Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06637059
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-10-05
Brief Title:
Artificially Intelligent Model for Accurate Detection of HCC
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Construction of an Artificially Intelligent Model for Accurate Detection of HCC by Integrating Clinical Radiological and Peripheral Immunological Features
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 1092 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
Experimental Design A total of 1092 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None