AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Chinese Academy of Sciences
200 participants
Sep 10, 2023
OBSERVATIONAL
Conditions
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 Criteria6
- 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 Criteria2
- Patients whose CT or pathology images are unclear, making lesion assessment infeasible;
- Patients diagnosed with other concurrent tumors.
Interventions
Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.
Locations(22)
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NCT06035250