In the field of medicine, radiomics is a method that extracts a large number of features from medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover tumoral patterns and characteristics that fail to be appreciated by the naked eye. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various cancer types, thus providing valuable information for personalized therapy. Radiomics emerged from the medical fields of radiology and oncology and is the most advanced in applications within these fields. However, the technique can be applied to any medical study where a pathological process can be imaged. Process Image acquisition The image data is provided by radiological modalities as CT, MRI, PET/CT or even PET/MR. The produced raw data volumes are used to find different pixel/voxel characteristics through extraction tools. The extracted features are saved in large databases were clinics have access so as to enable broadly collaborative and cumulative work in which all can benefit from growing amounts of data, ideally enabling a more precise workflow. Image segmentation After the images have been saved in the database, they have to be reduced to the essential parts, in this case the tumors, which are called “volumes of interest”. Because of the large image data that needs to be processed, it would be too much work to perform the segmentation manually for every single image if a radiomics database with lots of data is created. Instead of manual segmentation, an automated process has to be used. A possible solution are automatic and semiautomatic segmentation algorithms. Before it can be applied on a big scale an algorithm must score as high as possible in the following four tasks: First, it must be reproducible, which means that when it is used on the same data the outcome will not change. Another important factor is the consistency. The algorithm does solve the problem at hand and performs the task rather than doing something that is not important. In this case, it is necessary that the algorithm can detect the diseased part in all different scans. The algorithm also needs to be accurate. It is very important that the algorithm detects the diseased part in the most precise way possible. Only with accurate data, accurate results can be achieved. A minor but still important point is the