Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.

Background: Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective.

Purpose: To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI).

Methods: This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.

Objectives: To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI).

Materials And Methods: This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets.