Artificially Intelligent Model for Accurate Detection of HCC
NCT ID: NCT06637059
Last Updated: 2024-10-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
1092 participants
OBSERVATIONAL
2024-01-01
2024-10-01
Brief Summary
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Experimental Design: A total of 1,092 participants were enrolled from 16 centers. These participants were allocated into the training, internal validation, and external validation cohorts. Peripheral blood specimens were collected prospectively and subjected to mass cytometry analysis. Clinical and radiological data were obtained from electrical medical records. Various AI methods were employed to identify pertinent features and construct single-modal models with optimal performance. The XGBoost algorithm was utilized to amalgamate these models, integrating multi-modal information and facilitating the development of a fusion model. Model evaluation and interpretability were demonstrated using the SHapley Additive exPlanations method.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Training cohort
observational study
observation alone
Internal validation cohort
observational study
observation alone
External validation cohort
observational study
observation alone
Interventions
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observational study
observation alone
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* had taken medications affecting the hematological system within 2 weeks
* those who had received a blood transfusion within 6 months
ALL
No
Sponsors
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Zhejiang University
OTHER
Responsible Party
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TingBo Liang
Professor
Locations
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Hangzhou, Zhejiang, China
Countries
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Other Identifiers
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MMFAIHCC
Identifier Type: -
Identifier Source: org_study_id
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