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.
patients with histologically confirmed GC at a locally advanced stage (cT2-4N0/+M0) who received NACT
Cohort
Prospective
Not listed
Accuracy of pathological response to neoadjuvant chemotherapyin patients with locally advanced gastric cancer models
This prospective study will collect contrast-enhanced abdominal CT and ultrasound images, as well as peripheral blood samples, from 300 patients with locally advanced gastric cancer (LAGC) prior to neoadjuvant chemotherapy. Using deep learning and machine learning algorithms, we will construct a tumor regression grade (TRG)-oriented model to predict pathological response to treatment. TRG classification is defined in accordance with the NCCN Guidelines (Version 4, 2021): TRG 0-1 indicates favorable response; TRG 2-3 poor response. The diagnostic accuracy and stability of the model will be evaluated, with performance quantified via the AUC and precision-recall curve.
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Liu Yang
Lead sponsor
Qianfoshan Hospital
Sponsor institution
This trial is not recruiting at the moment. You can still explore other options:
