Gastric AdenocarcinomaFUTURE12NCT06690268
Multimodal Model Predicts Recurrence
An observational study of Gastric Adenocarcinoma, run by Qun Zhao at 1 site in China.
Trial summary
This study focuses on developing an advanced model that combines clinical information, imaging, and pathology data to predict the likelihood of cancer returning after surgery in patients with locally advanced gastric cancer. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Gastric Adenocarcinoma
- What is being tested?
- Multimodal AI-driven predictive model
- How many people
- 93 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2022to 31 Oct 2024
- How long will I take part?
- 24 Months
- 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 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population includes adult patients (aged 18 and older) diagnosed with locally advanced gastric cancer (Stage II or III) who have undergone surgical resection. This population is selected based on the availability of complete clinical, imaging, and pathology data necessary for analysis by the multimodal AI-driven predictive model. The study focuses on assessing the postoperative recurrence risk in this specific group to improve personalized follow-up and treatment planning.
Who may be able to join
- Patients diagnosed with locally advanced gastric cancer (Stage II or III).
- Patients who have undergone surgical resection for gastric cancer.
- Patients with complete clinical, imaging, and pathology data available for analysis.
- Age 18 years or older.
- Patients who provide informed consent to participate in the study.
Who cannot join
- Patients with distant metastasis (Stage IV) at the time of diagnosis.
- Patients with incomplete or missing clinical, imaging, or pathology data.
- Patients who have received prior treatment for gastric cancer other than surgical resection.
- Patients with other concurrent malignancies.
- Patients who are unable or unwilling to comply with the study follow-up requirements.
Trial locations and contacts
1 site in 1 country.
China1 site
the Fourth Hospital of Hebei Medical University
Shijiazhuang, Hebei, 050011
About this study
Official title: Multimodal Clinical-imaging-pathology-driven Artificial Intelligence Model for Predicting Postoperative Recurrence of Locally Advanced Gastric Cancer
This study focuses on developing an advanced model that combines clinical information, imaging, and pathology data to predict the likelihood of cancer returning after surgery in patients with locally advanced gastric cancer. By using artificial intelligence (AI), this model analyzes various data sources to create a more accurate prediction of recurrence risk, which can help doctors, patients, and families better understand the chances of recurrence. This AI-driven approach allows healthcare providers to make more informed decisions about personalized follow-up care and potential additional treatments to improve patient outcomes.
- Conditions
- Gastric Adenocarcinoma
- Being tested
- Multimodal AI-driven predictive model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal AI-driven predictive modelDiagnostic test
This intervention involves a multimodal artificial intelligence (AI) model that integrates clinical data, imaging results, and pathology findings to predict the risk of postoperative recurrence in patients with locally advanced gastric cancer. Unlike traditional methods that may rely on single data sources, this AI-driven model synthesizes multiple types of patient information, offering a comprehensive and personalized prediction of recurrence risk. This approach aims to improve accuracy in identifying high-risk patients, allowing for more tailored follow-up and treatment planning to enhance patient outcomes.
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction accuracy of postoperative recurrence in locally advanced gastric cancer
The primary outcome measure is the accuracy of the multimodal AI model in predicting the risk of postoperative recurrence in patients with locally advanced gastric cancer. This is assessed by comparing the model's predictions with actual recurrence events over a specified follow-up period, allowing evaluation of its effectiveness in identifying high-risk patients and guiding clinical decisions.
Measured: 24 months postoperative follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Nov 2024
- Study start1 Jan 2022
- Primary completion31 Oct 2024
- Study completion31 Oct 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric AdenocarcinomaStudies 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
- 93 participants (actual)
- Target duration
- 24 Months
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06690268
- Sponsor's protocol ID
- FUTURE12
- Acronym
- FUTURE12
- Status last verified
- Nov 2024
- First submitted
- 13 Nov 2024
- Last updated
- 15 Nov 2024
- 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 15 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Clinical-imaging-pathology-driven Artificial Intelligence Model for Predicting Postoperative Recurrence of Locally Advanced Gastric Cancer
- ClinicalTrials.gov ID
- NCT06690268
- Acronym / sponsor ID
- FUTURE12 / FUTURE12
- Conditions
- Gastric Adenocarcinoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 93 participants (actual)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Oct 2024 (actual)
- Study completion
- 31 Oct 2024 (actual)
- First posted
- 15 Nov 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.
This study focuses on developing an advanced model that combines clinical information, imaging, and pathology data to predict the likelihood of cancer returning after surgery in patients with locally advanced gastric cancer. By using artificial intelligence (AI), this model analyzes various data sources to create a more accurate prediction of recurrence risk, which can help doctors, patients, and families better understand the chances of recurrence. This AI-driven approach allows healthcare providers to make more informed decisions about personalized follow-up care and potential additional treatments to improve patient outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 24 Months
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population includes adult patients (aged 18 and older) diagnosed with locally advanced gastric cancer (Stage II or III) who have undergone surgical resection. This population is selected based on the availability of complete clinical, imaging, and pathology data necessary for analysis by the multimodal AI-driven predictive model. The study focuses on assessing the postoperative recurrence risk in this specific group to improve personalized follow-up and treatment planning.
Key inclusion criteria (5 listed)
- Patients diagnosed with locally advanced gastric cancer (Stage II or III).
- Patients who have undergone surgical resection for gastric cancer.
- Patients with complete clinical, imaging, and pathology data available for analysis.
- Age 18 years or older.
- Patients who provide informed consent to participate in the study.
Key exclusion criteria (5 listed)
- Patients with distant metastasis (Stage IV) at the time of diagnosis.
- Patients with incomplete or missing clinical, imaging, or pathology data.
- Patients who have received prior treatment for gastric cancer other than surgical resection.
- Patients with other concurrent malignancies.
- Patients who are unable or unwilling to comply with the study follow-up requirements.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Multimodal AI-driven predictive modelThis intervention involves a multimodal artificial intelligence (AI) model that integrates clinical data, imaging results, and pathology findings to predict the risk of postoperative recurrence in patients with locally advanced gastric cancer. Unlike traditional methods that may rely on single data sources, this AI-driven model synthesizes multiple types of patient information, offering a comprehensive and personalized prediction of recurrence risk. This approach aims to improve accuracy in identifying high-risk patients, allowing for more tailored follow-up and treatment planning to enhance patient outcomes. | Diagnostic test | – |
Primary outcome
Prediction accuracy of postoperative recurrence in locally advanced gastric cancer
Time frame: 24 months postoperative follow-up
The primary outcome measure is the accuracy of the multimodal AI model in predicting the risk of postoperative recurrence in patients with locally advanced gastric cancer. This is assessed by comparing the model's predictions with actual recurrence events over a specified follow-up period, allowing evaluation of its effectiveness in identifying high-risk patients and guiding clinical decisions.
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 15 Nov 2024.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Adenocarcinoma, run by Qun Zhao 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 5 inclusion and 5 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 15 Nov 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?
Prediction accuracy of postoperative recurrence in locally advanced gastric cancer (time frame: 24 months postoperative follow-up). 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 15 Nov 2024.