Regina Reszka
Charité
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Publication
Featured researches published by Regina Reszka.
Gut | 2018
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
Objective Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose. Design For a case–control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry. Results A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93–0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%–97.0%). In the test set, an AUC of 0.94 (95% CI 0.91–0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%–95.5%) and a specificity of 91.3% (95% CI 82.8%–96.4%) were achieved, successfully validating the biomarker signature. Conclusions In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%–99.9%) (training set) and 99.8% (95% CI 99.6%–99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9.
Archive | 2010
Beate Kamlage; Bianca Bethan; Regina Reszka; Edgar Leibold; Klaus Jung; Michael Lein; Glen Kristiansen
Archive | 2012
Regina Reszka; Beate Kamlage; Holger Kalthoff; Bodo Schniewind; Julia Mayerle; Markus M. Lerch; Christian Pilarsky; Robert Grützmann
Archive | 2010
Beate Kamlage; Bianca Bethan; Regina Reszka; Edgar Leibold; Klaus Jung; Michael Lein; Glen Kristiansen
Archive | 2015
Beate Kamlage; Regina Reszka; Erik Peter; Jürgen Kastler; Philipp Schatz; Holger Kalthoff; Bodo Schniewind; Julia Mayerle; Markus M. Lerch; Christian Pilarsky; Robert Grützmann
Archive | 2017
Edgar Leibold; Hugo A. Katus; Jens Fuhrmann; Johanna Wolf; Jürgen Kastler; Kristina Busch; Norberth Frey; Regina Reszka; Tanja Weis
Archive | 2017
Peter McGranaghan; Ulrike Rennefahrt; Beate Kamlage; Regina Reszka; Philipp Schatz; Bianca Bethan; Julia Mayerle; Markus M. Lerch
Archive | 2016
Beate Kamlage; Bethan Bianca; Regina Reszka; Edgar Leibold; Klaus Jung; Michael Lein; Glen Kristiansen
Archive | 2016
Jens Fuhrmann; Regina Reszka; Juergen Kastler; Kristina Busch; Edgar Leibold; Hugo A. Katus; Norbert Frey; Johanna Wolf; Tanja Weis
Archive | 2016
Beate Kamlage; Regina Reszka; Philipp Schatz; Martin Dostler; Susan Carvalho; Erik Peter; Philipp Mappes; Holger Kalthoff; Bodo Schniewind; Julia Mayerle; Markus M. Lerch; Robert Gruetzmann; Christian Pilarsky