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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Malignant Tumor With Metastasis

• Study Type: Observational
• Enrollment (Estimated): 10000

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

1. Aged ≥ 18 years old;
2. Diagnosed with malignant tumor confirmed by histopathology;
3. No distant metastasis detected at baseline enrollment assessment;
4. Regular imaging examinations for distant metastasis assessment are scheduled in the routine follow-up protocol after enrollment;
5. Complete baseline clinicopathological data are available;
6. Patients provide informed consent and permit researchers to collect and analyze their subsequent imaging and clinicopathological data.

Exclusion Criteria:

1. Concurrent presence of two or more primary malignant tumors;
2. Presence of any medical or social factors that may interfere with completion of routine imaging follow-up.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)

Collaborators and Investigators

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

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): December 31, 2036
• Study Completion (Estimated): December 31, 2036

Study Registration Dates

• First Submitted: May 19, 2026
• First Submitted That Met QC Criteria: May 25, 2026
• First Posted (Actual): May 29, 2026

Study Record Updates

• Last Update Posted (Actual): May 29, 2026
• Last Update Submitted That Met QC Criteria: May 25, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Other Study ID Numbers: SYSKY-2026-372-01

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No