mri

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

In this study, the authors extended the ComBat approach to provide a harmonization procedure that is applicable to any radiomic feature. They achieve this by...

European Radiology

5 months ago

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

We use a previously validated artificial neural network to evaluate its performance in a much larger, subsequent, consecutive cohort. In the community, there...

David Bonekamp

6 months ago

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

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...

European Radiology

7 months ago

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with...

European Radiology

7 months ago

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be...

European Radiology

8 months ago

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Advanced hepatocellular carcinoma (HCC) carries a dismal prognosis. For a decade, sorafenib, a multi-kinase inhibitor, was the only approved systemic therapy...

Stefanie Hectors

10 months ago

Radiomics based on brain MRI is expected to be a useful tool for early identification and prediction of WMH progression

As a research hotspot in recent years, radiomics provides a new perspective for image diagnosis or evaluation by mining a large number of non-traditional...

Yuyun Xu

10 months 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

12 months ago

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging

Endometrial cancer (EC) has the highest rate of malignancy in women in the entire world, including China, which has the largest population. Accurately staging...

He Zhang

1 year ago

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

Multiple automated methods for segmentation of multiple sclerosis (MS) lesions have been developed over the past years, and the use of artificial neural...

Philipp Kickingereder

1 year ago