Early detection and timely intervention of distant metastasis are essential for improving the prognosis of patients with malignant tumors. However, current clinical methods have notable limitations. Conventional imaging can only detect macroscopic metastatic lesions, failing to seize the optimal intervention window before metastasis occurs or during the micrometastasis stage. Previous research has adopted artificial intelligence to break the constraints of traditional imaging and realized subclinical early warning of distant metastasis based on retrospective data. On this basis, the present study aims to systematically validate the predictive performance and generalizability of the model in real-world clinical settings via a prospective cohort. This study intends to establish an organ-specific, non-invasive and cost-effective pan-cancer tool for early warning of distant metastasis. It can gain critical time for clinical intervention, help reduce the incidence of distant metastasis and ultimately optimize patient prognosis.
Aged ≥ 18 years old;
Diagnosed with malignant tumor confirmed by histopathology;
No distant metastasis detected at baseline enrollment assessment;
Regular imaging examinations for distant metastasis assessment are scheduled in the routine follow-up protocol after enrollment;
Concurrent presence of two or more primary malignant tumors;
Presence of any medical or social factors that may interfere with completion of routine imaging follow-up.