An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators.

ABSTRACT:

Background

This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia.

Methods

COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiom

SUBMITTER: Zhang M 

PROVIDER: S-EPMC8353975 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature