Gastric (Stomach) CancerBUDDLENCT06858644
Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning
An observational study of Gastric (Stomach) Cancer, run by Qun Zhao.
Trial summary
This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Gastric (Stomach) Cancer
- What is being tested?
- BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- 1 Mar 2025to 1 Mar 2027
- How long will I take part?
- 5 Years
- 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 75 years
- All sexes
- Adult, Older adult
Study population: The study population will consist of adult patients diagnosed with gastric cancer (GC) at various stages of the disease. Patients will be selected based on the presence of or risk factors for peritoneal metastasis and/or cachexia, which will be assessed through clinical evaluations, imaging (CT/MRI), and histopathological examination. Participants will be recruited from a cohort of GC patients who have available genomic, radiological, and pathological data, which are essential for training the predictive model. The study will focus on patients with a broad spectrum of GC manifestations, including both early and advanced stages, to ensure the model is applicable across different disease profiles. This diverse population will help evaluate the robustness and generalizability of the model in predicting peritoneal metastasis and cachexia, aiming for a comprehensive representation of GC progression.
Who may be able to join
- Adults aged 18-75 years diagnosed with gastric cancer (GC) at any stage. Histopathologically confirmed GC with available radiological (CT/MRI) and pathological data (biopsy samples).
- Patients with or at risk of peritoneal metastasis and/or cachexia, as determined by clinical assessment and imaging.
- Ability to provide informed consent and comply with study protocols. Willingness to undergo regular follow-up imaging and clinical evaluation for the duration of the study.
Who cannot join
- Patients with other primary cancers or serious comorbidities (e.g., severe cardiovascular disease, uncontrolled diabetes).
- Pregnant or breastfeeding women. Patients with contraindications to MRI or CT imaging. Those with insufficient clinical data (e.g., missing radiopathological information) for model training.
- Patients who are unable or unwilling to comply with the study protocol, including follow-up visits and evaluations.
About this study
This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. The study will focus on utilizing imaging genomics (radiomics) and histopathological data to identify early biomarkers for peritoneal metastasis and cachexia in GC patients. By leveraging deep learning algorithms, the project seeks to improve the accuracy and reliability of predictions, enabling earlier intervention and personalized treatment strategies. The ultimate goal is to enhance clinical decision-making and prognosis prediction in GC patients with peritoneal metastasis and cachexia.
Read the detailed description
Gastric cancer (GC) is one of the most common and aggressive malignancies, with peritoneal metastasis and cachexia significantly contributing to its poor prognosis. The BUB1 gene has been implicated in chromosomal instability and the progression of GC, but its role in peritoneal metastasis and cachexia remains unclear. This clinical trial aims to explore the potential of integrating BUB1 gene expression with imaging and pathological data to develop a predictive model for GC progression.
The study will collect comprehensive data from GC patients, including genomic profiles (BUB1 gene expression), radiological images (CT/MRI scans), and histopathological findings. Advanced radiomics analysis will extract quantitative features from imaging data, while pathological data will be analyzed for relevant histological markers. The combined dataset will be fed into a deep learning model to identify patterns associated with peritoneal metastasis and cachexia, focusing on the identification of early biomarkers.
The deep learning model will undergo iterative training and validation using both retrospective and prospective patient data. The primary endpoint of the trial is to assess the model's predictive accuracy for peritoneal metastasis and cachexia development, while secondary endpoints include its potential to inform personalized treatment strategies, improve survival rates, and guide clinical decision-making.
This study will also investigate the correlation between BUB1 expression and the radiopathomics features in GC, providing insights into the underlying mechanisms driving peritoneal metastasis and cachexia. The findings aim to establish a robust, clinically applicable predictive tool that can be integrated into current clinical practice for better patient outcomes.
- Conditions
- Gastric (Stomach) Cancer
- Being tested
- BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia PredictionDiagnostic test
This intervention utilizes a deep learning model that integrates BUB1 gene expression, radiopathomics (quantitative imaging features), and histopathological data to predict peritoneal metastasis and cachexia in gastric cancer (GC) patients. Unlike traditional approaches, this model combines genomic, imaging, and pathological data to enhance early detection and improve prognostic accuracy. The model aims to identify key patterns in multi-modal data to offer personalized predictions for GC progression. By leveraging artificial intelligence, it seeks to support clinicians in decision-making, improving patient outcomes through earlier interventions and tailored treatments. This approach offers a novel, comprehensive method for predicting GC metastasis and cachexia, providing a unique tool compared to existing interventions.
What is being measured
The primary outcome is the main question the study is designed to answer.
Predictive Accuracy of the BUB1-Integrated Deep Learning Model for Gastric Cancer Peritoneal Metastasis and Cachexia
Measured: 12 months for model training, validation, and initial clinical application
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Mar 2025
- Study start1 Mar 2025 (estimated)
- Primary completion1 Mar 2027 (estimated)
- Study completion1 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric (Stomach) 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
- 500 participants (estimated)
- Target duration
- 5 Years
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
- NCT06858644
- Sponsor's protocol ID
- BUB1
- Acronym
- BUDDLE
- Status last verified
- Feb 2025
- First submitted
- 27 Feb 2025
- Last updated
- 5 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning
- ClinicalTrials.gov ID
- NCT06858644
- Acronym / sponsor ID
- BUDDLE / BUB1
- Conditions
- Gastric (Stomach) Cancer
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Mar 2025 (estimated)
- Primary completion
- 1 Mar 2027 (estimated)
- Study completion
- 1 Mar 2027 (estimated)
- First posted
- 5 Mar 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 trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. The study will focus on utilizing imaging genomics (radiomics) and histopathological data to identify early biomarkers for peritoneal metastasis and cachexia in GC patients. By leveraging deep learning algorithms, the project seeks to improve the accuracy and reliability of predictions, enabling earlier intervention and personalized treatment strategies. The ultimate goal is to enhance clinical decision-making and prognosis prediction in GC patients with peritoneal metastasis and cachexia.
Detailed description
Gastric cancer (GC) is one of the most common and aggressive malignancies, with peritoneal metastasis and cachexia significantly contributing to its poor prognosis. The BUB1 gene has been implicated in chromosomal instability and the progression of GC, but its role in peritoneal metastasis and cachexia remains unclear. This clinical trial aims to explore the potential of integrating BUB1 gene expression with imaging and pathological data to develop a predictive model for GC progression.
The study will collect comprehensive data from GC patients, including genomic profiles (BUB1 gene expression), radiological images (CT/MRI scans), and histopathological findings. Advanced radiomics analysis will extract quantitative features from imaging data, while pathological data will be analyzed for relevant histological markers. The combined dataset will be fed into a deep learning model to identify patterns associated with peritoneal metastasis and cachexia, focusing on the identification of early biomarkers.
The deep learning model will undergo iterative training and validation using both retrospective and prospective patient data. The primary endpoint of the trial is to assess the model's predictive accuracy for peritoneal metastasis and cachexia development, while secondary endpoints include its potential to inform personalized treatment strategies, improve survival rates, and guide clinical decision-making.
This study will also investigate the correlation between BUB1 expression and the radiopathomics features in GC, providing insights into the underlying mechanisms driving peritoneal metastasis and cachexia. The findings aim to establish a robust, clinically applicable predictive tool that can be integrated into current clinical practice for better patient outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 5 Years
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of adult patients diagnosed with gastric cancer (GC) at various stages of the disease. Patients will be selected based on the presence of or risk factors for peritoneal metastasis and/or cachexia, which will be assessed through clinical evaluations, imaging (CT/MRI), and histopathological examination. Participants will be recruited from a cohort of GC patients who have available genomic, radiological, and pathological data, which are essential for training the predictive model. The study will focus on patients with a broad spectrum of GC manifestations, including both early and advanced stages, to ensure the model is applicable across different disease profiles. This diverse population will help evaluate the robustness and generalizability of the model in predicting peritoneal metastasis and cachexia, aiming for a comprehensive representation of GC progression.
Key inclusion criteria (3 listed)
- Adults aged 18-75 years diagnosed with gastric cancer (GC) at any stage. Histopathologically confirmed GC with available radiological (CT/MRI) and pathological data (biopsy samples).
- Patients with or at risk of peritoneal metastasis and/or cachexia, as determined by clinical assessment and imaging.
- Ability to provide informed consent and comply with study protocols. Willingness to undergo regular follow-up imaging and clinical evaluation for the duration of the study.
Key exclusion criteria (3 listed)
- Patients with other primary cancers or serious comorbidities (e.g., severe cardiovascular disease, uncontrolled diabetes).
- Pregnant or breastfeeding women. Patients with contraindications to MRI or CT imaging. Those with insufficient clinical data (e.g., missing radiopathological information) for model training.
- Patients who are unable or unwilling to comply with the study protocol, including follow-up visits and evaluations.
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 |
|---|---|---|
| BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia PredictionThis intervention utilizes a deep learning model that integrates BUB1 gene expression, radiopathomics (quantitative imaging features), and histopathological data to predict peritoneal metastasis and cachexia in gastric cancer (GC) patients. Unlike traditional approaches, this model combines genomic, imaging, and pathological data to enhance early detection and improve prognostic accuracy. The model aims to identify key patterns in multi-modal data to offer personalized predictions for GC progression. By leveraging artificial intelligence, it seeks to support clinicians in decision-making, improving patient outcomes through earlier interventions and tailored treatments. This approach offers a novel, comprehensive method for predicting GC metastasis and cachexia, providing a unique tool compared to existing interventions. | Diagnostic test | – |
Primary outcome
Predictive Accuracy of the BUB1-Integrated Deep Learning Model for Gastric Cancer Peritoneal Metastasis and Cachexia
Time frame: 12 months for model training, validation, and initial clinical application
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 5 Mar 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.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric (Stomach) Cancer, run by Qun Zhao.
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 3 inclusion and 3 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 5 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
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?
Predictive Accuracy of the BUB1-Integrated Deep Learning Model for Gastric Cancer Peritoneal Metastasis and Cachexia (time frame: 12 months for model training, validation, and initial clinical application). 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 5 Mar 2025.