Combination of CT and Ultrasound Radiomics Combined With Liquid Biopsy to Predict Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Gastric Cancer

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07697079

Enrollment

300

Registered

2026-07-10

Start date

2027-02-01

Completion date

2030-12-31

Last updated

2026-07-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gastric Cancer

Keywords

locally advanced gastric cancer, ultrasound, CT, liquid biopsies, radiomics, neoadjuvant chemotherapy, Pathological response

Brief summary

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

Inclusion criteria

1. Capable of understanding the study and voluntarily signing the written informed consent form (ICF) prior to any study-specified research procedures. 2. Aged ≥18 and ≤80 years old at the time of ICF signing. 3. Pathologically confirmed locally advanced gastric cancer (LAGC, cT2NxM0-cT4NxM0) with clinical indications for neoadjuvant chemotherapy. 4. Completion of gastrointestinal contrast-enhanced ultrasound and contrast-enhanced abdominal CT before neoadjuvant chemotherapy. 5. Provision of peripheral blood samples before chemotherapy (for genetic and protein detection). 6. Availability of postoperative pathological specimens for TRG grading after standardized neoadjuvant chemotherapy. 7. Willing and able to comply with all study protocol requirements.

Exclusion criteria

1. Diagnosis of non-primary gastric cancer. 2. Incomplete imaging data, failure to collect peripheral blood samples, or substandard sample quality. 3. Discontinued chemotherapy, modified treatment regimen, or lack of complete postoperative pathological assessment. 4. Unavailable follow-up data precluding evaluation of chemotherapy response. 5. Concurrent participation in another clinical trial; or any other conditions judged by investigators to warrant subject withdrawal, including severe comorbidities requiring simultaneous treatment (psychiatric disorders included), alcohol dependence, substance abuse, or familial/social factors that may compromise subject safety or treatment compliance.

Design outcomes

Primary

Measure	Time frame	Description
Accuracy of pathological response to neoadjuvant chemotherapyin patients with locally advanced gastric cancer models	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.

Countries

China

Contacts

CONTACTLiu Yang

yangliu102625@163.com+8615168862857

PRINCIPAL_INVESTIGATORGuang yong Zhang

Qianfoshan Hospital

Outcome results