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Back to main AI Blog

machine learning

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.

Most used hashtags:

machine learningdeep learningradiomicsmrix-ray computed tomographyai newsCOVID-19mammographyinterviewbreast cancerneural networksdiagnostic imagingtomographyMDCTworkflowradiography

Latest posts

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

European Radiology Experimental May 2020

The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). The authors found that the performance of convolutional neural networks (CNN) is comparable to that of experienced radiologists in assessing Child-Pugh class based on multiphase abdominal CT. Key points Established machine learning algorithms can predict

Read More →

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

European Radiology March 2020

This study identified 236 patients from two cohorts who underwent surgery for ground-glass nodules (GGNs). The novel marginal features described, when combined with a radiomics model, could help to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) on preoperative CT scans. Key points Our novel marginal features could improve the existing radiomics model to

Read More →

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

Read More →

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

Read More →

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

Burak Kocak February 2020

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 lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of

Read More →

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

European Radiology January 2020

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure. Key points Twenty-three candidate descriptors were manually extracted from

Read More →

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

Read More →

A pilot study on the performance of machine learning appled to texture-analysis-derived features for breast lesion characterisation at ABUS

European Radiology Experimental December 2019

In this study, the authors aimed to determine whether features derived from texture analysis (TA) are able to distinguish between normal, benign, and malignant tissue on automated breast ultrasound (ABUS). This pilot study included 54 women who underwent ABUS with benign and malignant solid breast lesions. The authors concluded that TA, in combination with machine learning (ML), may very well

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

European Radiology December 2019

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

European Radiology November 2019

In this study, the authors aimed to assess the potential of machine learning (ML) based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer lesions using transrectal ultrasound. The authors were able to demonstrate the technical feasibility of multiparametric ML to improve upon single US modalities for the localization of prostate cancer.

Read More →

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

European Radiology Experimental May 2020

The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). The authors found that the performance of convolutional neural networks (CNN) is comparable to that of experienced radiologists in assessing Child-Pugh class based on multiphase abdominal CT. Key points Established machine learning algorithms can predict

Read More →

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

European Radiology March 2020

This study identified 236 patients from two cohorts who underwent surgery for ground-glass nodules (GGNs). The novel marginal features described, when combined with a radiomics model, could help to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) on preoperative CT scans. Key points Our novel marginal features could improve the existing radiomics model to

Read More →

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

Read More →

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

Read More →

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

Burak Kocak February 2020

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 lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of

Read More →

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

European Radiology January 2020

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure. Key points Twenty-three candidate descriptors were manually extracted from

Read More →

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

Read More →

A pilot study on the performance of machine learning appled to texture-analysis-derived features for breast lesion characterisation at ABUS

European Radiology Experimental December 2019

In this study, the authors aimed to determine whether features derived from texture analysis (TA) are able to distinguish between normal, benign, and malignant tissue on automated breast ultrasound (ABUS). This pilot study included 54 women who underwent ABUS with benign and malignant solid breast lesions. The authors concluded that TA, in combination with machine learning (ML), may very well

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

European Radiology December 2019

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

European Radiology November 2019

In this study, the authors aimed to assess the potential of machine learning (ML) based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer lesions using transrectal ultrasound. The authors were able to demonstrate the technical feasibility of multiparametric ML to improve upon single US modalities for the localization of prostate cancer.

Read More →

Become A Member Today!

You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.

①

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.
②

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.
③

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For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.
Apply now

The membership type best fitting for you will be selected automatically during the application process.

Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.