Gastric CancerNCT06208046
Predict 5-Year Survival in Elderly Gastric Cancer
An observational study of Gastric Cancer and Machine Learning, run by Fujian Medical University at 1 site in China.
Trial summary
In this study, elderly patients with gastric cancer who underwent radical gastrectomy in Union Hospital Affiliated to Fujian Medical University from 2012 to 2018 were included as a derived cohort, and the training set and internal validation set were randomly divided by 4:1. Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Gastric Cancer, Machine Learning
- How many people
- 2,187 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2023to 4 Jan 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 65 years and older
- All sexes
- No healthy volunteers
- Older adult
Study population: This retrospective prognostic study encompassed patients aged 65 or older who underwent radical gastrectomy for GC from January 2012 to April 2018. The study involved a derivation cohort and an external validation cohort.
Who may be able to join
- (1) GC diagnosis confirmed by abdominal computed tomography (CT) or biopsy; (2) age ≥65 years at diagnosis; (3) underwent radical surgical resection without evidence of distant metastasis; and (4) availability of complete clinical and pathological data.
Who cannot join
- (1) postoperative pathology confirming non-gastric primary tumors; (2) distant metastasis; (3) incomplete clinical data; and (4) other concurrent malignancies within five years.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Machine Learning Models to Predict 5-Year Post-Surgery Survival of Older Gastric Cancer Patients
In this study, elderly patients with gastric cancer who underwent radical gastrectomy in Union Hospital Affiliated to Fujian Medical University from 2012 to 2018 were included as a derived cohort, and the training set and internal validation set were randomly divided by 4:1. Machine learning strategies of random forest, decision tree and support vector machine are used to construct survival prediction model. Each model was tested in an internal validation set and an external validation set consisting of patients from two other large medical centers.
Read the detailed description
This is a retrospective, supervised learning, data mining study.
- Conditions
- Gastric Cancer, Machine Learning
- Keywords
- Gastric Cancer, Elderly, Oxidative Stress, Machine Learning, Overall Survival
- Medical subject headings
- Stomach Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
5-year overall survival
Survival status at 5 years: survival, death, survival with tumor, deletion.
Measured: 5 years or 60 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Jan 2024
- Study start1 Jul 2023
- Primary completion1 Jan 2024
- Study completion4 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Gastric CancerStudies of the same condition, open and under way.
- More trials run by Fujian Medical UniversityThe sponsor's other registered studies.
- 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
- Retrospective
- Enrolment
- 2,187 participants (actual)
Sponsor and investigators
- Lead sponsor
- Fujian Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Fujian Medical University Union Hospital
- Responsible party
- Principal investigator, Chang-Ming Huang, Prof., Fujian Medical University
- Investigators
- Huang Chang-Ming, Prof., Principal investigator, Fujian Medical University Union Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06208046
- Sponsor's protocol ID
- 2023KY237
- Status last verified
- Jan 2024
- First submitted
- 5 Jan 2024
- Last updated
- 17 Jan 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Zhang XQ, Huang ZN, Wu J, Zheng CY, Liu XD, Huang YQ, Chen QY, Li P, Xie JW, Zheng CH, Lin JX, Zhou YB, Huang CM. Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers. BMC Cancer. 2025 Feb 1;25(1):188. doi: 10.1186/s12885-025-13545-x.
PubMed 39893402
Study at a glance
From the registry record, last updated by the sponsor on 17 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Models to Predict 5-Year Post-Surgery Survival of Older Gastric Cancer Patients
- ClinicalTrials.gov ID
- NCT06208046
- Acronym / sponsor ID
- 2023KY237
- Conditions
- Gastric Cancer, Machine Learning
- Keywords
- Gastric Cancer, Elderly, Oxidative Stress, Machine Learning, Overall Survival
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,187 participants (actual)
- Lead sponsor
- Fujian Medical UniversityUniversity, hospital or other
- Collaborators
- Fujian Medical University Union Hospital
- Study start
- 1 Jul 2023 (actual)
- Primary completion
- 1 Jan 2024 (actual)
- Study completion
- 4 Jan 2024 (actual)
- First posted
- 17 Jan 2024 (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.
In this study, elderly patients with gastric cancer who underwent radical gastrectomy in Union Hospital Affiliated to Fujian Medical University from 2012 to 2018 were included as a derived cohort, and the training set and internal validation set were randomly divided by 4:1. Machine learning strategies of random forest, decision tree and support vector machine are used to construct survival prediction model. Each model was tested in an internal validation set and an external validation set consisting of patients from two other large medical centers.
Detailed description
This is a retrospective, supervised learning, data mining study.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
- Study population
- This retrospective prognostic study encompassed patients aged 65 or older who underwent radical gastrectomy for GC from January 2012 to April 2018. The study involved a derivation cohort and an external validation cohort.
Key inclusion criteria (1 listed)
- (1) GC diagnosis confirmed by abdominal computed tomography (CT) or biopsy; (2) age ≥65 years at diagnosis; (3) underwent radical surgical resection without evidence of distant metastasis; and (4) availability of complete clinical and pathological data.
Key exclusion criteria (1 listed)
- (1) postoperative pathology confirming non-gastric primary tumors; (2) distant metastasis; (3) incomplete clinical data; and (4) other concurrent malignancies within five years.
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
5-year overall survival
Time frame: 5 years or 60 months.
Survival status at 5 years: survival, death, survival with tumor, deletion.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Jan 2024.
- Derived from this study: PubMed 39893402Zhang XQ, Huang ZN, Wu J, Zheng CY, Liu XD, Huang YQ, Chen QY, Li P, Xie JW, Zheng CH, Lin JX, Zhou YB, Huang CM. Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers. BMC Cancer. 2025 Feb 1;25(1):188. doi: 10.1186/s12885-025-13545-x.
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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. This study is not recruiting at the moment.
Huang Chang-Ming, Prof. principal investigator
Fujian Medical University Union Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Cancer and Machine Learning, run by Fujian Medical University at 1 site in China.
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 1 inclusion and 1 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: finished, as of the sponsor's last update on 17 Jan 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
5-year overall survival (time frame: 5 years or 60 months.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06208046, which also lists 1 linked publication.
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 17 Jan 2024.