Multimodal Model Predicts Recurrence (FUTURE12)

November 13, 2024 updated by: Qun Zhao

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

93

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Hebei
      • Shijiazhuang, Hebei, China, 050011
        • The Fourth Hospital of Hebei Medical University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

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.

Description

**Inclusion Criteria:**

  • 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.

**Exclusion Criteria:**

  • 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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
24 months postoperative follow-up

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Qun Zhao

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2022

Primary Completion (Actual)

October 31, 2024

Study Completion (Actual)

October 31, 2024

Study Registration Dates

First Submitted

November 13, 2024

First Submitted That Met QC Criteria

November 13, 2024

First Posted (Actual)

November 15, 2024

Study Record Updates

Last Update Posted (Actual)

November 15, 2024

Last Update Submitted That Met QC Criteria

November 13, 2024

Last Verified

November 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • FUTURE12

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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