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.

**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.

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.