Hepatocellular Carcinoma (HCC)
Conditions
Keywords
Hepatocellular carcinoma; Early detection; Multi-modal; Fusion model; Artificial intelligence
Brief summary
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.
Interventions
OTHERobservational study
observation alone
Sponsors
Zhejiang University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Diagnosis of liver disease through CT imaging | 1 month |
Countries
China
Outcome results
None listed