AI-driven cancer research initiative in Dresden: advancing cancer knowledge and treatment through artificial intelligence
In a significant development in the fight against cancer, an interdisciplinary research team led by the Else Krรถner Fresenius Center Dresden (EKFZ) for Digital Health has embarked on a five-year project called DECIPHER-M. This ambitious undertaking, which is being conducted at TU Dresden, is funded by the Federal Ministry of Education and Research (BMBF) and has an initial budget of approximately 5.5 million euros.
The project's primary focus is on understanding and addressing the challenge of metastasis in cancer, one of the biggest hurdles in oncology. Metastasis occurs when cancer cells detach from the original tumor and travel to other parts of the body, making cancer harder to treat.
DECIPHER-M aims to improve treatment options for cancer patients and enhance their quality of life. To achieve this, the project employs a multimodal approach, connecting various data sources such as tissue samples, X-ray images, and genetic information. By combining these different medical data, the team hopes to enable earlier, more precise diagnoses and preventive measures.
Prof. Jakob Nikolas Kather is coordinating the project, and an interdisciplinary research team is working tirelessly to make DECIPHER-M a success. In addition to Dresden, project partners from Aachen, Essen, Heidelberg, Mainz, and Munich are involved in DECIPHER-M. The project also collaborates with the German Cancer Research Center (DKFZ) and the University Hospital Mannheim.
The project's budget is planned to increase to around nine million euros, reflecting the importance and potential impact of DECIPHER-M. The project DECIPHER-M is using artificial intelligence (AI) to achieve its goals, with the aim of improving the survival rates of cancer patients.
The DECIPHER-M project is scheduled to run from this year until 2030, spanning a total of five years. The team's ultimate objective is to develop personalized treatment strategies and predict individual metastasis risk more accurately, ultimately leading to improved outcomes for cancer patients.
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