European Radiology

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space...

European Radiology

1 year ago

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

In this study, the authors aimed to evaluate whether MRI-based radiomic features were able to improve the accuracy of survival predictions for lower grade...

European Radiology

1 year ago

Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials

In this observational cohort study, the authors aimed to determine the potential impact of machine learning (ML) CT-derived fractional flow reserve (CT-FFR)...

European Radiology

1 year ago

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly...

European Radiology

1 year ago

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion

The purpose of this study was to develop a deep learning-based method for the automated classification of renal cell carcinoma (RCC) from benign solid renal...

European Radiology

1 year ago

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

In this study, the authors retrospectively collected 2,088 abnormal and 352 normal chest radiographs from two institutions in order to investigate the optimal...

European Radiology

1 year ago

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

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...

European Radiology

1 year ago

Deep learning: definition and perspectives for thoracic imaging

The authors of this review aimed to provide definitions for understanding the methods of machine learning, deep learning, and convolutional neural networks...

European Radiology

1 year ago

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images

In this study, the authors aimed to develop a deep learning-based artificial intelligence (AI) scheme in order to predict the likelihood of the ground-glass...

European Radiology

1 year ago

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT

The authors of this study aimed to determine the diagnostic performance of a deep learning algorithm for the automated detection of small 18F-FDG-avid...

European Radiology

1 year ago