QianfoshanH
Jinan, Shandong, 250014, China
NCT Number: NCT07697079
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.
Trial opening soon.
Get Notified18 year–80 year
All sexes
Observational
Jinan, Shandong, 250014, China
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: The pathological response prediction model will be assessed immediately after its development.
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.
Contact information is provided by the study sponsor or research team.
Liu Yang
Other
Combination of CT and Ultrasound Radiomics Combined With Liquid Biopsy to Predict Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Gastric Cancer: A Prospective Study
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