This is the companion blog post for a talk I gave on September 30, 2022 at the 6th European Hereditary Tumour Group (EHTG) Meeting in Mallorca on uncertainty quantification in oncology. The intended audience of the talk (and accordingly of this blog post) is mainly clinicians interested in novel methods that will influence cancer research in the future.
As this is a transcript of an eight minutes talk also this blog post cannot go to much into depth. But I hope that it will convince you that uncertainty quantification and mathematical modeling is a very useful tool for clinical decision making.
After a short intro on mathematical modeling I covered the various sources for uncertain data, and uncertainty quantification (UQ) as a means to handle them in a mathematical correct and also clinically relevant way. I illustrated UQ at the example of a mathematical model of Lynch syndrome carcinogenesis and pointed out a few connections to artificial intelligence before recommending a few simple points to make uncertain quantification as precise and clinically relevant as possible.