machine learning

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

2 years ago

AI abstracts from ECR 2019: analysis of topics and compliance with the STARD for abstracts checklist

New machine learning techniques, especially deep neural networks, hold the promise of revolutionizing many aspects of radiology and have gained immense public...

Daniel Pinto dos Santos

2 years 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

2 years ago

Can training data help radiologists to open deep learning black box?

Deep learning has recently pervaded the radiology field, reaching promising results that have encouraged both scientists and entrepreneurs to apply these...

Marina Codari

2 years ago

Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

Decisions regarding the optimal management of unruptured intracranial aneurysms (UIAs) depend on a comprehensive comparison of the risks between aneurysm...

Guo Zhong Chen

2 years 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

3 years ago

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase...

European Radiology Experimental

3 years ago

Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma

This study identified 236 patients from two cohorts who underwent surgery for ground-glass nodules (GGNs). The novel marginal features described, when...

European Radiology

3 years ago

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

This preliminary study aimed to differentiate malignant from benign enhancing foci on breast MRI using radiomic signature. Forty-five patients were included...

European Radiology Experimental

3 years ago

Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs

In this study, the authors developed a deep feature fusion model (DFFM) in order to segment postoperative gliomas on CT images, which were guided by...

European Radiology

3 years ago