  Tumor synthesis enables the creation of artificial tumors in medical images,
facilitating the training of AI models for tumor detection and segmentation.
However, success in tumor synthesis hinges on creating visually realistic
tumors that are generalizable across multiple organs and, furthermore, the
resulting AI models being capable of detecting real tumors in images sourced
from different domains (e.g., hospitals). This paper made a progressive stride
toward generalizable tumor synthesis by leveraging a critical observation:
early-stage tumors (< 2cm) tend to have similar imaging characteristics in
computed tomography (CT), whether they originate in the liver, pancreas, or
kidneys. We have ascertained that generative AI models, e.g., Diffusion Models,
can create realistic tumors generalized to a range of organs even when trained
on a limited number of tumor examples from only one organ. Moreover, we have
shown that AI models trained on these synthetic tumors can be generalized to
detect and segment real tumors from CT volumes, encompassing a broad spectrum
of patient demographics, imaging protocols, and healthcare facilities.
