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

NCT07616011 | Not Yet Recruiting

• 1 other identifier: SYSKY-2026-372-01
• Study Type: observational
• Target: 10,000
• Locations: 0 countries
• Sites: N/A

Brief 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

Outcome Measures

Primary Outcomes (1)

• Incidence of distant metastasis

  Proportion of patients with distant metastasis among malignant tumor cases

  At each routine follow-up visit (interval: approximately 6 months to 1 year)

Study Arms (2)

Model-predicted positive group

The model-predicted positive group is defined as patients predicted by the artificial intelligence model to develop distant metastasis in the future.

Model-predicted negative group

The model-predicted negative group is defined as patients predicted by the artificial intelligence model not to develop distant metastasis in the future.

Eligibility Criteria

• Age: 18 Years - 95 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The enrolled patients are those diagnosed with malignant tumors who receive treatment in hospitals across China and undergo regular follow-up.

You may qualify if:

• 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;
• Complete baseline clinicopathological data are available;
• Patients provide informed consent and permit researchers to collect and analyze their subsequent imaging and clinicopathological data.

You may not qualify if:

• 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.

Sponsors & Collaborators

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

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 4 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: May 19, 2026
• First Posted: May 29, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): December 31, 2036
• Study Completion (Estimated): December 31, 2036
• Last Updated: May 29, 2026

Record last verified: 2026-05