Hepatocellular carcinomas (HCCs) are a heterogeneous group of tumor subtypes with a different response behavior and prognosis. As a reaction, the World Health Organization (WHO) in its 5th version (updated in 2019) classifies no more two but eight subtypes, each with a different tumor biology and outcome. The new classification may serve as a key factor optimizing a more personalized therapeutic approach and therefore, especially diagnostic disciplines have to implement these new subtypes as soon as possible into their daily clinical routine algorithms.~Imaging does play a key role in this situation. Newer and advanced MRI techniques allow a precise tissue characterization. Furthermore, with the help of latest generation hepatobiliary contrast agents like the usage of Gd-EOB (Primovist) it is possible to quantify and measure the organ function and specific uptake behavior of focal liver lesions. Another approach that hold promise for advancing the characterization of HCCs heterogeneity is the use and development of artificial intelligence (AI)-based image postprocessing algorithms including radiomics analysis.~To date there aren't any established imaging features correlating with any of the new WHO HCC-subtypes. The goal of our project is to identify imaging biomarkers correlating with the new HCC-subtypes, helping to classify them noninvasively. As a next step with the help of our collaborators we will facilitate a radiological-pathological reference database. In a third step and with the help of the data we curated we will try to identify morphologic imaging characteristics by the use of AI-based post-processing algorithms to classify the subtypes noninvasively and to predict / estimate patients individual therapy response and prognosis. The last challenge will be to implement these algorithms into daily clinical routine, we therefore have to identify interface dilemmas and present smart solutions to solve them.~We are convinced that by implementing the updated WHO-criteria into clinical workflows current believes and guidelines in the diagnosis and therapy of HCC will change. MRI HCC imaging with Primovist will play a key role in this project. The results of our project may provide the knowledge to represent as a cornerstone in imaging and therapy assessment of HCC to improve a personalized therapy approach.
Non-invasive MRI subclassification of Heptocellular Carcinoma - HepCaSt-Study