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Artificial Intelligence-Based Early Warning for Distant Metastasis in Malignant Tumors

NCT07616011 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 10000

Last updated 2026-05-29

No results posted yet for this study

Summary

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.

Conditions

  • Malignant Tumor With Metastasis

Sponsors & Collaborators

  • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
95 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-01
Primary Completion
2036-12-31
Completion
2036-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07616011 on ClinicalTrials.gov