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2026-10Studienarbeit DOI: 10.25646/14466
Epidemiology and Treatment Patterns of Patients with Pancreatic Cancer in Germany: A Retrospective Analysis of Data from 2020-2024
dc.contributor.authorPharmaLex GmbH, Bad Homburg vor der Höhe
dc.date.accessioned2026-10-08T13:02:39Z
dc.date.available2026-10-08T13:02:39Z
dc.date.issued2026-10none
dc.identifier.urihttp://edoc.rki.de/176904/14032
dc.description.abstractPancreatic ductal adenocarcinoma (PDAC) accounts for approximately 90% of pancreatic cancers (PC) and is one of the most fatal malignancies. Given the high clinical burden and a poor overall survival of PDAC patients, contemporary population-based data are needed to describe how many patients are affected and how they are treated in routine care in Germany. Because evidence on treatment sequencing remains limited, real-world data can help clarify which therapies are used and, for systemic therapy, which substances are used across treatment lines. The aim of this study is to assess the prevalence and incidence of PC in Germany, including the distribution of pancreatic cancer types, as well as to analyze real-world treatment patterns among patients with PDAC. The specific study objectives are: - To estimate the incidence and prevalence of PC leveraging nationwide cancer registry data (ZfKD) from Germany - To describe the distribution of pancreatic cancer types and estimate the proportion PDAC patients within the overall PC population - To characterize PDAC patients at diagnosis, with a focus on demographics and tumor-specific characteristics, and assess the proportion of patients with metastatic PDAC - To describe real‑world treatment patterns for metastatic PDAC over time, including the use of systemic therapies - To assess overall survival (OS) and progression-free survival (PFS) of metastatic PDACeng
dc.language.isoengnone
dc.publisherRobert Koch-Institut
dc.subject.ddc610 Medizin und Gesundheitnone
dc.titleEpidemiology and Treatment Patterns of Patients with Pancreatic Cancer in Germany: A Retrospective Analysis of Data from 2020-2024none
dc.typeStudyThesis
dc.identifier.urnurn:nbn:de:0257-176904/14032-6
dc.identifier.doi10.25646/14466
local.edoc.type-nameStudienarbeit

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