3D Tumor

3D tumor research focuses on developing advanced computational methods to analyze and manipulate three-dimensional tumor representations from medical images. Current efforts concentrate on generating realistic synthetic tumors using generative AI models like diffusion models and GANs, often conditioned on radiomics features for precise control and biological relevance, and employing deep learning architectures such as U-Nets and recurrent neural networks for segmentation, localization, and motion prediction. These advancements improve the accuracy of tumor detection and segmentation, facilitate improved treatment planning and simulation, and enable more precise prediction of treatment response, ultimately leading to more effective and personalized cancer care.