Literature DB >> 19239583

Likelihood ratios of clinical, laboratory and image data of pancreatic cancer: Bayesian approach.

Esteban de Icaza1, Malaquías López-Cervantes, Armando Arredondo, Guillermo Robles-Díaz.   

Abstract

PURPOSE: The diagnosis of pancreatic cancer (PC) is most frequently established in advanced stages. The aim of this study is to estimate the likelihood ratios (LRs) of diagnostic data with regards to PC that could be used to approach an earlier diagnosis.
METHODS: A case-control study of 300 patients - 150 histological diagnosed cases of PC and 150 age-matched controls hospitalized for study of jaundice, abdominal pain, weight loss and/or chronic pancreatitis - was conducted. Bayesian probabilities in the form of LRs were estimated for PC predictions.
RESULTS: Probability of PC was associated with jaundice [odds ratio (OR) 2.89; confidence interval (CI) 1.71-4.85], glycemic disturbance (OR 5.64; CI 2.36-13.46), tobacco index >20 (OR 2.11; CI 1.08-4.09) and tumour marker CA 19-9 (OR 9.33; CI 1.36-63.95). Computed tomography showed the highest test performance with regards to PC when comparing with other diagnostic tests. LRs for variables relevant to PC were estimated, among the most relevant: jaundice LR + 1.92, CA 19-9 LR + 5.36 and computed tomography LR + 4.15. The prediction model with an endoscopic retrograde cholangiopancreatography at a tertiary referral hospital determined a 67% probability of detecting PC.
CONCLUSIONS: Through a Bayesian approach we can combine clinical, laboratory and imaging data to approximate to an earlier diagnosis of PC.

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Year:  2009        PMID: 19239583     DOI: 10.1111/j.1365-2753.2008.00955.x

Source DB:  PubMed          Journal:  J Eval Clin Pract        ISSN: 1356-1294            Impact factor:   2.431


  4 in total

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Authors:  Tilman Pickartz; Julia Mayerle; Matthias Kraft; Matthias Evert; Katja Evert; Jens-Peter Kühn; Claus-Dieter Heidecke; Markus M Lerch
Journal:  Med Klin (Munich)       Date:  2010-04

2.  Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

Authors:  Di Zhao; Chunhua Weng
Journal:  J Biomed Inform       Date:  2011-05-27       Impact factor: 6.317

3.  Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis.

Authors:  Julia Mayerle; Holger Kalthoff; Regina Reszka; Beate Kamlage; Erik Peter; Bodo Schniewind; Sandra González Maldonado; Christian Pilarsky; Claus-Dieter Heidecke; Philipp Schatz; Marius Distler; Jonas A Scheiber; Ujjwal M Mahajan; F Ulrich Weiss; Robert Grützmann; Markus M Lerch
Journal:  Gut       Date:  2017-01-20       Impact factor: 23.059

4.  Metabolomics Identifies Biomarker Signatures to Differentiate Pancreatic Cancer from Type 2 Diabetes Mellitus in Early Diagnosis.

Authors:  Hongmin Xu; Lei Zhang; Hua Kang; Jie Liu; Jiandong Zhang; Jie Zhao; Shuye Liu
Journal:  Int J Endocrinol       Date:  2021-11-25       Impact factor: 3.257

  4 in total

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