Cancer diagnosis and early detection are crucial for improving patient outcomes and survival rates. Early identification of cancers and appropriate intervention can significantly impact treatment success and prognosis. In clinical practice, oncologists often need to integrate a variety of patient data-including medical history, laboratory test results, imaging data such as CT scans and MRIs, and genetic markers-to make an accurate diagnosis and develop a personalized treatment plan.
To build the foundation for our work, first phase of the project was initiated in 2023, conducting a large-scale retrospective study. This foundational phase involved analyzing comprehensive, multimodal data from approximately 1 million cancer patients. The goal was to identify key patterns and build robust preliminary models.
As precision medicine becomes increasingly important, the challenge remains to identify cancer at early stages, especially when symptoms are subtle or absent. Building on the insights from our initial analysis, the project's second phase was launched in February 2025: a prospective study. This current study aims to develop and validate an AI-assisted decision-making system by integrating multimodal data from electronic health records, imaging, laboratory results, and genetic data in a real-world clinical setting. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized treatment options for cancer patients. Ultimately, through this comprehensive, two-phase approach, this system seeks to improve early detection, guide effective treatment strategies, and enhance patient survival rates.
