Gastric Cancer With Positive Peritoneal Exfoliative CytologyGC-CY1NCT06759467
Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology
An observational study of Gastric Cancer With Positive Peritoneal Exfoliative Cytology, run by Qun Zhao at 1 site in China.
Trial summary
This clinical study aims to develop and evaluate an artificial intelligence (AI)-driven virtual biopsy technology for the diagnosis of gastric cancer with positive peritoneal exfoliative cytology (PEC). Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Gastric Cancer With Positive Peritoneal Exfoliative Cytology
- What is being tested?
- AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric Cance
- How many people
- 346 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2024to 30 Jun 2024
- How long will I take part?
- 24 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study will involve patients diagnosed with primary gastric cancer who have a clinical suspicion or documented evidence of peritoneal metastasis. Specifically, the study will target individuals with peritoneal exfoliative cytology (PEC)-positive gastric cancer, a condition where peritoneal involvement is suspected based on cytological examination of peritoneal lavage fluid.
Who may be able to join
- Age: Patients aged 18-75 years. Diagnosis of Gastric Cancer: Histologically confirmed diagnosis of primary gastric cancer, with clinical suspicion of peritoneal metastasis (based on imaging or other clinical findings).
- Positive Peritoneal Lavage Cytology (PEC): Patients with suspected PEC-positive gastric cancer, based on previous or current peritoneal lavage cytology results or high clinical suspicion.
- ECOG Performance Status: Eastern Cooperative Oncology Group (ECOG) performance status of 0-2, indicating that the patient is well enough to participate in the study and undergo necessary diagnostic procedures.
- Informed Consent: Ability and willingness to provide informed consent and comply with the study protocol.
Who cannot join
- Previous Cancer History: History of other malignancies within the past 5 years, except for non-melanoma skin cancer or in situ cancers.
- Severe Comorbidities: Severe cardiovascular, respiratory, renal, or hepatic disease that would impair the patient's ability to participate in the study or undergo the required diagnostic procedures.
- Pregnancy or Lactation: Pregnant or breastfeeding women, or women planning to become pregnant during the study period.
- Non-Eligible Clinical Conditions: Any condition that, in the opinion of the investigator, could interfere with the patient's participation or compliance with the study protocol, or affect the quality of the data.
- Inability to Provide Samples: Patients who are unable to provide the necessary clinical samples (e.g., blood, urine, or peritoneal lavage fluid) for the AI-driven virtual biopsy analysis.
Trial locations and contacts
1 site in 1 country.
China1 site
the Fourth Hospital of Hebei Medical University
Shijiazhuang, None Selected, 050011
About this study
This clinical study aims to develop and evaluate an artificial intelligence (AI)-driven virtual biopsy technology for the diagnosis of gastric cancer with positive peritoneal exfoliative cytology (PEC). Gastric cancer with peritoneal metastasis often presents a challenge for early detection and diagnosis, with traditional diagnostic methods such as imaging and histopathology being limited in sensitivity and specificity.
In this study, we propose the use of AI algorithms to analyze non-invasive biomarkers, including transcriptomic profiles and imaging data, to predict the presence of peritoneal exfoliative cytology-positive gastric cancer. Virtual biopsy leverages AI to integrate multiple datasets, providing a comprehensive diagnostic tool that could potentially replace or supplement current invasive diagnostic procedures. By developing this technology, we aim to improve the early diagnosis and monitoring of gastric cancer, particularly in cases with occult peritoneal metastasis, and ultimately enhance patient outcomes through more timely and accurate treatment strategies.
Read more
The study will involve the collection of clinical samples from gastric cancer patients with suspected peritoneal metastasis. The AI model will be trained on these samples to identify relevant biomarkers for PEC-positive gastric cancer. Clinical validation will be conducted to assess the performance of this AI-driven virtual biopsy system compared to conventional diagnostic methods.
This study has the potential to provide a novel, non-invasive diagnostic approach for gastric cancer with peritoneal involvement, offering a significant advancement in the field of early cancer detection and personalized medicine.
- Being tested
- AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric Cance
- Keywords
- gastric cancer with positive peritoneal exfoliative cytology
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric CanceDiagnostic test
The intervention involves the use of an artificial intelligence (AI)-driven virtual biopsy technology for the non-invasive diagnosis of gastric cancer with positive peritoneal exfoliative cytology (PEC). Unlike traditional biopsy methods, which require invasive procedures to obtain tissue samples, this intervention utilizes AI algorithms to analyze non-invasive biomarkers derived from patient samples such as blood, urine, or peritoneal lavage fluid.
The AI model is designed to integrate various data types, including transcriptomic profiling, imaging data, and other biomarkers, to predict the presence of PEC-positive gastric cancer. This technology employs advanced machine learning techniques to identify molecular and cellular features indicative of peritoneal metastasis, providing a diagnostic tool that is potentially more sensitive and less invasive than conventional methods.
The intervention is unique in its ability to combine multi-omics data (such as gene expression and imaging.
