Hepatocellular Carcinoma Risk Stratification for Cirrhosis Patients: Integrating Radiomics and Deep Learning Computed Tomography Signatures of the Liver and Spleen into a Clinical Model (IMAGE)

Xia & He Publishing Inc.

Hepatocellular Carcinoma Risk Stratification for Cirrhosis Patients: Integrating Radiomics and Deep Learning Computed Tomography Signatures of the Liver and Spleen into a Clinical Model

Caption

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

Credit

Jin-Lin Hou, Hong-Yang Wang, Rong Fan

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Credit must be given to the creator. Only noncommercial uses of the work are permitted.

License

CC BY-NC

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