AI can detect blood cancer with high reliability
London: Artificial Intelligence can help detect one of the most common forms of blood cancer acute myeloid leukemia (AML) with high reliability, new research has found.
Their approach, based on the analysis of the gene activity of cells found in the blood, could support conventional diagnostics and possibly accelerate the beginning of therapy, said the study published in the journal iScience.
In the early stages, the symptoms of AML can resemble those of a bad cold. However, AML is a life-threatening disease that should be treated as quickly as possible.
“With a blood test, as it seems possible on the basis of our study, it is conceivable that the family doctor would already clarify a suspicion of AML,” said Joachim Schultze, a research group leader at the German Center for Neurodegenerative Diseases (DZNE).
“And when the suspicion is confirmed, the patient is referred to a specialist. Possibly, the diagnosis would then happen earlier than it does now and therapy could start earlier,” added Schultze, who is also Head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn in Germany.
For the study, the researchers focused on the “transcriptome”, which is a kind of fingerprint of gene activity.
In each and every cell, depending on its condition, only certain genes are actually “switched on”, which is reflected in their profiles of gene activity.
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Exactly such data derived from cells in blood samples and spanning many thousands of genes were analyzed in the current study.
“The transcriptome holds important information about the condition of cells. However, classical diagnostics is based on different data. We, therefore, wanted to find out what an analysis of the transcriptome can achieve using artificial intelligence, that is to say, trainable algorithms,” said Schultze.
Data from more than 12,000 blood samples these came from 105 different studies were taken into account for the study.
Approximately 4,100 of these blood samples derived from individuals diagnosed with AML, the remaining ones had been taken from individuals with other diseases or from healthy individuals.
The scientists fed their algorithms parts of this data set. The input included information about whether a sample came from an AML patient or not.
“The algorithms then searched the transcriptome for disease-specific patterns. This is a largely automated process. It’s called machine learning,” said Schultze.
Based on this pattern recognition, further data was analyzed and classified by the algorithms categorized into samples with AML and without AML.