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

Philipp Kickingereder March 2020

Multiple automated methods for segmentation of multiple sclerosis (MS) lesions have been developed over the past years, and the use of artificial neural networks (ANN) has recently generated many outstanding results in the public segmentation challenges. As we all know from our work as radiologists, the routine clinical practice is always conducted with an economical balance between optimal scan times

Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas?

European Radiology March 2020

In this study, the authors aimed to improve the prediction of patients’ prognosis of myxoid/round cell liposarcomas (MRC-LPS) using a radiomics approach. 35 patients with MRC-LPS were included in this retrospective study. They found that the best prediction of metastatic relapse-free survival for MRC-LPS was achieved by combining the radiomics score to relevant radiological features. Key points Fourteen radiomics features

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

European Radiology Experimental February 2020

This preliminary study aimed to differentiate malignant from benign enhancing foci on breast MRI using radiomic signature. Forty-five patients were included in the study, with 12 malignant lesions and 33 benign lesions. The study showed how feasible a radiomic approach was in the characterization of enhancing foci on breast MRI. Key points Radiomic signature could distinguish malignant from benign enhancing

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

European Radiology February 2020

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 multi-sequence MRIs. The authors found that DFFM enabled accurate segmentation of CT postoperative gliomas, which may help to improve radiotherapy planning. Key points A fully automated deep learning method was developed to segment postoperative gliomas

Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

European Radiology December 2019

The purpose of this retrospective study was to investigate the value of radiomics features from diffusion-weighted imaging (DWI) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). The study included 218 bladder cancer patients who underwent DWI prior to biopsy between July 2014 and December 2018. The authors discovered that combining DWI radiomics features with transurethral resection (TUR)

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

ESR December 2019

The purpose of this study was to create a radiomics approach based on multiparametric MRI (mpMRI) features that were extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant peripheral zone prostate cancer (pCA). The study included 206 patients and the authors concluded that the developed radiomics model that extracts mpMRI features with an auto-fixed

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

European Radiology Experimental November 2019

The authors of this study aimed to assess the performance of a convolutional neural network (CNN) algorithm to register cross-sectional liver imaging series and its performance to manual image registration. The study included three hundred fourteen patients who underwent gadoxetate disodium-enhanced magnetic resonance imaging (MRI) and were retrospectively selected. Key points Image registration across series can improve lesion co-localization and

MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma

European Radiology October 2019

This study integrated the clinical data and radiomics signature generated by a support vector machine to establish a radiomics nomogram for prediction of induction chemotherapy response and survival in nasopharyngeal carcinoma patients. The results proved that multiparametric MRI-based radiomics could be helpful for personalized risk stratification in patients receiving induction chemotherapy. Key points MRI Radiomics can predict IC response and

Why radiomics research does not translate to clinical practice: evaluation of literature using RQS and TRIPOD

Daniel Pinto dos Santos July 2019

Over the last few years, the number of studies published using quantitative imaging biomarkers to classify or predict pathologies has steadily increased. As of today, a quick PubMed search for radiomics, imaging biomarkers or radiogenomics reveals well over 4,000 articles. However, somewhat surprisingly, given this amount of published research, outside of academic literature there is no widespread clinical application of

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging

European Radiology July 2019

In an attempt to determine whether deep learning with the convolutional neural networks (CNN) can be used for identifying parkinsonian disorder on MRI, the authors of this study trained the CNN to distinguish each parkinsonian disorder and then assessed the CNN’s performance. The levels of accuracy achieved confirmed that deep learning with CNN can discriminate parkinsonian disorders with high accuracy.

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

Philipp Kickingereder March 2020

Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

European Radiology December 2019

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

ESR December 2019

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

European Radiology Experimental November 2019

MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma

European Radiology October 2019

Why radiomics research does not translate to clinical practice: evaluation of literature using RQS and TRIPOD

Daniel Pinto dos Santos July 2019

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging

European Radiology July 2019

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

Latest posts

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

Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas?

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

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

Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma

Why radiomics research does not translate to clinical practice: evaluation of literature using RQS and TRIPOD

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging

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

Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas?

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

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

Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma

Why radiomics research does not translate to clinical practice: evaluation of literature using RQS and TRIPOD

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging

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mri

Welcome to the blog on Artificial Intelligence ofthe European Society of Radiology

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Check out our different membership options.

Membership

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ESR Friends

Footnotes:

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology