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.

本研究利用放射组学特征探索了生成合成肿瘤图像的可行性，通过RadiomicsFill-Mammo技术，使用遮罩图像和对侧乳房图像生成模拟的乳腺肿块图像，此方法能根据放射组学特征生成真实多样的肿瘤图像，并显著改进了肿块检测能力，为治疗规划和肿瘤模拟开辟了新的研究方向。

放射影像学填充乳房X光照片:基于放射影像学特征的合成乳房X光照片肿块处理