Gastric Adenocarcinoma
Conditions
Brief 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. 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.
Interventions
DIAGNOSTIC_TESTMultimodal AI-driven predictive model
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.
Sponsors
Qun Zhao
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
\*\*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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Prediction accuracy of postoperative recurrence in locally advanced gastric cancer | 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. |
Countries
China
Outcome results
None listed