Diagnosing Leukemia through AI

The presence of cancer of the lymphatic system is often determined by testing blood or bone marrow samples. A team led by Professor Dr. Peter Krawitz from the University of Bonn found that artificial intelligence can help diagnose such lymphomas and leukemia. The technology fully utilizes the potential of all measurement values and increases the speed and objectivity of the analyzes compared to established methods. The method has been further developed so that even the smallest laboratories can benefit from this free access machine learning method – an important step towards clinical practice. The study was published in the journal “Patterns”.

The lymph nodes swell, weight loss and fatigue, as well as fever and infections, are typical symptoms of malignant B-cell lymphomas and related leukemia. If such a cancer of the lymphatic system is suspected, the doctor takes a blood or bone marrow sample and sends it to specialized laboratories. This is where flow cytometry comes in. Flow cytometry is a method in which the blood cells flow past measurement sensors at high speed. The properties of the cells can be detected depending on their shape, structure or coloring. The exact detection and characterization of pathological cells is important for the diagnosis.

Laboratories use “antibodies” that adhere to the surface of cells and are coupled to fluorescent dyes. These markers can also be used to spot small differences between cancer cells and healthy blood cells. Flow cytometry generates large amounts of data. On average, more than 50,000 cells are measured per sample. This data is usually analyzed on the screen by plotting the expression of the markers used against each other. “But with 20 markers, the doctor would already have to compare about 150 two-dimensional images,” says Prof. Dr. Peter Krawitz of the Institute for Genomic Statistics and Bioinformatics at the University Hospital Bonn. “That’s why it’s usually too costly to thoroughly sift through the entire data set.”

For this reason, Krawitz, together with the bioinformaticians Nanditha Mallesh and Max Zhao, investigated how artificial intelligence can be used to analyze cytometry data. The team considered more than 30,000 data sets from patients with B-cell lymphoma to train artificial intelligence (AI). “AI takes full advantage of the data and increases the speed and objectivity of diagnoses,” says lead author Nanditha Mallesh. The result of the AI evaluations is a suggested diagnosis that still needs to be verified by the physician. In the process, the AI provides indications of conspicuous cells.

Specialists reviewed the results of artificial intelligence

Blood samples and cytometer data were obtained from the Munich Leukemia Laboratory (MLL), the Charité – Universitätsmedizin Berlin, the University Hospital Erlangen and the Bonn University Hospital. Specialists from these institutions examined the results of artificial intelligence. “The gold standard is diagnosis by hematologists, which can also take into account results of additional tests,” Krawitz says. “The point of using AI is not to replace physicians, but to make the best use of the information contained in the data.”

The big innovation of the AI ​​that has just been introduced lies in the possibility of knowledge transfer: Smaller laboratories in particular can benefit from this, which cannot afford their own expertise in bioinformatics and may also have too few samples to develop their own AI from scratch. During the phase in which the AI ​​learns the details of the new laboratory, it can then use knowledge from many thousands of data sets.

All raw data and the complete software are open source and therefore freely accessible. In addition, res mechanica GmbH, which was involved in the study, has developed a web service that makes artificial intelligence usable even for users without bioinformatics expertise. “With https://hema.to, we want to enable the exchange of anonymized flow cytometry data between laboratories and in this way create the conditions for even higher quality in diagnostics,” says Dr. Hannes Lüling of res mechanica.

Great potential

The team sees great potential in this technology, which is why the researchers also want to work with leading manufacturers of analysis devices and software to advance the use of artificial intelligence. In the case of B-cell lymphomas, for example, genetic and cytomorphological data are also collected to confirm the diagnoses.

“If we succeed in using AI for these methods as well, we would have an even more powerful tool,” says Krawitz, who is also a member of the Cluster of Excellence ImmunoSensation2 at the University of Bonn. The artificial intelligence developed can in principle also be used for diagnoses of rheumatic diseases, which are often also based on flow cytometric data.

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