  We introduce RadiomicsFill, a synthetic tumor generator conditioned on
radiomics features, enabling detailed control and individual manipulation of
tumor subregions. This conditioning leverages conventional high-dimensional
features of the tumor (i.e., radiomics features) and thus is biologically
well-grounded. Our model combines generative adversarial networks,
radiomics-feature conditioning, and multi-task learning. Through experiments
with glioma patients, RadiomicsFill demonstrated its capability to generate
diverse, realistic tumors and its fine-tuning ability for specific radiomics
features like 'Pixel Surface' and 'Shape Sphericity'. The ability of
RadiomicsFill to generate an unlimited number of realistic synthetic tumors
offers notable prospects for both advancing medical imaging research and
potential clinical applications.
