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 leukemias. . 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 accessible machine learning method is 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, these are typical symptoms of malignant B-cell lymphomas and related leukemia. If this type of cancer of the lymphatic system is suspected, the doctor takes a sample of blood or bone marrow and sends it to you specialized laboratories. This is where flow cytometry comes in. Flow cytometry is a method in which blood cells pass through measuring sensors at high speed. The properties of cells can be recognized by their shape, structure or color. The exact detection and characterization of pathological cells is important for the diagnosis.
The laboratories use “antibodies” that dock to the surface of the cells and are coupled to fluorescent dyes. Such markers can also be used to detect 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. These data are then typically analyzed on 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 bioinformaticians Nanditha Mallesh and Max Zhao, investigated how artificial intelligence can be used to analyze cytometric data and considered more than 30,000 data sets from B-cell lymphoma patients to use 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), Charité Universitätsmedizin Berlin, the University Hospital Erlangen and 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 great new feature of the AI now presented lies in the possibility of knowledge transfer: Particularly smaller laboratories that cannot afford their own bioinformatics expertise and may also have too few samples to develop their own AI from scratch can benefit from this. After a short training phase, during which the AI learns the specifics of the new laboratory, it can then draw on knowledge derived from many thousands of data sets.
All raw data and the complete software are open source and therefore freely accessible. In addition, the res mechanica GmbH involved in the study has developed a web service that makes artificial intelligence usable even for users with no experience in bioinformatics.
“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 in order 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.