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI-Driven Virtual Biopsy in Diagnosing PEC-Positive Gastric Cancer
Measured: 24 months postoperative follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jan 2025
- Study start1 Jan 2024
- Primary completion30 Jun 2024
- Study completion30 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric Cancer With Positive Peritoneal Exfoliative CytologyStudies 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
- 346 participants (actual)
- Target duration
- 24 Months
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06759467
- Sponsor's protocol ID
- GC-CY1
- Acronym
- GC-CY1
- Status last verified
- Dec 2024
- First submitted
- 28 Dec 2024
- Last updated
- 6 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnosis of Peritoneal Exfoliative Cytology-positive Gastric Cancer Based on Artificial Intelligence-driven Virtual Biopsy Technology
- ClinicalTrials.gov ID
- NCT06759467
- Acronym / sponsor ID
- GC-CY1 / GC-CY1
- Keywords
- gastric cancer with positive peritoneal exfoliative cytology
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 346 participants (actual)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 30 Jun 2024 (actual)
- Study completion
- 30 Jun 2024 (actual)
- First posted
- 6 Jan 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 clinical study aims to develop and evaluate an artificial intelligence (AI)-driven virtual biopsy technology for the diagnosis of gastric cancer with positive peritoneal exfoliative cytology (PEC). Gastric cancer with peritoneal metastasis often presents a challenge for early detection and diagnosis, with traditional diagnostic methods such as imaging and histopathology being limited in sensitivity and specificity.
In this study, we propose the use of AI algorithms to analyze non-invasive biomarkers, including transcriptomic profiles and imaging data, to predict the presence of peritoneal exfoliative cytology-positive gastric cancer. Virtual biopsy leverages AI to integrate multiple datasets, providing a comprehensive diagnostic tool that could potentially replace or supplement current invasive diagnostic procedures. By developing this technology, we aim to improve the early diagnosis and monitoring of gastric cancer, particularly in cases with occult peritoneal metastasis, and ultimately enhance patient outcomes through more timely and accurate treatment strategies.
The study will involve the collection of clinical samples from gastric cancer patients with suspected peritoneal metastasis. The AI model will be trained on these samples to identify relevant biomarkers for PEC-positive gastric cancer. Clinical validation will be conducted to assess the performance of this AI-driven virtual biopsy system compared to conventional diagnostic methods.
This study has the potential to provide a novel, non-invasive diagnostic approach for gastric cancer with peritoneal involvement, offering a significant advancement in the field of early cancer detection and personalized medicine.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 24 Months
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study will involve patients diagnosed with primary gastric cancer who have a clinical suspicion or documented evidence of peritoneal metastasis. Specifically, the study will target individuals with peritoneal exfoliative cytology (PEC)-positive gastric cancer, a condition where peritoneal involvement is suspected based on cytological examination of peritoneal lavage fluid.
Key inclusion criteria (4 listed)
- Age: Patients aged 18-75 years. Diagnosis of Gastric Cancer: Histologically confirmed diagnosis of primary gastric cancer, with clinical suspicion of peritoneal metastasis (based on imaging or other clinical findings).
- Positive Peritoneal Lavage Cytology (PEC): Patients with suspected PEC-positive gastric cancer, based on previous or current peritoneal lavage cytology results or high clinical suspicion.
- ECOG Performance Status: Eastern Cooperative Oncology Group (ECOG) performance status of 0-2, indicating that the patient is well enough to participate in the study and undergo necessary diagnostic procedures.
- Informed Consent: Ability and willingness to provide informed consent and comply with the study protocol.
Key exclusion criteria (5 listed)
- Previous Cancer History: History of other malignancies within the past 5 years, except for non-melanoma skin cancer or in situ cancers.
- Severe Comorbidities: Severe cardiovascular, respiratory, renal, or hepatic disease that would impair the patient's ability to participate in the study or undergo the required diagnostic procedures.
- Pregnancy or Lactation: Pregnant or breastfeeding women, or women planning to become pregnant during the study period.
- Non-Eligible Clinical Conditions: Any condition that, in the opinion of the investigator, could interfere with the patient's participation or compliance with the study protocol, or affect the quality of the data.
- Inability to Provide Samples: Patients who are unable to provide the necessary clinical samples (e.g., blood, urine, or peritoneal lavage fluid) for the AI-driven virtual biopsy analysis.
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 |
|---|---|---|
| AI-Driven Virtual Biopsy for Diagnosis of Peritoneal Exfoliative Cytology-Positive Gastric CanceThe intervention involves the use of an artificial intelligence (AI)-driven virtual biopsy technology for the non-invasive diagnosis of gastric cancer with positive peritoneal exfoliative cytology (PEC). Unlike traditional biopsy methods, which require invasive procedures to obtain tissue samples, this intervention utilizes AI algorithms to analyze non-invasive biomarkers derived from patient samples such as blood, urine, or peritoneal lavage fluid. The AI model is designed to integrate various data types, including transcriptomic profiling, imaging data, and other biomarkers, to predict the presence of PEC-positive gastric cancer. This technology employs advanced machine learning techniques to identify molecular and cellular features indicative of peritoneal metastasis, providing a diagnostic tool that is potentially more sensitive and less invasive than conventional methods. The intervention is unique in its ability to combine multi-omics data (such as gene expression and imaging. | Diagnostic test | – |
Primary outcome
Accuracy of AI-Driven Virtual Biopsy in Diagnosing PEC-Positive Gastric Cancer
Time frame: 24 months postoperative follow-up
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jan 2025.
- Individual participant dataSharing planned: No
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. This study is not recruiting at the moment.
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 With Positive Peritoneal Exfoliative Cytology, 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 4 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: finished, as of the sponsor's last update on 6 Jan 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Accuracy of AI-Driven Virtual Biopsy in Diagnosing PEC-Positive Gastric Cancer (time frame: 24 months postoperative follow-up). 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 6 Jan 2025.