, stromal processes inhibit tumor growth, resulting in dormant tumors. The presence of PPARα in the tumor cells as well as in the host is necessary for unabated tumor growth. Objective: We hypothesized that blocking the PPARα pathway with a small molecule PPARα antagonist (NXT) may prevent pancreatic cancer progression by targeting tumor cells as well as non-neoplastic cells in the tumor microenvironment. Methods: Growth inhibitory activity of the PPARα antagonist was assessed in murine as well as human pancreatic tumor cell lines (Panc0H7 and BxPC3) and in a murine macrophage cell line (RAW 264.7). Cell viability was determined by trypan blue exclusion assay. AKT, P-AKT, PCNA, BAX, and p27 levels were analyzed by western blot analysis. Cell cycle changes were detected by flow cytometry. Cellular senescence was determined by senescence-associated β-gal (SA-β-gal) staining. Results: The PPARα antagonist inhibited cell growth in macrophages and in pancreatic tumor cells as confirmed by reduced protein level expression of PCNA and activated AKT. Treatment of the PPARα antagonist was non-cytotoxic to tumor cells. Inhibition of PPARα induced cell cycle arrest at G0/G1 in tumor cells and macrophages. The induction of cellular senescence was observed in pancreatic cancer cells. Interestingly, we observed a reduction in protein level expression of BAX, a marker for apoptosis, and p27, an inhibitor of the cell cycle. Conclusion: We now demonstrate that a PPARα antagonist exerts its anti-growth activity by inducing G0/G1 cell cycle arrest, thereby inducing cellular senescence without cell death. These findings provide a mechanism for the anti-tumorigenic activity of PPARα inhibition, and the rationale to use PPARα antagonists as a novel therapeutic approach to pancreatic cancer.2016-11-03T00:00:00

    CHARACTERISTICS OF INDIVIDUALS UNDERGOING PANEL GENETIC TESTING FOR PRIMARY BRAIN TUMORS

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    Background. Currently, there are no genetic testing guidelines for patients with a primary brain tumor (PBT). This population is largely understudied in terms of the family history, tumor grade, pathology, and their relation to genetic contribution. Our aim was to describe patient-specific characteristics and family histories across mutation-positive, negative, and variant of uncertain significance (VUS) cohorts based on cancer-panel genetic test results among patients with a PBT. Methods. Subjects were referred for multi-gene panel testing between March 2012 and June 2016. Clinical data were ascertained from test requisition forms. The incidence of pathogenic mutations (including likely pathogenic) and VUS’s were calculated for each gene and patient cohort. Results. Almost all tumors were glial (n=293, 53%) or meningeal pathology (n=222, 40%). Age of diagnosis differed significantly between glial and meningeal tumors (pCHEK2 (20/104), BRCA2 (13/104), PMS2 (10/104), TP53 (8/104), and APC (8/104). Of 165 patients with available family history information, nearly all (n=157, 95%) reported a family history of some cancer. Conclusions. Our data suggest PBTs can be the primary presenting cancer in hereditary syndromes with a known PBT risk. While pathology is helpful in narrowing down the differential diagnosis, patients’ pathology can be atypical in relation to their hereditary cancer syndrome. Family history evaluations are a beneficial risk assessment modality, particularly until testing criteria are developed for PBTs. Further research is necessary for the development of genetic testing criteria in the PBT population and more robust identification of at-risk individuals
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