Clinical trial · Observational
Artificially Intelligent Model for Accurate Detection of HCC
Construction of an Artificially Intelligent Model for Accurate Detection of HCC by Integrating Clinical, Radiological, and Peripheral Immunological Features
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Purpose: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. The investigators aimed to develop a model with multi-modal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection. 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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Hepatocellular Carcinoma (HCC) | Hepatocellular Carcinoma | ONTOLOGY_EXACT | 0.85 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| observational study | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (3)
- label
- Training cohort
- interventionNames
- Other: observational study
- label
- Internal validation cohort
- interventionNames
- Other: observational study
- label
- External validation cohort
- interventionNames
- Other: observational study
Primary outcomes (1)
- measure
- Diagnosis of liver disease through CT imaging
- timeFrame
- 1 month
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Benign liver diseases, including but not limited to, hemangiomas, hepatic cysts, focal nodular hyperplasia, and cirrhosis Exclusion Criteria: * Participants who had undergone previous treatment for HCC or benign liver diseases, * had taken medications affecting the hematological system within 2 weeks * those who had received a blood transfusion within 6 months
References
Publications (0)
Data not yet available