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Population-based epidemiological data of follicular lymphoma in Poland: 15 years of observation.

Anna Szumera-CiećkiewiczUrszula WojciechowskaJoanna DidkowskaJan PoleszhukGrzegorz RymkiewiczEwa Paszkiewicz-KozikKamil SokółMonika Prochorec-SobieszekJan Walewski
Published in: Scientific reports (2020)
Available epidemiological reports on follicular lymphoma (FL) often highlight a significant discrepancy between its high and low incidence rates in Western and Eastern Europe, respectively. The reasons behind that difference are not fully understood, but underreporting is typically presumed as one of the main factors. This study aimed to assess FL epidemiology in Poland based on 2000-2014 data from the Polish National Cancer Registry, which has 100% population coverage and over 90% completeness of the registration. All cases were coded according to ICD-10 and ICD-O-3 recommendations. The total number of registered FL cases was 3,928 with crude (CR) and standardized (SR) incidence rates of 0.72/105 and 0.87/105, respectively. The median age of FL diagnosis was 61 years, with the male to female incidence ratio of 1.06. The distribution of morphological types of FL: not otherwise specified (NOS), grades 1, 2, or 3 were 72.58, 4.81, 12.88, and 9.73%, respectively. Among all reported mature B-cell non-Hodgkin lymphomas, FL was ranked the fourth in incidence, just after chronic lymphocytic leukemia/small lymphocytic lymphoma (CR 3.62/105, SR 4.99/105), plasma cell neoplasms (CR 3.78/105, SR 4.97/105) and diffuse B-cell lymphoma, NOS (CR 2.13/105, SR 2.65/105). The systematic increase in FL incidence among females was observed. Our study confirms a lower FL incidence rate in Poland as compared to other European countries. Moreover, as our analysis was based on a registry with high data completeness, it provides evidence that reasons other than underreporting are responsible for FL incidence discrepancies between Eastern and Western Europe.
Keyphrases
  • risk factors
  • diffuse large b cell lymphoma
  • emergency department
  • chronic lymphocytic leukemia
  • big data
  • healthcare
  • single cell
  • cell therapy
  • bone marrow
  • deep learning
  • artificial intelligence
  • drug induced