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Disclaimer: We have tried to ensure that the information provided on this site is accurate and up-to-date, but it should not be relied upon. If you are concerned about your health you should consult your doctor. HMRN cannot accept liability for any loss or damage resulting from any inaccuracy in our information or in third-party information on websites to which we link.

This site is designed and maintained by the Epidemiology & Cancer Statistics Group at the University of York.
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Background
Background

Welcome to the Haematological Malignancy Research Network (HMRN).

On these pages you will find information about haematological cancers and related blood disorders. HMRN is a comprehensive population-based register that effectively links diagnostics and prognostics with data on outcome and treatment, providing a unique opportunity to gain insight into aspects of haematological cancer that cannot be studied elsewhere

This site is intended for anyone interested in haematological malignancy. It contains information for patients, statistics for researchers, as well as a section for the clinical teams working across the network

HMRN covers a population of 3.6 million and accrues around 2,000 new haematological cancers each year. It is based in the UK Cancer Networks of Yorkshire, and Humber & Yorkshire Coast, with a population that is broadly representative of the UK as a whole. All haematological malignancies, including transformations and progressions, are centrally reviewed and coded using the latest techniques by internationally recognized experts. Clinical care is provided by a single unified network, and following diagnosis information on demographics, prognostics and staging, as well as details about all treatments, responses and relapses is abstracted onto highly structured forms - each cancer having its own specially tailored version. A critically important feature of the abstraction process is the emphasis on primary source data, which enables embedded computerised algorithms to automatically generate staging and prognostic scores at the time of data entry.