This prospective cohort study aims to construct an artificial intelligence (AI)-derived predictive model for neoadjuvant chemotherapy response prediction in patients with locally advanced gastric cancer based on preoperative ultrasound (US), computed tomography (CT) images and liquid biopsy. Additionally, we explore the potential biological mechanisms behind this model.
Capable of understanding the study and voluntarily signing the written informed consent form (ICF) prior to any study-specified research procedures.
Aged ≥18 and ≤80 years old at the time of ICF signing.
Pathologically confirmed locally advanced gastric cancer (LAGC, cT2NxM0-cT4NxM0) with clinical indications for neoadjuvant chemotherapy.
Completion of gastrointestinal contrast-enhanced ultrasound and contrast-enhanced abdominal CT before neoadjuvant chemotherapy.
Diagnosis of non-primary gastric cancer.
Incomplete imaging data, failure to collect peripheral blood samples, or substandard sample quality.
Discontinued chemotherapy, modified treatment regimen, or lack of complete postoperative pathological assessment.
Unavailable follow-up data precluding evaluation of chemotherapy response.
Liu Yang
Lead sponsor
Qianfoshan Hospital
Sponsor institution