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...
The aim of this study was to develop a supervised machine learning (ML) algorithm that would use diffusion-weighted imaging-derived radiomic features to...
This study integrated the clinical data and radiomics signature generated by a support vector machine to establish a radiomics nomogram for prediction of...
The goal of this study was to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas in patients who underwent...
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.
