Aufbauend auf dem Krebsimmunzellzyklus wurde ein neuesModellentwickelt. Dieses spannt eine Brücke zwischen neusten wissenschaftlichen und klinischen Erkenntnissen und erklärt zugleich, warum Krebsimmuntherapien bei jedem Patienten unterschiedlich wirken können. Gemäß diesem Modell gehen wir davon aus, dass sich alle Krebserkrankungen des Menschen je nach Immunstatus in drei Immunprofile – sogenannte Immunphänotypen – unterteilen lassen.

Um die drei Phänotypen besser zu verstehen, hilft ein Gedankenspiel, in dem man den Kampf unseres Immunsystems gegen den Krebs mit einer Mission zum Mond vergleicht…

Das Wissen darüber, wie Krebs entsteht und fortschreitet wächst jeden Tag. Und wir verstehen immer besser, welche Rolle dabei unser Immunsystem spielt. Diese Erkenntnisse bieten uns heute die einmalige Chance, personalisierte Krebsimmuntherapien zu entwickeln. Denn unser Ziel ist es, dass zukünftig jeder Patient genau die Krebsimmuntherapie erhält, die ihn für seinen persönlichen Kampf gegen die Krebserkrankung stark macht.

In deep learning, a computer trains itself to detect patterns and relationships in a set of training data, using hundreds of layers of analysis that each pick up different relevant features in an image without any guidance from a user. The system then applies its knowledge to novel input data of the same type. In this case, we gave our computers a large set of CFP and OCT data from participants in two large DME clinical trials to train on.

The deep learning system examined a total of 17,997 CFP images from ~700 patients and compared them with corresponding OCT thickness measurements. The best model we developed using this training set was able to predict macular thickness greater than the 250 micron threshold with an accuracy of 97 percent — an impressive level of performance. Deep learning could even do a reliable job of predicting the actual OCT measurement of the macula’s thickness from a CFP image if it was of sufficient quality.

This initial finding surpassed our expectations, and we wanted to learn more about how it happened. When we looked into it, we were thrilled to find that the computer was focusing on the same parts of the images as specialists have been doing for years, such as the contours and calibre of blood vessels.

To test that finding, we still need to validate our system by testing it on other datasets. But presuming it works well, this tool could be of tremendous value to ophthalmologists as they treat patients with diabetes and DME. Once DME patients begin treatment, for example, many of them have to be seen every four weeks for OCT testing to ensure that their condition is not progressing. AI might enable people to use a cell phone camera to monitor their retinal tissue in real time, making it much easier for doctors to keep track of their patients’ need for and response to treatment. We could even envision an app to assess whether treatment is working. Such an innovation would not only be more convenient for patients, but also make them much more active participants in their own care. For ophthalmologists, the ability to estimate macular thickness with CFP would make it easier to identify the most urgent cases and treat them quickly and appropriately.

One important lesson of this experiment was the value of having a large clinical trial dataset to train our system on. Machine learning, which encompasses deep learning as well as other techniques that computers use to develop knowledge bases for data analysis, depends on robust, high-quality and representative training data for success. That is an asset that an organisation like ours possesses in abundance, in the form of lab measurements, clinical trial data and real-world information.

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