About the research topic
Soft tissue tumors (STT) are a rare and complex group of lesions with a broad range of differentiation. All STT subtypes greatly differ in their clinical behavior, aggressiveness, molecular background, and preferred treatments given. Diagnosis of the correct phenotype, the grade of aggressiveness, and molecular make-up is therefore of utmost importance. Diagnosis of STT is generally supported by imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI). However, visual assessment by a radiologist tends to be subjective and not precise. Quantitative, computational (“radiomics”) imaging features and state-of-the-art Artificial Intelligence (AI) techniques based on machine learning could enable more objective and precise STT diagnosis. With the support of the Hanarth Foundation, we develop a comprehensive STT diagnostic model, both for phenotyping and grading, based on quantitative image analysis by radiomics and deep learning. Our AI model will guide diagnosis and treatment decisions, thereby facilitating personalized medicine.
In order to train and validate this model we have setup a large, multi-center cohort. This Sarcoma Artificial Intelligence (SAI) consortium is a collaboration between various sarcoma expert centers in Europe and the United States.