Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning.

ABSTRACT:

Objective

To establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology.

Methods

This retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor signature. Next, LASSO and Random Forest methods were used to optimize and model. Finally, the clinical information was combined to accurately predict the survival outcomes of GBC patients.

Results

Fifteen CT features were selected through LASSO and random forest. On the basis of relative importance GLZLM-HGZE, GLCM-homogeneity and NGLDM-coarsenes

SUBMITTER: Liu Z 

PROVIDER: S-EPMC7729190 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature