- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06760494
Microvascular Invasion Artificial Intelligence Prediction Via Contrast-enhanced Ultrasound With Explainability (MAPUSE)
Prediction of Microvascular Invasion in HCC Using Spatiotemporal Radiomics of Contrast-enhanced Ultrasound: a Deep Learning Model With Transcriptomics Correlation
An artificial intelligence (AI) model to predict MVI of HCC using contrast-enhanced ultrasound was constructed. This model also has biological explainability. The investigators named it as MAPUSE (MVI AI prediction via contrast-enhanced ultrasound with explainability).
The goal of MAPUSE study is to prospectively test the performance of MAPUSE model on MVI prediction and its biological correlation in different geographical areas of China.
Study Overview
Status
Intervention / Treatment
Detailed Description
The presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a critical prognostic indicator, but its preoperative diagnosis remains challenging. Contrast-enhanced ultrasound (CEUS), with its dynamic microvascular imaging capability, holds promise in prediction of MVI.
The investigators constructed an artificial intelligence (AI) model to predict MVI using contrast-enhanced ultrasound. This model also has biological explainability. We named it as MAPUSE (MVI AI prediction via contrast-enhanced ultrasound with explainability).
The goal of MAPUSE study is to prospectively test the performance of MAPUSE model on MVI prediction and its biological correlation in different geographical areas of China.
The performance of MAPUSE is to be tested in two prospective testing cohorts from two centers in southern and northern China. Before surgery, patient CEUS videos will be collected and analysed by MAPUSE model to generate an MVI risk score. According to the postoperative pathological diagnosis of MVI (golden criterion), the result of MAPUSE will be evaluated. Parameters include area under curve (AUC), accuracy (ACC), sensitivity, specificity and F1-score.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Beijing, China, 100853
- Chinese PLA General Hospital
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Guangdong
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Guangzhou, Guangdong, China, 510080
- The First Affiliated Hospital of Sun Yat-Sen University
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Age >18 years old.
- The HCC diagnosis and the presence of MVI were confirmed by surgical pathology.
- Complete and clear CEUS videos obtained within two weeks preoperatively.
Exclusion Criteria:
- Unqualified CEUS images.
- Missing surgical pathological diagnosis.
- Lesions underwent local treatments.
- Non-HCC diagnosis
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
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Chinese PLA General Hospital Cohort
Patients from Chinese PLA General Hospital (northern China) after surgical treatment
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Using the MAPUSE model to predict MVI status before surgical resection for HCC patients
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the First Affiliated Hospital of Sun Yat-sen University Cohort
Patients from the First Affiliated Hospital of Sun Yat-sen University (southern China) after surgical treatment
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Using the MAPUSE model to predict MVI status before surgical resection for HCC patients
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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area under operating characteristic curves (AUC)
Time Frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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the area under operating characteristic curves (AUC) to evaluate the performance of MAPUSE model in predicting MVI in HCC patients
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From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
ACC (accuracy)
Time Frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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The ratio of the number of samples correctly predicted by the model to the total number of samples
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From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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Specificity
Time Frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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Proportion of all patients without MVI who are predicted negative by MAPUSE
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From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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Sensitivity
Time Frame: From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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The proportion of patients with MVI that MAPUSE correctly identifies
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From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)
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Collaborators and Investigators
Sponsor
Collaborators
Investigators
- Principal Investigator: Chuan Pang, Chinese PLA General Hospital
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- MAPUSE
- 82030047 (Other Identifier: The National Natural Science Foundation of China)
- 92159305 (Other Identifier: The National Natural Science Foundation of China)
- 82325027 (Other Identifier: The National Natural Science Foundation of China)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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