Deep Neural Network and Radiomics-based Magnetic Resonance Imaging System for Predicting Microvascular Invasion in Hepatocellular Carcinoma.

ABSTRACT: Background: Accurate preoperative evaluation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for surgeons to make informed decisions regarding appropriate treatment strategies. However, it continues to pose a significant challenge for radiologists. The integration of computer-aided diagnosis utilizing deep learning technology emerges as a promising approach to enhance the prediction accuracy. Methods: This experiment incorporated magnetic resonance imaging (MRI) scans with six different sequences. After a cross-sequence registration preprocess, a deep neural network was employed for the segmentation of hepatocellular carcinoma. The final prediction model was constructed by combining radiomics features with clinical features. The selection of clini

SUBMITTER: Lin ZY 

PROVIDER: S-EPMC11540505 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature