A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection

Abstract

Supplemental Digital Content is available in the text.

Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features

“…However, current radiomics studies on the differentiation of COVID-19 from other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19, and the evaluation of radiomic feature among different classifiers on COVID-19, are scarce. Our study differed from previous studies [ [37] , [38] , [39] ] in several ways. First, we used a variety of machine learning classifiers to prove the effectiveness of radiomics for COVID-19 lesion feature analysis, and the performance of the current mainstream classifiers for COVID-19 classification was determined.…”

A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

“…Fang et al [ 31 ] developed a radiomics model to predict COVID-19 pneumonia. Fu et al [ 32 ] used a machine learning-based tool to develop radiomics signatures and perform prognosis analysis of COVID-19 patients. Ozturk et al [ 33 ] developed a COVID-19 detection model based on X-ray images to diagnosis COVID-19.…”

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19

“…Compared to other radiomics signatures published in the last months [ 41 , 42 ], our signature was trained and tested on a wider dataset, acquired at different time points, to account for the small variability that might be present in scan acquisition at different dates. This is considered a more reliable strategy [ 43 ] as it closely mimics what happens in a real world clinical scenario.…”