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In a study published in the Journal of Clinical Oncology, an international research team has used machine learning to improve risk stratification for patients over 60 diagnosed with acute myeloid leukaemia, an aggressive type of blood cancer.

A patient and a doctor consulting a tablet together in a clinic. © Yuri A/Shutterstock.com

An international collaboration led by Paresh Vyas (MRC Molecular Haematology Unit in the MRC Weatherall Institute of Molecular Medicine) and Peter Valk (Erasmus University MC Cancer Institute) has created a tool to help clinicians predict how older patients with acute myeloid leukaemia will respond to treatment. Their new online tool AML60+ will allow doctors to apply this in clinical practice easily.

Acute myeloid leukaemia (AML) is the most common aggressive blood cancer in adults. In the UK, around 3100 are diagnosed with it each year. AML is a diverse disease with very varied patient outcomes; clinicians use various indicators to predict who will benefit from treatment and how likely it is that the leukaemia will come back (this is called “risk stratification”). However, current predictive tools used by clinicians are based on younger patients, not those over the age of 60, for whom the disease is especially diverse. 

In a study published in the Journal of Clinical Oncology earlier this month, a team of researchers from the UK, Netherlands, Switzerland and Belgium created a risk stratification system for patients over the age of 60 to identify those who would benefit most from intensive treatment and stem cell transplantation.

Read the full story on the MRC Weatherall Institute of Molecular Medicine.