This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing cancer, leveraging multimodal health data.

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.

Inclusion Criteria:

1、Patients with comprehensive electronic health records (EHRs), including medical history, laboratory test results, imaging data, and genetic data (if available).

2. Individuals without severe cognitive impairments or conditions that would prevent them from providing informed consent or participating in the study.

3. Parents or guardians must provide informed consent for minors, while adult participants must provide informed consent for themselves.

Exclusion Criteria:

1. Patients with incomplete or missing key electronic health record data or insufficient follow-up data.
2. Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation.
3. Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).

The study population consists of individuals aged 0 to 90 years who have received care at participating study centers. Participants must have comprehensive electronic health records (EHRs) available, including medical history, laboratory test results, imaging data, and genetic information (if available). Both individuals diagnosed with cancer (including pediatric and adult cancers) and healthy individuals with no history of cancer will be included in the study to evaluate the AI-assisted model's diagnostic and predictive capabilities. The study will focus on patients with complete and documented care records from the participating centers, ensuring a diverse cohort for analysis across different age groups and cancer types.