Non-invasive Liver Fibrosis Screening on CT Images using Radiomics

arXiv:2211.14396v2 Announce Type: replace-cross
Abstract: Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver.
Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT images of patients who underwent simultaneous liver biopsy and CT examinations. Combinations of contrast, normalization, machine learning model, and feature selection method were determined based on their mean test Area Under the Receiver Operating Characteristic curve (AUC) on randomly …

abstract arxiv centre cs.cv cs.lg features images machine machine learning machine learning model patients q-bio.qm radiomics retrospective screening study type