Gastric CancerNCT06947096
Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients
An observational study of Gastric Cancer and Para-Aortic Lymph Node Metastasis, run by Qun Zhao at 1 site in China.
Trial summary
This study aims to develop and validate an artificial intelligence (AI) model based on radiomics features extracted from preoperative CT images to predict para-aortic lymph node (PALN) metastasis in patients with gastric cancer. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Gastric Cancer, Para-Aortic Lymph Node Metastasis, Lymphatic Metastasis
- What is being tested?
- Radiomics-Based AI Imaging Analysis
- How many people
- 120 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2025to 30 Jun 2025
- 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
- Adult, Older adult
Study population: The study population will consist of adult patients diagnosed with gastric adenocarcinoma who are scheduled to undergo radical gastrectomy at a tertiary care center. All participants will have preoperative contrast-enhanced CT scans and no evidence of distant metastasis. The population represents individuals at risk of para-aortic lymph node metastasis, and is intended to reflect real-world patients who may benefit from non-invasive, AI-assisted preoperative assessment tools. Participants will be enrolled consecutively to minimize selection bias.
Who may be able to join
- Adults aged 18-80 years.
- Histologically confirmed gastric adenocarcinoma.
- Planned to undergo radical gastrectomy with or without para-aortic lymph node dissection.
- Preoperative contrast-enhanced abdominal CT scan available within 3 weeks before surgery.
- No evidence of distant metastasis on imaging.
- ECOG performance status 0-2.
- Provided written informed consent.
Who cannot join
- History of other malignant tumors within the past 5 years.
- Received neoadjuvant chemotherapy or radiotherapy prior to CT imaging.
- Poor-quality or incomplete CT images not suitable for radiomics analysis.
- Severe comorbidities that may affect prognosis or surgical decision-making.
- Pregnancy or breastfeeding.
- Inability to provide informed consent or comply with study procedures.
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
the Fourth Hospital of Hebei Medical University
Shijiazhuang, None Selected, 050011
About this study
Official title: A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer
This study aims to develop and validate an artificial intelligence (AI) model based on radiomics features extracted from preoperative CT images to predict para-aortic lymph node (PALN) metastasis in patients with gastric cancer. Accurately identifying PALN metastasis before surgery can help doctors make better treatment decisions, such as whether to proceed with surgery, consider chemotherapy, or use other treatment strategies. The study will prospectively enroll patients who are diagnosed with gastric cancer and scheduled for surgery. All participants will undergo routine imaging tests, and their data will be analyzed using advanced AI techniques. The results of this study may improve the precision of preoperative staging and support personalized treatment planning for gastric cancer patients.
- Conditions
- Gastric Cancer, Para-Aortic Lymph Node Metastasis, Lymphatic Metastasis, Preoperative Imaging Assessment, Radiomics, Artificial Intelligence
- Being tested
- Radiomics-Based AI Imaging Analysis
- Medical subject headings
- Stomach Neoplasms, Lymphatic Metastasis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Radiomics-Based AI Imaging AnalysisDiagnostic test
This intervention involves the development and application of a radiomics-based artificial intelligence (AI) model to analyze preoperative abdominal CT images of patients with gastric cancer. The AI algorithm extracts high-dimensional imaging features from the para-aortic region to predict the presence or absence of para-aortic lymph node metastasis (PALNM). This non-invasive method aims to assist clinicians in preoperative risk stratification and treatment planning. The model will be trained and validated using manually segmented lymph node regions and correlated with postoperative pathological findings to ensure accuracy and clinical relevance.
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer
The primary outcome is the diagnostic performance of the radiomics-based AI model in predicting para-aortic lymph node metastasis (PALNM) in patients with gastric cancer. Performance will be evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and predictive values. The ground truth for PALNM status will be based on postoperative pathological findings or multidisciplinary consensus diagnosis. The model's predictions will be compared with actual clinical outcomes to assess its reliability and clinical utility.
