  Motivated by the question, "Can we generate tumors with desired attributes?''
this study leverages radiomics features to explore the feasibility of
generating synthetic tumor images. Characterized by its low-dimensional yet
biologically meaningful markers, radiomics bridges the gap between complex
medical imaging data and actionable clinical insights. We present
RadiomicsFill-Mammo, the first of the RadiomicsFill series, an innovative
technique that generates realistic mammogram mass images mirroring specific
radiomics attributes using masked images and opposite breast images, leveraging
a recent stable diffusion model. This approach also allows for the
incorporation of essential clinical variables, such as BI-RADS and breast
density, alongside radiomics features as conditions for mass generation.
Results indicate that RadiomicsFill-Mammo effectively generates diverse and
realistic tumor images based on various radiomics conditions. Results also
demonstrate a significant improvement in mass detection capabilities,
leveraging RadiomicsFill-Mammo as a strategy to generate simulated samples.
Furthermore, RadiomicsFill-Mammo not only advances medical imaging research but
also opens new avenues for enhancing treatment planning and tumor simulation.
Our code is available at https://github.com/nainye/RadiomicsFill.
