AI-Based Prediction of Treatment Response and Recurrence in Gastric Cancer

Gachon University Gil Medical Center0 个研究点目标入组 3,200 人开始时间: 2025年12月3日最近更新: 2026年8月3日

适应症

试验速览

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研究类型: Observational
观察模型: Cohort, Time Perspective : Retrospective, Enrollment : 3200, Biospecimen Retention : Not collect nor Archive

•Patients with histologically confirmed gastric cancer.
•Patients who received anti-cancer treatment (e.g., surgical intervention, endoscopic resection, or systemic pharmacotherapy) following gastric cancer diagnosis.
•Patients with at least one of the following data types available pre- or post-treatment:
•o Radiological/Endoscopic Imaging: Chest CT, abdominal CT/ultrasound, or endoscopic images.
•o Pathology: Histopathological slides or tissue biopsy reports.
•o Laboratory/Genomic Data: Blood tests or Next-Generation Sequencing (NGS) data.
•Patients with linkable temporal metadata across imaging, pathology, and genomic datasets (e.g., feasibility of pre- and post-treatment RECIST(Response Evaluation Criteria in Solid Tumors) response evaluation).

•Unusable imaging or pathological slide data due to poor quality (e.g., severe artifacts, low resolution).
•Withdrawal of patient consent or existence of re-identification risk.
•Unclear history of anti-cancer treatment or uncertain timing of therapeutic response evaluation.
•Synchronous double primary cancer or severe systemic comorbidities that significantly confound gastric cancer prognosis.
•Follow-up duration of less than 1 month.

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