Malignant Tumor With MetastasisNCT07616011
Artificial Intelligence-Based Early Warning for Distant Metastasis in Malignant Tumors
An observational study of Malignant Tumor With Metastasis, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 95 years
- Conditions the study is for
- Malignant Tumor With Metastasis
- How many people
- 10,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jun 2026to 31 Dec 2036
- How long will I take part?
- 4 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 95 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The enrolled patients are those diagnosed with malignant tumors who receive treatment in hospitals across China and undergo regular follow-up.
Who may be able to join
- 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.
Who cannot join
- 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.
About this study
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
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
Incidence of distant metastasis
Proportion of patients with distant metastasis among malignant tumor cases
Measured: At each routine follow-up visit (interval: approximately 6 months to 1 year)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 May 2026
- Study start1 Jun 2026 (estimated)
- Primary completion31 Dec 2036 (estimated)
- Study completion31 Dec 2036 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Malignant Tumor With MetastasisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
- Target duration
- 4 Years
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chen Kai, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07616011
- Sponsor's protocol ID
- SYSKY-2026-372-01
- Status last verified
- May 2026
- First submitted
- 19 May 2026
- Last updated
- 29 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Based Early Warning for Distant Metastasis in Malignant Tumors
- ClinicalTrials.gov ID
- NCT07616011
- Acronym / sponsor ID
- SYSKY-2026-372-01
- Conditions
- Malignant Tumor With Metastasis
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2036 (estimated)
- Study completion
- 31 Dec 2036 (estimated)
- First posted
- 29 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 4 Years
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 95 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The enrolled patients are those diagnosed with malignant tumors who receive treatment in hospitals across China and undergo regular follow-up.
Key inclusion criteria (6 listed)
- 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;
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Incidence of distant metastasis
Time frame: At each routine follow-up visit (interval: approximately 6 months to 1 year)
Proportion of patients with distant metastasis among malignant tumor cases
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 May 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Malignant Tumor With Metastasis, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 29 May 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incidence of distant metastasis (time frame: At each routine follow-up visit (interval: approximately 6 months to 1 year)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 May 2026.