The study focused on localizing and detecting the volume of tissues surrounding the brain tumor to be irradiated, the so-called organs at risk, the protection of which is given top priority in radiotherapy. Traditionally, CT and MRI images are interpreted by specially trained medical professionals who manually delineate the contours of organs at risk. This procedure is not only extremely time consuming, but also requires several physicians to analyze an image in order to exclude individual deviations or misinterpretations. In modern systems, this work is increasingly performed by automated software that interprets images based on the use of AI to plot the desired contours or calculate volumes. The present study investigated to which extent currently used metrics for automated image segmentation lead to clinically meaningful conclusions about dose distribution in organs at risk near brain tumors. Data from glioblastomas, a common, aggressive type of brain tumor, were used.
Metrics of automatic, AI-based image segmentation could be improved
Surprisingly, the current methods used to determine the quality of AI based automated contouring do not have any predictive value on the quality of the treatment. Additionally, it was found that in many cases, mis-interpretations of the organs at risk have very little impact on the dose that would be delivered during radiotherapy. This means that the metrics used today in AI-assisted image evaluation could be reconsidered, and this changes the direction of improving AI methods and the implementation in day-to-day practice.