Measured: From Preoperative Imaging to Postoperative Pathological Confirmation (Approximately 4-6 Weeks per Patient)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Apr 2025
- Study start1 Jan 2025
- Primary completion30 Jun 2025 (estimated)
- Study completion30 Jun 2025 (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
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Collaborators
- First Hospital of Shijiazhuang City, Baoding First Central Hospital, Hengshui People's Hospital
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06947096
- Sponsor's protocol ID
- GC-RAD-AI-2025-01
- Status last verified
- Apr 2025
- First submitted
- 21 Apr 2025
- Last updated
- 27 Apr 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Clinical Study of Radiomics-Based Artificial Intelligence for Predicting Para-Aortic Lymph Node Metastasis in Patients With Gastric Cancer
- ClinicalTrials.gov ID
- NCT06947096
- Acronym / sponsor ID
- GC-RAD-AI-2025-01
- Conditions
- Gastric Cancer, Para-Aortic Lymph Node Metastasis, Lymphatic Metastasis, Preoperative Imaging Assessment, Radiomics, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Collaborators
- First Hospital of Shijiazhuang City, Baoding First Central Hospital, Hengshui People's Hospital
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 30 Jun 2025 (estimated)
- Study completion
- 30 Jun 2025 (estimated)
- First posted
- 27 Apr 2025 (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 study aims to develop and validate an artificial intelligence (AI) model based on radiomics features extracted from preoperative CT images to predict para-aortic lymph node (PALN) metastasis in patients with gastric cancer. Accurately identifying PALN metastasis before surgery can help doctors make better treatment decisions, such as whether to proceed with surgery, consider chemotherapy, or use other treatment strategies. The study will prospectively enroll patients who are diagnosed with gastric cancer and scheduled for surgery. All participants will undergo routine imaging tests, and their data will be analyzed using advanced AI techniques. The results of this study may improve the precision of preoperative staging and support personalized treatment planning for gastric cancer patients.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of adult patients diagnosed with gastric adenocarcinoma who are scheduled to undergo radical gastrectomy at a tertiary care center. All participants will have preoperative contrast-enhanced CT scans and no evidence of distant metastasis. The population represents individuals at risk of para-aortic lymph node metastasis, and is intended to reflect real-world patients who may benefit from non-invasive, AI-assisted preoperative assessment tools. Participants will be enrolled consecutively to minimize selection bias.
Key inclusion criteria (7 listed)
- Adults aged 18-80 years.
- Histologically confirmed gastric adenocarcinoma.
- Planned to undergo radical gastrectomy with or without para-aortic lymph node dissection.
- Preoperative contrast-enhanced abdominal CT scan available within 3 weeks before surgery.
- No evidence of distant metastasis on imaging.
Key exclusion criteria (6 listed)
- History of other malignant tumors within the past 5 years.
- Received neoadjuvant chemotherapy or radiotherapy prior to CT imaging.
- Poor-quality or incomplete CT images not suitable for radiomics analysis.
- Severe comorbidities that may affect prognosis or surgical decision-making.
- Pregnancy or breastfeeding.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Radiomics-Based AI Imaging AnalysisThis intervention involves the development and application of a radiomics-based artificial intelligence (AI) model to analyze preoperative abdominal CT images of patients with gastric cancer. The AI algorithm extracts high-dimensional imaging features from the para-aortic region to predict the presence or absence of para-aortic lymph node metastasis (PALNM). This non-invasive method aims to assist clinicians in preoperative risk stratification and treatment planning. The model will be trained and validated using manually segmented lymph node regions and correlated with postoperative pathological findings to ensure accuracy and clinical relevance. | Diagnostic test | – |
Primary outcome
Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer
Time frame: From Preoperative Imaging to Postoperative Pathological Confirmation (Approximately 4-6 Weeks per Patient)
The primary outcome is the diagnostic performance of the radiomics-based AI model in predicting para-aortic lymph node metastasis (PALNM) in patients with gastric cancer. Performance will be evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and predictive values. The ground truth for PALNM status will be based on postoperative pathological findings or multidisciplinary consensus diagnosis. The model's predictions will be compared with actual clinical outcomes to assess its reliability and clinical utility.
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 27 Apr 2025.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 and Para-Aortic Lymph Node Metastasis, run by Qun Zhao 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 6 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: enrolling by invitation, as of the sponsor's last update on 27 Apr 2025. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the AI Radiomics Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer (time frame: From Preoperative Imaging to Postoperative Pathological Confirmation (Approximately 4-6 Weeks per Patient)). 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 27 Apr 2025.