Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.

ABSTRACT:

Purpose

Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively.

Methods

In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models.

Results

Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%)

SUBMITTER: Jiang YQ 

PROVIDER: S-EPMC7873117 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature