Multimodal Model Predicts Recurrence

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.

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Key information

Conditions

Gastric Adenocarcinoma

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

the Fourth Hospital of Hebei Medical University

Shijiazhuang, Hebei, 050011, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

**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.

Treatment and study plan

Multimodal AI-driven predictive model

Diagnostic 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.

Primary outcomes

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.

Sponsors and collaborators

Lead sponsor

Qun Zhao

Other

Registry information

Official study title

Multimodal Clinical-imaging-pathology-driven Artificial Intelligence Model for Predicting Postoperative Recurrence of Locally Advanced Gastric Cancer

Acronym: FUTURE12

Important dates

Study start: 2022
Primary completion: 2024
Study completion: 2024
First posted: Nov 15, 2024
Registry last updated: Nov 15, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed