Construction of an Artificially Intelligent Model for Accurate Detection of HCC by Integrating Clinical, Radiological, and Peripheral Immunological Features

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.