Gastric CancerNCT07697079
Combination of CT and Ultrasound Radiomics Combined With Liquid Biopsy to Predict Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Gastric Cancer
An observational study of Gastric Cancer, run by Liu Yang at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Gastric Cancer
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 1 Feb 2027to 31 Dec 2030
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients with histologically confirmed GC at a locally advanced stage (cT2-4N0/+M0) who received NACT
Who may be able to join
- 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.
- Provision of peripheral blood samples before chemotherapy (for genetic and protein detection).
- Availability of postoperative pathological specimens for TRG grading after standardized neoadjuvant chemotherapy.
- Willing and able to comply with all study protocol requirements.
Who cannot join
- 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.
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
QianfoshanH
Jinan, Shandong, 250014
Guang yong Zhang, 053189269890, qykyc309@163.cm
About this study
Official title: 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
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.
- Conditions
- Gastric Cancer
- Keywords
- locally advanced gastric cancer, ultrasound, CT, liquid biopsies, radiomics, neoadjuvant chemotherapy, Pathological response
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Good pathological response | Patients with locally advanced gastric cancer achieved TRG grade 0-1 after the neoadjuvant chemotherapy | |
| Poor pathological response | Patients with locally advanced gastric cancer achieved TRG grade 2-3 after the neoadjuvant chemotherapy |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: The pathological response prediction model will be assessed immediately after its development.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jul 2026
- Study start1 Feb 2027 (estimated)
- Primary completion31 Dec 2029 (estimated)
- Study completion31 Dec 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Liu Yang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Liu Yang, Qianfoshan Hospital
- Investigators
- Guang yong Zhang, Principal investigator, Qianfoshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07697079
- Sponsor's protocol ID
- YXLL-KY-2026(046)
- Status last verified
- Jul 2026
- First submitted
- 5 Jul 2026
- Last updated
- 13 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The datasets utilized and analyzed in this study are not publicly available due to patient privacy requirements and ethical restriction.
Study at a glance
From the registry record, last updated by the sponsor on 13 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07697079
- Acronym / sponsor ID
- YXLL-KY-2026(046)
- Conditions
- Gastric Cancer
- Keywords
- locally advanced gastric cancer, ultrasound, CT, liquid biopsies, radiomics, neoadjuvant chemotherapy, Pathological response
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Liu YangUniversity, hospital or other
- Study start
- 1 Feb 2027 (estimated)
- Primary completion
- 31 Dec 2029 (estimated)
- Study completion
- 31 Dec 2030 (estimated)
- First posted
- 10 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Good pathological response | – | –Patients with locally advanced gastric cancer achieved TRG grade 0-1 after the neoadjuvant chemotherapy |
| Poor pathological response | – | –Patients with locally advanced gastric cancer achieved TRG grade 2-3 after the neoadjuvant chemotherapy |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients with histologically confirmed GC at a locally advanced stage (cT2-4N0/+M0) who received NACT
Key inclusion criteria (7 listed)
- 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.
- Provision of peripheral blood samples before chemotherapy (for genetic and protein detection).
Key exclusion criteria (5 listed)
- 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.
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of pathological response to neoadjuvant chemotherapyin patients with locally advanced gastric cancer models
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Jul 2026.
- Individual participant dataSharing planned: No. The datasets utilized and analyzed in this study are not publicly available due to patient privacy requirements and ethical restriction.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Guang yong Zhang principal investigator
Qianfoshan Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Cancer, run by Liu Yang at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 13 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Liu Yang (+8615168862857, yangliu102625@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of pathological response to neoadjuvant chemotherapyin patients with locally advanced gastric cancer models (time frame: The pathological response prediction model will be assessed immediately after its development.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Jul 2026.