The BMBF is funding the joint project ATLAS (Al and Simulation for Tumor Liver ASsessment) in the line "Computational Life Sciences - AI Methods for Systems Medicine". The project will combine machine learning and continuum biomechanical simulation methods to support clinicians in tumor boards in the diagnosis and treatment of malignant liver diseases.
Cancer is one of the most frequent causes of medical treatment in Germany, with around 500,000 new cases each year. In particular, there is a dramatic increase in liver tumors, which now represent the fifth most common tumor worldwide and which constitute the second leading cause of cancer-related death. Diagnosis and treatment are time-critical and require highly patient-specific diagnostic and treatment pathways. Medical decision-making is based on a multitude of interdependent factors related to various medical disciplines, past experiences and clinical guidelines. The consideration of all decision factors in combination with the possible therapy approaches is a major challenge for the physicians and often cannot be solved in an optimal way even in the interdisciplinary tumor board.
In this project, ATLAS, a decision support tool will be developed, which will significantly assist clinicians with this challenge. Based on AI methods, ATLAS processes all relevant patient data from databases, systems medicine and continuum biomechanical in silico prognoses modeling data as well as individual patient data. The tool will be developed in a co-design approach by experts in surgical oncology, mathematical modeling, and machine learning. Chosen technologies integrate the automated understanding of a highly complex patient situation through the simulation of liver functions with expert knowledge and ontology-guided learning with knowledge graphs from retrospective cases of liver tumors. ATLAS will be based on detailed historical data cohort from over 6,000 patients with liver tumors and will be evaluated on case studies at the Jena University Hospital. The integration of medical expert knowledge, mathematical modeling and artificial intelligence constitutes a highly original and most promising approach for high-quality diagnoses and treatments of liver tumors resulting in patient-specific prognosis improvement.
Scientific insights from this project will offer exploitation possibilities for the transfer to malignancies in other organs, such as lungs, kidneys or the brain. Tool and demonstrator development will provide for sustainable exploitation pathways for future commercial applications.
In addition to the spokesperson Tim Ricken (Mechanics U, Stuttgart), Steffen Staab, (Computer Science, U Stuttgart), Matthias König (Systems Biology, HU Berlin) and Hans-Michael Tautenhahn (Medicine, UK Jena) are involved.