AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Summary

This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.

Eligibility

Inclusion Criteria:

* Age 18 years or older;
* Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;
* Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;
* Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;
* CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;
* Patients possess comprehensive preoperative clinical information and post-operative TRG grading.

Exclusion Criteria:

* Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
* Patients diagnosed with other concurrent tumors.

Conditions4

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