1. Locally Advanced Gastric Cancer (LAGC): The Primary Condition Under Investigation is Locally Advanced Gastric Cancer, Which Refers to StomachNCT06478394
Machine Learning-driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer
An observational study of 1. Locally Advanced Gastric Cancer (LAGC): The Primary Condition Under Investigation is Locally Advanced Gastric Cancer, Which Refers to Stomach, run by Qun Zhao at 1 site in China.
Trial summary
Brief Summary: Machine Learning-Driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer Gastric cancer, commonly known as stomach cancer, is a significant health issue worldwide, especially when it progresses to an advanced… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- 1. Locally Advanced Gastric Cancer (LAGC): The Primary Condition Under Investigation is Locally Advanced Gastric Cancer, Which Refers to Stomach
- What is being tested?
- Laparoscopic exploration
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 30 Jan 2024to 31 Dec 2025
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population will consist of patients diagnosed with locally advanced gastric cancer (LAGC). This specific group of patients is chosen because they are at a critical stage where early detection of occult peritoneal metastases can significantly impact treatment planning and outcomes.
Who may be able to join
- Diagnosis of Locally Advanced Gastric Cancer (LAGC): Patients must have a confirmed diagnosis of locally advanced gastric cancer, as determined by standard diagnostic procedures such as imaging and histopathological examination.
- Age: Participants must be adults aged 18 years or older. Consent: Patients must be able to provide informed consent to participate in the study.
- Adequate Organ Function: Participants should have adequate bone marrow, liver, and kidney function as defined by specific laboratory criteria (e.g., specific levels of hemoglobin, platelet count, liver enzymes, and creatinine clearance).
- Performance Status: Patients should have an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, indicating they are fully active, restricted in physically strenuous activity but ambulatory, or capable of all self-care but unable to carry out any work activities.
- Willingness to Provide Blood Samples: Participants must be willing to provide blood samples at specified time points throughout the study.
- Previous Treatment: Patients who have received prior treatments for gastric cancer (e.g., chemotherapy, radiation therapy, or surgery) may be included, provided there is a sufficient washout period as determined by the study protocol.
Who cannot join
- Distant Metastases: Patients with confirmed distant metastases (beyond the peritoneum) are excluded.
- Other Malignancies: Individuals with a history of other malignancies within the past five years, except for adequately treated basal cell or squamous cell skin cancer, or carcinoma in situ of the cervix.
- Severe Comorbid Conditions: Patients with severe or uncontrolled comorbid conditions, such as significant cardiovascular disease, uncontrolled diabetes, severe infections, or other conditions that could interfere with the study participation or outcomes.
- Pregnancy and Lactation: Pregnant or lactating women are excluded due to potential risks to the fetus or infant.
- Immunocompromised Status: Patients who are immunocompromised, such as those with HIV/AIDS, or who are receiving immunosuppressive therapy.
- Concurrent Participation in Other Clinical Trials: Individuals currently participating in another clinical trial that could interfere with this study's procedures or outcomes.
- Allergies to Study Materials: Patients with known allergies to any components of the study materials used for liquid biopsy processing and analysis.
- Non-compliance: Individuals deemed unable or unwilling to comply with the study procedures and follow-up requirements.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department of General Surgery
Shijiazhuang, Hebei, 050011
Ping'an Ding, 031186095363, ding_ping_an@hebmu.edu.cn
Qun Zhao, Doctor
About this study
Brief Summary: Machine Learning-Driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer
Gastric cancer, commonly known as stomach cancer, is a significant health issue worldwide, especially when it progresses to an advanced stage. One of the major challenges in treating locally advanced gastric cancer (LAGC) is the detection of occult (hidden) peritoneal metastases. These metastases are cancer cells that spread to the peritoneum (the lining of the abdominal cavity) but are not easily detectable with standard imaging techniques or during surgery. Early and accurate detection of these hidden metastases can significantly improve treatment strategies and outcomes for patients.
Read more
This clinical study explores an innovative approach to tackle this problem using machine learning (ML) technology and liquid biopsies. Liquid biopsies are a noninvasive method that involves analyzing blood samples to detect cancer-related biomarkers, such as circulating tumor DNA or RNA. This study specifically focuses on the transcriptomics of liquid biopsies, which refers to the analysis of RNA molecules to understand the gene expression profiles associated with cancer.
Hypothesis
The hypothesis of this study is that machine learning algorithms can effectively analyze transcriptomics data from liquid biopsies to detect occult peritoneal metastases in patients with locally advanced gastric cancer. By doing so, this method could provide a noninvasive, accurate, and early diagnosis of metastases, which are otherwise difficult to identify through traditional methods.
Study Design
- Participants: The study will enroll patients diagnosed with locally advanced gastric cancer. These patients will undergo standard diagnostic and staging procedures to confirm their cancer stage and overall health status.
