The idea of quantification of disease severity of chronic obstructive pulmonary disease (COPD) with CT has been introduced as early as the late 1980s with the...
Radiomics as a research topic in radiology is certainly a promising field. Over the last years, many publications have shown promising results, showing that...
The purpose of this study, performed between January 2014 and May 2019 across five different centers, was to construct an MRI radiomics model and help...
This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with...
The aim of this retrospective study was to establish and validate a radiomics nomogram that was based on contrast-enhanced spectral mammography (CESM) for the...
The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space...
In oncology, we are in the era of personalized medicine that enables increasingly precise, often molecular-based approaches (genomics, transcriptomics,...
The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately...
In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis...
In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of...
Welcome to the blog on Artificial Intelligence of the European Society of Radiology
This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.