- Sample Collection: Blood samples will be collected from the participants at various stages of their treatment journey. These samples will be processed to extract RNA, which will then be analyzed to obtain transcriptomic data.
- Machine Learning Analysis: Advanced machine learning algorithms will be employed to analyze the transcriptomic data from the liquid biopsies. The algorithms will be trained to identify patterns and markers associated with occult peritoneal metastases. The models will be continuously refined and validated using a subset of the collected data to ensure accuracy and reliability.
- Comparison with Traditional Methods: The results of the machine learning analysis will be compared with the outcomes of traditional diagnostic methods, such as imaging and surgical examinations, to evaluate the effectiveness of the ML-driven approach.
- Outcome Measures: The primary outcome measure will be the accuracy of the machine learning models in detecting occult peritoneal metastases compared to traditional methods. Secondary measures will include the impact of early detection on treatment decisions, patient outcomes, and overall survival rates.
Significance
Early and accurate detection of occult peritoneal metastases in locally advanced gastric cancer is crucial for effective treatment planning. Traditional diagnostic methods often fail to identify these hidden metastases until they have progressed, limiting the treatment options and adversely affecting patient prognosis. By leveraging machine learning technology to analyze transcriptomics data from liquid biopsies, this study aims to develop a noninvasive and reliable screening tool that can detect these metastases at an earlier stage.
Such an advancement could lead to several benefits, including:
- Improved Treatment Planning: Early detection allows for more tailored and effective treatment strategies, potentially including more aggressive therapies or surgical interventions when necessary.
- Better Patient Outcomes: With earlier and more accurate diagnosis, patients have a higher chance of receiving timely and appropriate treatments, which can improve survival rates and quality of life.
- Noninvasive Screening: Liquid biopsies are less invasive than traditional biopsy methods, reducing the physical and psychological burden on patients.
- Cost-Effectiveness: Early detection and treatment can potentially reduce the overall cost of care by preventing the need for more extensive and expensive treatments at later stages of the disease.
Conclusion
This clinical study represents a promising step forward in the fight against gastric cancer. By integrating machine learning with noninvasive liquid biopsy techniques, it aims to provide a new tool for the early detection of occult peritoneal metastases, ultimately improving outcomes for patients with locally advanced gastric cancer. The success of this study could pave the way for broader applications of machine learning in cancer diagnostics and personalized medicine.
- Being tested
- Laparoscopic exploration
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Laparoscopic explorationDiagnostic test
Laparoscopic exploration
What is being measured
The primary outcome is the main question the study is designed to answer.
Peritoneal metastasis
Measured: 2025-12-31
Secondary outcomes (1)
Peritoneal free cancer cells
Measured: 2025-12-31
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jun 2024
- Study start30 Jan 2024
- Primary completion31 Dec 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for 1. Locally Advanced Gastric Cancer (LAGC): The Primary Condition Under Investigation is Locally Advanced Gastric Cancer, Which Refers to StomachStudies 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
- Qun Zhao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06478394
- Sponsor's protocol ID
- FUTURE07
- Status last verified
- Jun 2024
- First submitted
- 22 Jun 2024
- Last updated
- 27 Jun 2024
- 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 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning-driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer
- ClinicalTrials.gov ID
- NCT06478394
- Acronym / sponsor ID
- FUTURE07
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 30 Jan 2024 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 27 Jun 2024 (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.
Brief Summary: Machine Learning-Driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer
Gastric cancer, commonly known as stomach cancer, is a significant health issue worldwide, especially when it progresses to an advanced stage. One of the major challenges in treating locally advanced gastric cancer (LAGC) is the detection of occult (hidden) peritoneal metastases. These metastases are cancer cells that spread to the peritoneum (the lining of the abdominal cavity) but are not easily detectable with standard imaging techniques or during surgery. Early and accurate detection of these hidden metastases can significantly improve treatment strategies and outcomes for patients.
This clinical study explores an innovative approach to tackle this problem using machine learning (ML) technology and liquid biopsies. Liquid biopsies are a noninvasive method that involves analyzing blood samples to detect cancer-related biomarkers, such as circulating tumor DNA or RNA. This study specifically focuses on the transcriptomics of liquid biopsies, which refers to the analysis of RNA molecules to understand the gene expression profiles associated with cancer.
Hypothesis
The hypothesis of this study is that machine learning algorithms can effectively analyze transcriptomics data from liquid biopsies to detect occult peritoneal metastases in patients with locally advanced gastric cancer. By doing so, this method could provide a noninvasive, accurate, and early diagnosis of metastases, which are otherwise difficult to identify through traditional methods.
Study Design
- Participants: The study will enroll patients diagnosed with locally advanced gastric cancer. These patients will undergo standard diagnostic and staging procedures to confirm their cancer stage and overall health status.
- Sample Collection: Blood samples will be collected from the participants at various stages of their treatment journey. These samples will be processed to extract RNA, which will then be analyzed to obtain transcriptomic data.
- Machine Learning Analysis: Advanced machine learning algorithms will be employed to analyze the transcriptomic data from the liquid biopsies. The algorithms will be trained to identify patterns and markers associated with occult peritoneal metastases. The models will be continuously refined and validated using a subset of the collected data to ensure accuracy and reliability.
- Comparison with Traditional Methods: The results of the machine learning analysis will be compared with the outcomes of traditional diagnostic methods, such as imaging and surgical examinations, to evaluate the effectiveness of the ML-driven approach.
- Outcome Measures: The primary outcome measure will be the accuracy of the machine learning models in detecting occult peritoneal metastases compared to traditional methods. Secondary measures will include the impact of early detection on treatment decisions, patient outcomes, and overall survival rates.
Significance
Early and accurate detection of occult peritoneal metastases in locally advanced gastric cancer is crucial for effective treatment planning. Traditional diagnostic methods often fail to identify these hidden metastases until they have progressed, limiting the treatment options and adversely affecting patient prognosis. By leveraging machine learning technology to analyze transcriptomics data from liquid biopsies, this study aims to develop a noninvasive and reliable screening tool that can detect these metastases at an earlier stage.
Such an advancement could lead to several benefits, including:
- Improved Treatment Planning: Early detection allows for more tailored and effective treatment strategies, potentially including more aggressive therapies or surgical interventions when necessary.
- Better Patient Outcomes: With earlier and more accurate diagnosis, patients have a higher chance of receiving timely and appropriate treatments, which can improve survival rates and quality of life.
- Noninvasive Screening: Liquid biopsies are less invasive than traditional biopsy methods, reducing the physical and psychological burden on patients.
- Cost-Effectiveness: Early detection and treatment can potentially reduce the overall cost of care by preventing the need for more extensive and expensive treatments at later stages of the disease.
Conclusion
This clinical study represents a promising step forward in the fight against gastric cancer. By integrating machine learning with noninvasive liquid biopsy techniques, it aims to provide a new tool for the early detection of occult peritoneal metastases, ultimately improving outcomes for patients with locally advanced gastric cancer. The success of this study could pave the way for broader applications of machine learning in cancer diagnostics and personalized medicine.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of patients diagnosed with locally advanced gastric cancer (LAGC). This specific group of patients is chosen because they are at a critical stage where early detection of occult peritoneal metastases can significantly impact treatment planning and outcomes.
Key inclusion criteria (6 listed)
- Diagnosis of Locally Advanced Gastric Cancer (LAGC): Patients must have a confirmed diagnosis of locally advanced gastric cancer, as determined by standard diagnostic procedures such as imaging and histopathological examination.
- Age: Participants must be adults aged 18 years or older. Consent: Patients must be able to provide informed consent to participate in the study.
- Adequate Organ Function: Participants should have adequate bone marrow, liver, and kidney function as defined by specific laboratory criteria (e.g., specific levels of hemoglobin, platelet count, liver enzymes, and creatinine clearance).
- Performance Status: Patients should have an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, indicating they are fully active, restricted in physically strenuous activity but ambulatory, or capable of all self-care but unable to carry out any work activities.
- Willingness to Provide Blood Samples: Participants must be willing to provide blood samples at specified time points throughout the study.
Key exclusion criteria (8 listed)
- Distant Metastases: Patients with confirmed distant metastases (beyond the peritoneum) are excluded.
- Other Malignancies: Individuals with a history of other malignancies within the past five years, except for adequately treated basal cell or squamous cell skin cancer, or carcinoma in situ of the cervix.
- Severe Comorbid Conditions: Patients with severe or uncontrolled comorbid conditions, such as significant cardiovascular disease, uncontrolled diabetes, severe infections, or other conditions that could interfere with the study participation or outcomes.
- Pregnancy and Lactation: Pregnant or lactating women are excluded due to potential risks to the fetus or infant.
- Immunocompromised Status: Patients who are immunocompromised, such as those with HIV/AIDS, or who are receiving immunosuppressive therapy.
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 |
|---|---|---|
| Laparoscopic explorationLaparoscopic exploration | Diagnostic test | – |
Primary outcome
Peritoneal metastasis
Time frame: 2025-12-31
1 secondary outcome
Peritoneal free cancer cells
Time frame: 2025-12-31
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jun 2024.
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.
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 1. Locally Advanced Gastric Cancer (LAGC): The Primary Condition Under Investigation is Locally Advanced Gastric Cancer, Which Refers to Stomach, 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 6 inclusion and 8 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: status not confirmed, as of the sponsor's last update on 27 Jun 2024. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Peritoneal metastasis (time frame: 2025-12-31). 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 Jun 2024.